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Träfflista för sökning "WFRF:(Ng Amos H. C. 1970 ) "

Sökning: WFRF:(Ng Amos H. C. 1970 )

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1.
  • Bernedixen, Jacob (författare)
  • Automated Bottleneck Analysis of Production Systems : Increasing the applicability of simulation-based multi-objective optimization for bottleneck analysis within industry
  • 2018
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Manufacturing companies constantly need to explore new management strategies and new methods to increase the efficiency of their production systems and retain their competitiveness. It is of paramount importance to develop new bottleneck analysis methods that can identify the factors that impede the overall performance of their productionsystems so that the optimal improvement actions can be performed. Many of the bottleneck-related research methods developed in the last two decades are aimed mainly at detecting bottlenecks. Due to their sole reliance on historical data and lackof any predictive capability, they are less useful for evaluating the effect of bottleneck improvements.There is an urgent need for an efficient and accurate method of pinpointing bottlenecks, identifying the correct improvement actions and the order in which these should be carried out, and evaluating their effects on the overall system performance. SCORE (simulation-based constraint removal) is a novel method that uses simulation based multi-objective optimization to analyze bottlenecks. By innovatively formulating bottleneck analysis as a multi-objective optimization problem and using simulation to evaluate the effects of various combinations of improvements, all attainable, maximum throughput levels of the production system can be sought through a single optimization run. Additionally, post-optimality frequency analysis of the Pareto-optimal solutions can generate a rank order of the attributes of the resources required to achieve the target throughput levels. However, in its original compilation, SCORE has a very high computational cost, especially when the simulation model is complex with a large number of decision variables. Some tedious manual setup of the simulation based optimization is also needed, which restricts its applicability within industry, despite its huge potential. Furthermore, the accuracy of SCORE in terms of convergence in optimization theory and correctness of identifying the optimal improvement actions has not been evaluated scientifically.Building on previous SCORE research, the aim of this work is to develop an effective method of automated, accurate bottleneck identification and improvement analysis that can be applied in industry.The contributions of this thesis work include:(1) implementation of a versatile representation in terms of multiple-choice set variables and a corresponding constraint repair strategy into evolutionary multi-objective optimization algorithms;(2) introduction of a novel technique that combines variable screening enabled initializationof population and variable-wise genetic operators to support a more efficient search process;(3) development of an automated setup for SCORE to avoid the tedious manual creation of optimization variables and objectives;(4) the use of ranking distance metrics to quantify and visualize the convergence and accuracy of the bottleneck ranking generated by SCORE.All these contributions have been demonstrated and evaluated through extensive experiments on scalable benchmark simulation models as well as several large-scale simulation models for real-world improvement projects in the automotive industry.The promising results have proved that, when augmented with the techniques proposed in this thesis, the SCORE method can offer real benefits to manufacturing companies by optimizing their production systems.
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2.
  • Karlsson, Ingemar, et al. (författare)
  • Combining augmented reality and simulation-based optimization for decision support in manufacturing
  • 2017
  • Ingår i: Proceedings of the 2017 Winter Simulation Conference. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538634288 - 9781538634295 - 9781538634301 ; , s. 3988-3999
  • Konferensbidrag (refereegranskat)abstract
    • Although the idea of using Augmented Reality and simulation within manufacturing is not a new one, the improvement of hardware enhances the emergence of new areas. For manufacturing organizations, simulation is an important tool used to analyze and understand their manufacturing systems; however, simulation models can be complex. Nonetheless, using Augmented Reality to display the simulation results and analysis can increase the understanding of the model and the modeled system. This paper introduces a decision support system, IDSS-AR, which uses simulation and Augmented Reality to show a simulation model in 3D. The decision support system uses Microsoft HoloLens, which is a head-worn hardware for Augmented Reality. A prototype of IDSS-AR has been evaluated with a simulation model depicting a real manufacturing system on which a bottleneck detection method has been applied. The bottleneck information is shown on the simulation model, increasing the possibility of realizing interactions between the bottlenecks. 
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3.
  • Lidberg, Simon, 1986-, et al. (författare)
  • Evaluating the impact of changes on a global supply chain using an iterative approach in a proof-of-concept model
  • 2018
  • Ingår i: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 467-472
  • Konferensbidrag (refereegranskat)abstract
    • Analyzing networks of supply-chains, where each chain is comprised of several actors with different purposes and performance measures, is a difficult task. There exists a large potential in optimizing supply-chains for many companies and therefore the supply-chain optimization problem is of great interest to study. To be able to optimize the supply-chain on a global scale, fast models are needed to reduce computational time. Previous research has been made into the aggregation of factories, but the technique has not been tested against supply-chain problems. When evaluating the configuration of factories and their inter-transportation on a global scale, new insights can be gained about which parameters are important and how the aggregation fits to a supply-chain problem. The paper presents an interactive proof-of-concept model enabling testing of supply chain concepts by users and decision makers.
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4.
  • Lidberg, Simon, 1986-, et al. (författare)
  • Optimizing real-world factory flows using aggregated discrete event simulation modelling : Creating decision-support through simulation-based optimization and knowledge-extraction
  • 2020
  • Ingår i: Flexible Services and Manufacturing Journal. - : Springer. - 1936-6582 .- 1936-6590. ; 32:4, s. 888-912
  • Tidskriftsartikel (refereegranskat)abstract
    • Reacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition. 
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5.
  • Lidberg, Simon, 1986-, et al. (författare)
  • Using Aggregated Discrete Event Simulation Models and Multi-Objective Optimization to Improve Real-World Factories
  • 2018
  • Ingår i: Proceedings of the 2018 Winter Simulation Conference. - : IEEE. - 9781538665725 - 9781538665732 - 9781538665701 - 9781538665718 ; , s. 2015-2024
  • Konferensbidrag (refereegranskat)abstract
    • Improving production line performance and identifying bottlenecks using simulation-based optimization has been shown to be an effective approach. Nevertheless, for larger production systems which are consisted of multiple production lines, using simulation-based optimization can be too computationally expensive, due to the complexity of the models. Previous research has shown promising techniques for aggregating production line data into computationally efficient modules, which enables the simulation of higher-level systems, i.e., factories. This paper shows how a real-world factory flow can be optimized by applying the previously mentioned aggregation techniques in combination with multi-objective optimization using an experimental approach. The particular case studied in this paper reveals potential reductions of storage levels by over 30 %, lead time reductions by 67 %, and batch sizes reduced by more than 50 % while maintaining the delivery precision of the industrial system.
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6.
  • Amouzgar, Kaveh, 1980-, et al. (författare)
  • A framework for simulation-based multi-objective optimization and knowledge discovery of machining process
  • 2018
  • Ingår i: The International Journal of Advanced Manufacturing Technology. - : Springer Science and Business Media LLC. - 0268-3768 .- 1433-3015. ; 98:9-12, s. 2469-2486
  • Tidskriftsartikel (refereegranskat)abstract
    • The current study presents an effective framework for automated multi-objective optimization (MOO) of machining processes by using finite element (FE) simulations. The framework is demonstrated by optimizing a metal cutting process in turning AISI-1045, using an uncoated K10 tungsten carbide tool. The aim of the MOO is to minimize tool-chip interface temperature and tool wear depth, that are extracted from FE simulations, while maximizing the material removal rate. The effect of tool geometry parameters, i.e., clearance angle, rake angle, and cutting edge radius, and process parameters, i.e., cutting speed and feed rate on the objective functions are explored. Strength Pareto Evolutionary Algorithm (SPEA2) is adopted for the study. The framework integrates and connects several modules to completely automate the entire MOO process. The capability of performing the MOO in parallel is also enabled by adopting the framework. Basically, automation and parallel computing, accounts for the practicality of MOO by using FE simulations. The trade-off solutions obtained by MOO are presented. A knowledge discovery study is carried out on the trade-off solutions. The non-dominated solutions are analyzed using a recently proposed data mining technique to gain a deeper understanding of the turning process.
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7.
  • Amouzgar, Kaveh, 1980-, et al. (författare)
  • Metamodel based multi-objective optimization of a turning process by using finite element simulation
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.
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8.
  • Amouzgar, Kaveh, 1980-, et al. (författare)
  • Metamodel based multi-objective optimization of a turning process by using finite element simulation
  • 2020
  • Ingår i: Engineering optimization (Print). - : Taylor & Francis Group. - 0305-215X .- 1029-0273. ; 52:7, s. 1261-1278
  • Tidskriftsartikel (refereegranskat)abstract
    • This study investigates the advantages and potentials of the metamodelbased multi-objective optimization (MOO) of a turning operation through the application of finite element simulations and evolutionary algorithms to a metal cutting process. The objectives are minimizing the interface temperature and tool wear depth obtained from FE simulations using DEFORM2D software, and maximizing the material removal rate. Tool geometry and process parameters are considered as the input variables. Seven metamodelling methods are employed and evaluated, based on accuracy and suitability. Radial basis functions with a priori bias and Kriging are chosen to model tool–chip interface temperature and tool wear depth, respectively. The non-dominated solutions are found using the strength Pareto evolutionary algorithm SPEA2 and compared with the non-dominated front obtained from pure simulation-based MOO. The metamodel-based MOO method is not only advantageous in terms of reducing the computational time by 70%, but is also able to discover 31 new non-dominated solutions over simulation-based MOO.
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9.
  • Amouzgar, Kaveh, 1980- (författare)
  • Metamodel Based Multi-Objective Optimization with Finite-Element Applications
  • 2018
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • As a result of the increase in accessibility of computational resources and the increase of computer power during the last two decades, designers are able to create computer models to simulate the behavior of complex products. To address global competitiveness, companies are forced to optimize the design of their products and production processes. Optimizing the design and production very often need several runs of computationally expensive simulation models. Therefore, integrating metamodels, as an efficient and sufficiently accurate approximate of the simulation model, with optimization algorithms is necessary. Furthermore, in most of engineering problems, more than one objective function has to be optimized, leading to multi-objective optimization(MOO). However, the urge to employ metamodels in MOO, i.e., metamodel based MOO (MB-MOO), is more substantial.Radial basis functions (RBF) is one of the most popular metamodeling methods. In this thesis, a new approach to constructing RBF with the bias to beset a priori by using the normal equation is proposed. The performance of the suggested approach is compared to the classic RBF and four other well-known metamodeling methods, in terms of accuracy, efficiency and, most importantly, suitability for integration with MOO evolutionary algorithms. It has been found that the proposed approach is accurate in most of the test functions, and it was the fastest compared to other methods. Additionally, the new approach is the most suitable method for MB-MOO, when integrated with evolutionary algorithms. The proposed approach is integrated with the strength Pareto evolutionary algorithm (SPEA2) and applied to two real-world engineering problems: MB-MOO of the disk brake system of a heavy truck, and the metal cutting process in a turning operation. Thereafter, the Pareto-optimal fronts are obtained and the results are presented. The MB-MOO in both case studies has been found to be an efficient and effective method. To validate the results of the latter MB-MOO case study, a framework for automated finite element (FE) simulation based MOO (SB-MOO) of machining processes is developed and presented by applying it to the same metal cutting process in a turning operation. It has been proved that the framework is effective in achieving the MOO of machining processes based on actual FE simulations.
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10.
  • Amouzgar, Kaveh, 1980-, et al. (författare)
  • Multi-objective optimisation of tool indexing problem : a mathematical model and a modified genetic algorithm
  • 2021
  • Ingår i: International Journal of Production Research. - : Taylor & Francis Group. - 0020-7543 .- 1366-588X. ; 59:12, s. 3572-3590
  • Tidskriftsartikel (refereegranskat)abstract
    • Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices. 
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11.
  • Amouzgar, Kaveh, 1980-, et al. (författare)
  • Optimizing index positions on CNC tool magazines considering cutting tool life and duplicates
  • 2020
  • Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; 93, s. 1508-1513
  • Tidskriftsartikel (refereegranskat)abstract
    • Minimizing the non-machining time of CNC machines requires optimal positioning of cutting tools on indexes (stations) of CNC machine turret magazine. This work presents a genetic algorithm with a novel solution representation and genetic operators to find the best possible index positions while tool duplicates and tools life are taken in to account during the process. The tool allocation in a machining process of a crankshaft with 10 cutting operations, on a 45-index magazine, is optimized for the entire life of the tools on the magazine. The tool-indexing time is considerably reduced compared to the current index positions being used in an automotive factory. 
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12.
  • Amouzgar, Kaveh, 1980-, et al. (författare)
  • Radial basis functions with a priori bias as surrogate models : A comparative study
  • 2018
  • Ingår i: Engineering applications of artificial intelligence. - : Elsevier. - 0952-1976 .- 1873-6769. ; 71, s. 28-44
  • Tidskriftsartikel (refereegranskat)abstract
    • Radial basis functions are augmented with a posteriori bias in order to perform robustly when used as metamodels. Recently, it has been proposed that the bias can simply be set a priori by using the normal equation, i.e., the bias becomes the corresponding regression model. In this study, we demonstrate the performance of the suggested approach (RBFpri) with four other well-known metamodeling methods; Kriging, support vector regression, neural network and multivariate adaptive regression. The performance of the five methods is investigated by a comparative study, using 19 mathematical test functions, with five different degrees of dimensionality and sampling size for each function. The performance is evaluated by root mean squared error representing the accuracy, rank error representing the suitability of metamodels when coupled with evolutionary optimization algorithms, training time representing the efficiency and variation of root mean squared error representing the robustness. Furthermore, a rigorous statistical analysis of performance metrics is performed. The results show that the proposed radial basis function with a priori bias achieved the best performance in most of the experiments in terms of all three metrics. When considering the statistical analysis results, the proposed approach again behaved the best, while Kriging was relatively as accurate and support vector regression was almost as fast as RBFpri. The proposed RBF is proven to be the most suitable method in predicting the ranking among pairs of solutions utilized in evolutionary algorithms. Finally, the comparison study is carried out on a real-world engineering optimization problem.
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13.
  • Andersson, Martin, 1981-, et al. (författare)
  • A Parallel Computing Software Architecture for the Bilevel Parameter Tuning of Optimization Algorithms
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Most optimization algorithms extract important algorithmic design decisions as control parameters. This is necessary because different problems can require different search strategies to be solved effectively. The control parameters allow for the optimization algorithm to be adapted to the problem at hand. It is however difficult to predict what the optimal control parameters are for any given problem. Finding these optimal control parameter values is referred to as the parameter tuning problem. One approach of solving the parameter tuning problem is to use a bilevel optimization where the parameter tuning problem itself is formulated as an optimization problem involving algorithmic performance as the objective(s). In this paper, we present a framework and architecture that can be used to solve large-scale parameter tuning problems using a bilevel optimization approach. The proposed framework is used to show that evolutionary algorithms are competitive as tuners against irace which is a state-of-the-art tuning method. Two evolutionary algorithms, differential evaluation (DE) and a genetic algorithm (GA) are evaluated as tuner algorithms using the proposed framework and software architecture. The importance of replicating optimizations and avoiding local optima is also investigated. The architecture is deployed and tested by running millions of optimizations using a computing cluster. The results indicate that the evolutionary algorithms can consistently find better control parameter values than irace. The GA, however, needs to be configured for an explicit exploration and exploitation strategy in order avoid local optima.
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14.
  • Andersson, Martin, 1981-, et al. (författare)
  • On the Trade-off Between Runtime and Evaluation Efficiency In Evolutionary Algorithms
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Evolutionary optimization algorithms typically use one or more parameters that control their behavior. These parameters, which are often kept constant, can be tuned to improve the performance of the algorithm on specific problems.  However, past studies have indicated that the performance can be further improved by adapting the parameters during runtime. A limitation of these studies is that they only control, at most, a few parameters, thereby missing potentially beneficial interactions between them. Instead of finding a direct control mechanism, the novel approach in this paper is to use different parameter sets in different stages of an optimization. These multiple parameter sets, which remain static within each stage, are tuned through extensive bi-level optimization experiments that approximate the optimal adaptation of the parameters. The algorithmic performance obtained with tuned multiple parameter sets is compared against that obtained with a single parameter set.  For the experiments in this paper, the parameters of NSGAII are tuned when applied to the ZDT, DTLZ and WFG test problems. The results show that using multiple parameter sets can significantly increase the performance over a single parameter set.
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15.
  • Andersson, Martin, 1981-, et al. (författare)
  • Parameter Tuning Evolutionary Algorithms for Runtime versus Cost Trade-off in a Cloud Computing Environment
  • 2018
  • Ingår i: Simulation Modelling Practice and Theory. - : Elsevier. - 1569-190X. ; 89, s. 195-205
  • Tidskriftsartikel (refereegranskat)abstract
    • The runtime of an evolutionary algorithm can be reduced by increasing the number of parallel evaluations. However, increasing the number of parallel evaluations can also result in wasted computational effort since there is a greater probability of creating solutions that do not contribute to convergence towards the global optimum. A trade-off, therefore, arises between the runtime and computational effort for different levels of parallelization of an evolutionary algorithm.  When the computational effort is translated into cost, the trade-off can be restated as runtime versus cost. This trade-off is particularly relevant for cloud computing environments where the computing resources can be exactly matched to the level of parallelization of the algorithm, and the cost is proportional to the runtime and how many instances that are used. This paper empirically investigates this trade-off for two different evolutionary algorithms, NSGA-II and differential evolution (DE) when applied to multi-objective discrete-event simulation-based (DES) problem. Both generational and steadystate asynchronous versions of both algorithms are included. The approach is to perform parameter tuning on a simplified version of the DES model. A subset of the best configurations from each tuning experiment is then evaluated on a cloud computing platform. The results indicate that, for the included DES problem, the steady-state asynchronous version of each algorithm provides a better runtime versus cost trade-off than the generational versions and that DE outperforms NSGA-II.
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16.
  • Ayani, Mikel, et al. (författare)
  • Digital Twin : Applying emulation for machine reconditioning
  • 2018
  • Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271 .- 2212-8271. ; 72, s. 243-248
  • Tidskriftsartikel (refereegranskat)abstract
    • Old machine reconditioning projects extend the life length of machines with reduced investments, however they frequently involve complex challenges. Due to the lack of technical documentation and the fact that the machines are running in production, they can require a reverse engineering phase and extremely short commissioning times. Recently, emulation software has become a key tool to create Digital Twins and carry out virtual commissioning of new manufacturing systems, reducing the commissioning time and increasing its final quality. This paper presents an industrial application study in which an emulation model is used to support a reconditioning project and where the benefits gained in the working process are highlighted.
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17.
  • Ayani, Mikel, et al. (författare)
  • Optimizing Cycle Time and Energy Efficiency of a Robotic Cell Using an Emulation Model
  • 2018
  • Ingår i: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 411-416
  • Konferensbidrag (refereegranskat)abstract
    • Industrial automated systems are mostly designed and pre-adjusted to always work at their maximum production rate. This leaves room for important energy consumption reductions considering the production rate variations of factories in reality. This article presents a multi-objective optimization application targeting cycle time and energy consumption of a robotic cell. A novel approach is presented where an existing emulation model of a fictitious robotic cell was extended with low-level electrical components modeled and encapsulated as FMUs. The model, commanded by PLC and Robot Control software, was subjected to a multi-objective optimization algorithm in order to find the Pareto front between energy consumption and production rate. The result of the optimization process allows selecting the most efficient energy consumption for the robotic cell in order to achieve the required cycle.
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18.
  • Bandaru, Sunith, 1984-, et al. (författare)
  • An empirical comparison of metamodeling strategies in noisy environments
  • 2018
  • Ingår i: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018). - New York, NY, USA : ACM Digital Library. - 9781450356183 ; , s. 817-824
  • Konferensbidrag (refereegranskat)abstract
    • Metamodeling plays an important role in simulation-based optimization by providing computationally inexpensive approximations for the objective and constraint functions. Additionally metamodeling can also serve to filter noise, which is inherent in many simulation problems causing optimization algorithms to be mislead. In this paper, we conduct a thorough statistical comparison of four popular metamodeling methods with respect to their approximation accuracy at various levels of noise. We use six scalable benchmark problems from the optimization literature as our test suite. The problems have been chosen to represent different types of fitness landscapes, namely, bowl-shaped, valley-shaped, steep ridges and multi-modal, all of which can significantly influence the impact of noise. Each metamodeling technique is used in combination with four different noise handling techniques that are commonly employed by practitioners in the field of simulation-based optimization. The goal is to identify the metamodeling strategy, i.e. a combination of metamodeling and noise handling, that performs significantly better than others on the fitness landscapes under consideration. We also demonstrate how these results carry over to a simulation-based optimization problem concerning a scalable discrete event model of a simple but realistic production line.
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19.
  • Bandaru, Sunith, 1984-, et al. (författare)
  • Trend Mining : A Visualization Technique to Discover Variable Trends in the Objective Space
  • 2019
  • Ingår i: Evolutionary Multi-Criterion Optimization. - Cham, Switzerland : Springer. - 9783030125974 - 9783030125981 ; , s. 605-617
  • Konferensbidrag (refereegranskat)abstract
    • Practical multi-objective optimization problems often involve several decision variables that influence the objective space in different ways. All variables may not be equally important in determining the trade-offs of the problem. Decision makers, who are usually only concerned with the objective space, have a hard time identifying such important variables and understanding how the variables impact their decisions and vice versa. Several graphical methods exist in the MCDM literature that can aid decision makers in visualizing and navigating high-dimensional objective spaces. However, visualization methods that can specifically reveal the relationship between decision and objective space have not been developed so far. We address this issue through a novel visualization technique called trend mining that enables a decision maker to quickly comprehend the effect of variables on the structure of the objective space and easily discover interesting variable trends. The method uses moving averages with different windows to calculate an interestingness score for each variable along predefined reference directions. These scores are presented to the user in the form of an interactive heatmap. We demonstrate the working of the method and its usefulness through a benchmark and two engineering problems.
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20.
  • Barrera Diaz, Carlos Alberto, 1987-, et al. (författare)
  • An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems
  • 2023
  • Ingår i: Mathematics. - : MDPI. - 2227-7390. ; 11:6
  • Tidskriftsartikel (refereegranskat)abstract
    • In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.
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21.
  • Barrera Diaz, Carlos Alberto, 1987-, et al. (författare)
  • Enabling Knowledge Discovery from Simulation-Based Multi-Objective Optimization in Reconfigurable Manufacturing Systems
  • 2022
  • Ingår i: Proceedings of the 2022 Winter Simulation Conference. - : IEEE. - 9781665476614 - 9781665476621 ; , s. 1794-1805
  • Konferensbidrag (refereegranskat)abstract
    • Due to the nature of today's manufacturing industry, where enterprises are subjected to frequent changes and volatile markets, reconfigurable manufacturing systems (RMS) are crucial when addressing ramp-up and ramp-down scenarios derived from, among other challenges, increasingly shortened product lifecycles. Applying simulation-based optimization techniques to their designs under different production volume scenarios has become valuable when RMS becomes more complex. Apart from proposing the optimal solutions subject to various production volume changes, decision-makers can extract propositional knowledge to better understand the RMS design and support their decision-making through a knowledge discovery method by combining simulation-based optimization and data mining techniques. In particular, this study applies a novel flexible pattern mining algorithm to conduct post-optimality analysis on multi-dimensional, multi-objective optimization datasets from an industrial-inspired application to discover the rules regarding how the tasks are assigned to the workstations constitute reasonable solutions for scalable RMS. 
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22.
  • Barrera Diaz, Carlos Alberto, 1987-, et al. (författare)
  • Optimizing reconfigurable manufacturing systems : A Simulation-based Multi-objective Optimization approach
  • 2021
  • Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; 104, s. 1837-1842
  • Tidskriftsartikel (refereegranskat)abstract
    • Application of reconfigurable manufacturing systems (RMS) plays a significant role in manufacturing companies’ success in the current fiercely competitive market. Despite the RMS’s advantages, designing these systems to achieve a high-efficiency level is a complex and challenging task that requires the use of optimization techniques. This study proposes a simulation-based optimization approach for optimal allocation of work tasks and resources (i.e., machines) to workstations. Three conflictive objectives, namely maximizing the throughput, minimizing the buffers’ capacity, and minimizing the number of machines, are optimized simultaneously while considering the system’s stochastic behavior to achieve the desired system’s configuration.
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23.
  • Barrera Diaz, Carlos Alberto, 1987-, et al. (författare)
  • Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes : A Simulation-Based Multi-Objective Approach
  • 2021
  • Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 9, s. 144195-144210
  • Tidskriftsartikel (refereegranskat)abstract
    • In today's global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today's manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach.
  •  
24.
  • Barrera Diaz, Carlos Alberto, 1987-, et al. (författare)
  • Simulation-based multi-objective optimization for reconfigurable manufacturing system configurations analysis
  • 2020
  • Ingår i: Proceedings of the 2020 Winter Simulation Conference. - : IEEE. - 9781728194998 - 9781728195001 ; , s. 1527-1538, s. 1527-1538
  • Konferensbidrag (refereegranskat)abstract
    • The purpose of this study is to analyze the use of Simulation-Based Multi-Objective Optimization (SMO) for Reconfigurable Manufacturing System Configuration Analysis (RMS-CA). In doing so, this study addresses the need for efficiently performing RMS-CA with respect to the limited time for decision-making in the industry, and investigates one of the salient problems of RMS-CA: determining the minimum number of machines necessary to satisfy the demand. The study adopts an NSGA II optimization algorithm and presents two contributions to existing literature. Firstly, the study proposes a series of steps for the use of SMO for RMS-CA and shows how to simultaneously maximize production throughput, minimize lead time, and buffer size. Secondly, the study presents a qualitative comparison with the prior work in RMS-CA and the proposed use of SMO; it discusses the advantages and challenges of using SMO and provides critical insight for production engineers and managers responsible for production system configuration.
  •  
25.
  • Bernedixen, Jacob, et al. (författare)
  • Multiple Choice Sets and Manhattan Distance Based Equality Constraint Handling for Production Systems Optimization
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Many simulation-based optimization packages provide powerful algorithms to solve industrialproblems. But most of them fail to oer their users the techniques they needto eectively handle multiple-choice problems involving a large set of decision variableswith mixed types (continuous, discrete and combinatorial) and problems that are highlyconstrained (e.g., with many equality constraints). Yet such issues are found in manyreal-world production system design and improvement problems. Thus, this paper introducesa method to eectively embed multiple choice sets and Manhattan-distancebasedconstraint handling into multi-objective optimization algorithms like NSGA-II andNSGA-III. This paper illustrates and evaluates how these two techniques have been appliedtogether to solve optimal workload, buer and workforce allocation problems. Anexample follows, showing their application to a complex production system improvementproblem at an automotive manufacturer.
  •  
26.
  • Bernedixen, Jacob, et al. (författare)
  • On the convergence of stochastic simulation-based multi-objective optimization for bottleneck identification
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • By innovatively formulating a bottleneck identication problem into a bi-objective optimization,simulation-based multi-objective optimization (SMO) can be eectively used as a new method for gen-eral production systems improvement. In a single optimization run, all attainable, maximum throughputlevels of the system can be sought through various optimal combinations of improvement changes ofthe resources. Additionally, the post-optimality frequency analysis on the Pareto-optimal solutions cangenerate a rank order of the attributes of the resources required to achieve the target throughput levels.Observing that existing research mainly put emphasis on measuring the convergence of the optimizationin the objective space, leaving no information on when the solutions in the decision space have convergedand stabilized, this paper represents the rst eort in increasing the knowledge about the convergence ofSMO for the rank ordering in the context of bottleneck analysis. By customizing the Spearman's footruledistance and Kendall's tau, this paper presents how these metrics can be used eectively to provide thedesired visual aid in determining the convergence of bottleneck ranking, hence can assist the user todetermine correctly the terminating condition of the optimization process. It illustrates and evaluatesthe convergence of the SMO for bottleneck analysis on a set of scalable benchmark models as well as twoindustrial simulation models. The results have shed promising direction of applying these new metrics tocomplex, real-world applications.
  •  
27.
  • Bernedixen, Jacob, et al. (författare)
  • Variables Screening Enabled Multi-Objective Optimization for Bottleneck Analysis of Production Systems
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Bottleneck analysis can be defined as the process that includes both bottleneck identification and improvement. In the literature most of the proposed bottleneck-related methods address mainly bottleneck detection. By innovatively formulating a bottleneck analysis into a bi-objective optimization method, recent research has shown that all attainable, maximized TH of a production system, through various combinations of improvement changes of the resources, can be sought in a single optimization run. Nevertheless, when applied to simulation-based evaluation, such a bi-objective optimization is computationally expensive especially when the simulation model is complex and/or with a large amount of decision variables representing the improvement actions. The aim of this paper is therefore to introduce a novel variables screening enabled bi-objective optimization that is customized for bottleneck analysis of production systems. By using the Sequential Bifurcation screening technique which is particularly suitable for large-scale simulation models, fewer simulation runs are required to find the most influenacing factors in a simulation model. With the knowledge of these input variables, the bi-objective optimization used in the bottleneck analysis can customize the genetic operators on these variables individually according to their rank of main effects with the target to speed up the entire optimization process. The screening-enabled algorithm is then applied to a set of experiments designed to evaluate how well it performs when the number of variables increases is a scalable, benchmark model, as well as two real-world industrial-scale simulation models found in the automotive industry. The results have illustrated the promising direction of incorporating the knowledge of influencing variables and variable-wise genetic operators into a multi-objective optimization algorithm for bottleneck analysis.
  •  
28.
  • Dudas, Catarina, et al. (författare)
  • Integration of data mining and multi-objective optimisation for decision support in production system development
  • 2014
  • Ingår i: International journal of computer integrated manufacturing (Print). - : Taylor & Francis. - 0951-192X .- 1362-3052. ; 27:9, s. 824-839
  • Tidskriftsartikel (refereegranskat)abstract
    • Multi-objective optimisation (MOO) is a powerful approach for generating a set of optimal trade-off (Pareto) design alternatives that the decision-maker can evaluate and then choose the most-suitable configuration, based on some high-level strategic information. Nevertheless, in practice, choosing among a large number of solutions on the Pareto front is often a daunting task, if proper analysis and visualisation techniques are not applied. Recent research advancements have shown the advantages of using data mining techniques to automate the post-optimality analysis of Pareto-optimal solutions for engineering design problems. Nonetheless, it is argued that the existing approaches are inadequate for generating high-quality results, when the set of the Pareto solutions is relatively small and the solutions close to the Pareto front have almost the same attributes as the Pareto-optimal solutions, of which both are commonly found in many real-world system problems. The aim of this paper is therefore to propose a distance-based data mining approach for the solution sets generated from simulation-based optimisation, in order to address these issues. Such an integrated data mining and MOO procedure is illustrated with the results of an industrial cost optimisation case study. Particular emphasis is paid to showing how the proposed procedure can be used to assist decision-makers in analysing and visualising the attributes of the design alternatives in different regions of the objective space, so that informed decisions can be made in production systems development.
  •  
29.
  • Dudas, Catarina, et al. (författare)
  • Post-analysis of multi-objective optimization solutions using decision trees
  • 2015
  • Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 19:2, s. 259-278
  • Tidskriftsartikel (refereegranskat)abstract
    • Evolutionary algorithms are often applied to solve multi-objective optimization problems. Such algorithms effectively generate solutions of wide spread, and have good convergence properties. However, they do not provide any characteristics of the found optimal solutions, something which may be very valuable to decision makers. By performing a post-analysis of the solution set from multi-objective optimization, relationships between the input space and the objective space can be identified. In this study, decision trees are used for this purpose. It is demonstrated that they may effectively capture important characteristics of the solution sets produced by multi-objective optimization methods. It is furthermore shown that the discovered relationships may be used for improving the search for additional solutions. Two multi-objective problems are considered in this paper; a well-studied benchmark function problem with on a beforehand known optimal Pareto front, which is used for verification purposes, and a multi-objective optimization problem of a real-world production system. The results show that useful relationships may be identified by employing decision tree analysis of the solution sets from multi-objective optimizations.
  •  
30.
  • Fathi, Masood, et al. (författare)
  • An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem
  • 2020
  • Ingår i: Engineering computations. - : Emerald Group Publishing Limited. - 0264-4401 .- 1758-7077. ; 37:2, s. 501-521
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose – This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision makers aim to design an efficient assembly line while satisfying a set of constraints.Design/methodology/approach – An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP in order to optimize the number of stations and the workload smoothness.Findings – To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs.Originality/value – The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is employed in the IGA to enhance its local search capability.
  •  
31.
  • Fathi, Masood, et al. (författare)
  • An optimization model for balancing assembly lines with stochastic task times and zoning constraints
  • 2019
  • Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 7, s. 32537-32550
  • Tidskriftsartikel (refereegranskat)abstract
    • This study aims to bridge the gap between theory and practice by addressing a real-world assembly line balancing problem (ALBP) where task times are stochastic and there are zoning constraints in addition to the commonly known ALBP constraints. A mixed integer programming (MIP) model is proposed for each of the straight and U-shaped assembly line configurations. The primary objective in both cases is to minimize the number of stations; minimizing the maximum of stations’ mean time and the stations’ time variance are considered secondary objectives. Four different scenarios are discussed for each model, with differences in the objective function. The models are validated by solving a real case taken from an automobile manufacturing company and some standard test problems available in the literature. The results indicate that both models are able to provide optimum solutions for problems of different sizes. The technique for order preference by similarity to ideal solution (TOPSIS) is used to create reliable comparisons of the different scenarios and valid analysis of the results. Finally, some insights regarding the selection of straight and U-shaped layouts are provided.
  •  
32.
  • Fathi, Masood, et al. (författare)
  • Assembly Line Balancing Type-E with Technological Requirement : A Mathematical Model
  • 2018
  • Ingår i: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 183-188
  • Konferensbidrag (refereegranskat)abstract
    • This study is motivated by a real-world assembly line in an automotive manufacturing company and it addresses the simple assembly line balancing problem type-E (SALBPE). The SALBPE aims to maximize the balance efficiency (BE) through determining the best combinations of cycle time and station number. To cope with the problem, a mixed integer nonlinear programming (MINLP) model is proposed. The MINLP model differs from the existing ALBPE models as it includes the technological requirements of assembly tasks and optimizes the variation of workload beside the BE. The validity of the proposed model is tested by solving the real-world case study and a set of benchmark problems.
  •  
33.
  •  
34.
  • Frantzén, Marcus, et al. (författare)
  • Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming
  • 2022
  • Ingår i: Decision Analytics Journal. - : Elsevier. - 2772-6622. ; 3
  • Tidskriftsartikel (refereegranskat)abstract
    • Modern decision support systems need to be connected online to equipment so that the large amount of data available can be used to guide the decisions of shop floor operators, making full use of the potential of industrial manufacturing systems. This paper investigates a novel optimization and data analytic method to implement such a decision support system, based on heuristic generation using genetic programming and simulation-based optimization running on a digital twin. Such a digital-twin-based decision support system allows the proactively searching of the best attribute combinations to be used in a data-driven composite dispatching rule for the short-term corrective maintenance task prioritization. Both the job (e.g., bottlenecks) and operator priorities use multiple criteria, including competence, utilization, operator walking distances on the shop floor, bottlenecks, work-in-process, and parallel resource availability. The data-driven composite dispatching rules are evaluated using a digital twin, built for a real-world machining line, which simulates the effects of decisions regarding disruptions. Experimental results show improved productivity because of using the composite dispatching rules generated by such heuristic generation method compared to the priority dispatching rules based on similar attributes and methods. The improvement is more pronounced when the number of operators is reduced. This paper thus offers new insights about how shop floor data can be transformed into useful knowledge with a digital-twin-based decision support system to enhance resource efficiency.
  •  
35.
  • Gandhi, Kanika, et al. (författare)
  • Machine maintenance decision support system : A systematic literature review
  • 2018
  • Ingår i: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 349-354
  • Konferensbidrag (refereegranskat)abstract
    • Growing competition market situations have emerged the requirement of the real-time data, understanding data behaviour, and maintenance actions in the manufacturing system. The future decision-making process in manufacturing needs to be more flexible to adapt to various methods for maintenance decision support systems (MDSS). This paper classifies various application areas of MDSS through a systemic literature review. Specifically, it identifies the relationship between the machine maintenance areas and the processes in which it integrates different tools and techniques to develop MDSS. The accumulated information helps in analyzing trends and shortcomings to concentrate the efforts for future research work. The reviewed papers are selected based on the contents, application tool assessments and clustered by their application areas. Furthermore, it proposes a structure outlined based on the functional knowledge as well as the information flow design during the development of MDSS, along with the relationship among application areas.
  •  
36.
  • Gandhi, Kanika, et al. (författare)
  • Towards data mining based decision support in manufacturing maintenance
  • 2018
  • Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271 .- 2212-8271. ; 72, s. 261-265
  • Tidskriftsartikel (refereegranskat)abstract
    • The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.
  •  
37.
  • Goienetxea, Ainhoa, 1983-, et al. (författare)
  • Bringing together Lean and simulation : a comprehensive review
  • 2020
  • Ingår i: International Journal of Production Research. - : Taylor & Francis Group. - 0020-7543 .- 1366-588X. ; 58:1, s. 87-117
  • Forskningsöversikt (refereegranskat)abstract
    • Lean is and will still be one of the most popular management philosophies in the Industry 4.0 context and simulation is one of its key technologies. Many authors discuss about the benefits of combining Lean and simulation to better support decision makers in system design and improvement. However, there is a lack of reviews in the domain. Therefore, this paper presents a four-stage comprehensive review and analysis of existing literature on their combination. The aim is to identify the state of the art, existing methods and frameworks for combining Lean and simulation, while also identifying key research perspectives and challenges. The main trends identified are the increased interest in the combination of Lean and simulation in the Industry 4.0 context and in their combination with optimisation, Six Sigma, as well as sustainability. The number of articles in these areas is likely to continue to grow. On the other hand, we highlight six gaps found in the literature regarding the combination of Lean and simulation, which may induce new research opportunities. Existing technical, organisational, as well as people and culture related challenges on the combination of Lean and simulation are also discussed.
  •  
38.
  • Goienetxea Uriarte, Ainhoa, 1983- (författare)
  • Bringing Together Lean, Simulation and Optimization : Defining a framework to support decision-making in system design and improvement
  • 2019
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • The rapid changes in the market including globalization, the requirement for personalizedproducts and services by the customers, shorter product life-cycles, the exponential growthof technological advances, and the demographical changes, will demand organizations toeffectively improve and design their systems in order to survive. This is the actual paradigmcharacterizing the industrial and service sectors. This scenario presents a considerablechallenge to decision makers who will need to decide about how to design and improve amore than ever complex system without compromising the quality of the decision taken.Lean, being a widely applied management philosophy with very powerful principles, itsmethods and tools are static in nature and have some limitations when it comes to the designand improvement of complex and dynamic systems. Some authors have proposed thecombined use of simulation with Lean in order to overcome these limitations. Furthermore,optimization and post-optimization tools coupled to simulation, provide knowledge aboutoptimal or nearly optimal system configurations to choose from. However, even if Leanprinciples, methods and tools, as well as simulation and optimization, pursue the objectiveof supporting organizations regarding system design and improvement, a bilateral approachfor their combination and its benefits have barely been addressed in the literature.Many studies focus only on how specific Lean tools and simulation can be combined, treatingLean purely as a toolbox and not considering how Lean can support the simulation process.The aim of this research is to address this knowledge gap by analyzing the mutualbenefits and presenting a framework for combining Lean, simulation and optimization tobetter support decision makers in system design and improvement where the limitationsof Lean tools and simulation are overcome by their combination. This framework includesa conceptual framework explaining the relationships between the Lean philosophy, methodsand tools with simulation and optimization; the purposes for this combination and stepby step processes to achieve these purposes; the identification of the roles involved in eachprocess; a maturity model providing guidelines on how to implement the framework; existingbarriers for the implementation; and ethical considerations to take into account. Anindustrial handbook has also been written which explains how to deploy the framework.The research has been conducted in three main stages including an analysis of the literatureand the real-world needs, the definition and formulation of the framework, and finally, itsevaluation in real-world projects and with subject matter experts. The main contributionof this research is the reflection provided on the bilateral benefits of the combination, aswell as the defined and evaluated framework, which will support decision makers take qualitydecisions in system design and improvement even in complex scenarios.
  •  
39.
  • Goienetxea Uriarte, Ainhoa, 1983-, et al. (författare)
  • Introducing simulation and optimization in the Lean continuous improvement standards in an automotive company
  • 2019
  • Ingår i: Proceedings of the Winter Simulation Conference, Gothenburg, December 9-12, 2018. - Piscataway, New Jersey : IEEE. - 9781538665725 - 9781538665701 - 9781538665718 - 9781538665732 ; , s. 3352-3363
  • Konferensbidrag (refereegranskat)abstract
    • The highly competitive automobile market requires automotive companies to become efficient by continuously improving their production systems. This paper presents a case study where simulationbased optimization (SBO) was employed as a step within a Value Stream Mapping event. The aim of the study was to promote the use of SBO to strengthen the continuous improvement work of the company. The paper presents all the key steps performed in the study, including the challenges faced and a reflection on how to introduce SBO as a powerful tool within the lean continuous improvement standards.
  •  
40.
  • Goienetxea Uriarte, Ainhoa, 1983-, et al. (författare)
  • Supporting the lean journey with simulation and optimization in the context of Industry 4.0
  • 2018
  • Ingår i: Procedia Manufacturing. - : Elsevier. - 2351-9789. ; 25, s. 586-593
  • Tidskriftsartikel (refereegranskat)abstract
    • The new industrial revolution brings important changes to organizations that will need to adapt their machines, systems and employees’ competences to sustain their business in a highly competitive market. Management philosophies such as lean will also need to adapt to the improvement possibilities that Industry 4.0 brings. This paper presents a review on the role of lean and simulation in the context of Industry 4.0. Additionally, the paper presents a conceptual framework where simulation and optimization will make the lean approach more efficient, speeding up system improvements and reconfiguration, by means of an enhanced decision-making process and supported organizational learning.
  •  
41.
  • Karlsson, Ingemar, et al. (författare)
  • Online Knowledge Extraction and Preference Guided Multi-Objective Optimization in Manufacturing
  • 2021
  • Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 145382-145396
  • Tidskriftsartikel (refereegranskat)abstract
    • The integration of simulation-based optimization and data mining is an emerging approach to support decision-making in the design and improvement of manufacturing systems. In such an approach, knowledge extracted from the optimal solutions generated by the simulation-based optimization process can provide important information to decision makers, such as the importance of the decision variables and their influence on the design objectives, which cannot easily be obtained by other means. However, can the extracted knowledge be directly used during the optimization process to further enhance the quality of the solutions? This paper proposes such an online knowledge extraction approach that is used together with a preference-guided multi-objective optimization algorithm on simulation models of manufacturing systems. Specifically, it introduces a combination of the multi-objective evolutionary optimization algorithm, NSGA-II, and a customized data mining algorithm, called Flexible Pattern Mining (FPM), which can extract knowledge in the form of rules in an online and automatic manner, in order to guide the optimization to converge towards a decision maker's preferred region in the objective space. Through a set of application problems, this paper demonstrates how the proposed FPM-NSGA-II can be used to support higher quality decision-making in manufacturing.
  •  
42.
  • Kumbhar, Mahesh, et al. (författare)
  • A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks
  • 2023
  • Ingår i: Journal of manufacturing systems. - : Springer. - 0278-6125 .- 1878-6642. ; 66, s. 92-106
  • Tidskriftsartikel (refereegranskat)abstract
    • Digitalization through Industry 4.0 technologies is one of the essential steps for the complete collaboration, communication, and integration of heterogeneous resources in a manufacturing organization towards improving manufacturing performance. One of the ways is to measure the effective utilization of critical resources, also known as bottlenecks. Finding such critical resources in a manufacturing system has been a significant focus of manufacturing research for several decades. However, finding a bottleneck in a complex manufacturing system is difficult due to the interdependencies and interactions of many resources. In this work, a digital twin framework is developed to detect, diagnose, and improve bottleneck resources using utilization-based bottleneck analysis, process mining, and diagnostic analytics. Unlike existing bottleneck detection methods, this novel approach is capable of directly utilizing enterprise data from multiple levels, namely production planning, process execution, and asset monitoring, to generate event-log which can be fed into a digital twin. This enables not only the detection and diagnosis of bottleneck resources, but also validation of various what-if improvement scenarios. The digital twin itself is generated through process mining techniques, which can extract the main process map from a complex system. The results show that the utilization can detect both sole and shifting bottlenecks in a complex manufacturing system. Diagnosing and managing bottleneck resources through the proposed approach yielded a minimum throughput improvement of 10% in a real factory setting. The concept of a custom digital twin for a specific context and goal opens many new possibilities for studying the strong interaction of multi-source data and decision-making in a manufacturing system. This methodology also has the potential to be exploited for multi-objective optimization of bottleneck resources.
  •  
43.
  • Kumbhar, Mahesh, et al. (författare)
  • Bottleneck Detection Through Data Integration, Process Mining and Factory Physics-Based Analytics
  • 2022
  • Ingår i: SPS2022. - Amsterdam; Berlin; Washington, DC : IOS Press. - 9781643682686 - 9781643682693 ; , s. 737-748
  • Konferensbidrag (refereegranskat)abstract
    • Production systems are evolving rapidly, thanks to key Industry 4.0 technologies such as production simulation, digital twins, internet-of-things, artificial intelligence, and big data analytics. The combination of these technologies can be used to meet the long-term enterprise goals of profitability, sustainability, and stability by increasing the throughput and reducing production costs. Owing to digitization, manufacturing companies can now explore operational level data to track the performance of systems making processes more transparent and efficient. This untapped granular data can be leveraged to better understand the system and identify constraining activities or resources that determine the system’s throughput. In this paper, we propose a data-driven methodology that exploits the technique of data integration, process mining, and analytics based on factory physics to identify constrained resources, also known as bottlenecks. To test the proposed methodology, a case study was performed on an industrial scenario were a discrete event simulation model is built and validated to run future what-if analyses and optimization scenarios. The proposed methodology is easy to implement and can be generalized to any other organization that captures event data.
  •  
44.
  • Lidberg, Simon, MSc. 1986-, et al. (författare)
  • A Knowledge Extraction Platform for Reproducible Decision-Support from Multi-Objective Optimization Data
  • 2022
  • Ingår i: SPS2022. - Amsterdam; Berlin; Washington, DC : IOS Press. - 9781643682686 - 9781643682693 ; 21, s. 725-736
  • Konferensbidrag (refereegranskat)abstract
    • Simulation and optimization enables companies to take decision based on data, and allows prescriptive analysis of current and future production scenarios, creating a competitive edge. However, it can be difficult to visualize and extract knowledge from the large amounts of data generated by a many-objective optimization genetic algorithm, especially with conflicting objectives. Existing tools offer capabilities for extracting knowledge in the form of clusters, rules, and connections. Although powerful, most existing software is proprietary and is therefore difficult to obtain, modify, and deploy, as well as for facilitating a reproducible workflow. We propose an open-source web-based application using commonly available packages in the R programming language to extract knowledge from data generated from simulation-based optimization. This application is then verified by replicating the experimental methodology of a peer-reviewed paper on knowledge extraction. Finally, further work is also discussed, focusing on method improvements and reproducible results.
  •  
45.
  • Lidberg, Simon, MSc. 1986-, et al. (författare)
  • A System Architecture for Continuous Manufacturing Decision Support Using Knowledge Generated from Multi-Level Simulation-Based Optimization
  • 2024
  • Ingår i: Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning. - : IOS Press. - 9781643685106 - 9781643685113 ; , s. 231-243
  • Konferensbidrag (refereegranskat)abstract
    • Manufacturing is becoming increasingly complex as product life cycles shorten, and new disruptive technologies are introduced. The increased complexity in the manufacturing footprint also complicates industrial decision-making. Proposed improvements to alleviate bottlenecks do not guarantee effective problem resolution. Instead, improvement efforts can become misguided, targeting a bottleneck that affects a single production line rather than the entire site. An effective method for identifying production issues and predicting system performance is discrete-event simulation. When coupled with multi-objective optimization and multi-level modeling, production performance issues can be identified at both the site and workstation levels. However, optimization studies yield vast amounts of data, which can be challenging to extract useful knowledge from. To address this, we employ data-mining methods to assist decision-makers in extracting valuable insights from optimization data. This study presents an architecture for a decision support system that utilizes simulation-based optimization to continuously aid in industrial decision-making. Through a novel model generation method, simulation models are automatically generated and updated using logged data from the manufacturing shop floor and product lifecycle management systems. To reduce the computational complexity of the optimization, model simplification, varying replication numbers, surrogate modeling, and parallel computing in the cloud are also employed within this architecture. The results are presented to a decision-maker in an intelligent decision-support system, allowing for timely and relevant industrial decisions. 
  •  
46.
  • Lidberg, Simon, MSc. 1986- (författare)
  • Evaluating Fast and Efficient Modeling Methods for Simulation-Based Optimization
  • 2021
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • As the industry in general, and the automotive industry in particular, is transforming -- due to new technologies and changes in market demands through electrification, digitalization, and globalization -- maintaining a competitive edge will require better predictions. Better predictions of production performance allows companies to capitalize on opportunities, avoid costly mistakes, and be proactive about change.A commonly used tool in manufacturing for the prediction of production performance is discrete-event simulation. In combination with artificial intelligence methods such as multi-objective optimization, in literature often referred to as simulation-based optimization, and knowledge extraction, bottlenecks in the production process can be identified and recipes for optimal improvement order can be obtained. These recipes support the decision-maker in both understanding the production system and improving it optimally in terms of resource efficiency and investment cost. Even though the use of simulation-based optimization is widespread on the production line level, use on the factory level is more scarce. Improvements on the production line level, without a holistic view of factory performance, can be suboptimal and may only lead to increased storage levels instead of increased output to the customer.The main obstacle for applying simulation-based optimization to the factory level is the complexity of its constituent parts, i.e., detailed production line models. Connecting several detailed production line models to create a factory model results in an overly complicated, albeit, accurate model. A single factory model running for one minute would equate to almost 140 days required for an optimization project, too long to provide decision-support relevant to manufacturing decision-making. This can be mitigated by parallel computing, but a more effective approach is to simplify the production line models to decrease the runtime while trying to maintain accuracy. Model simplification methods are approaches to reduce model complexity in new and existing simulation models. Previous research has provided an accurate and runtime efficient simplification method by use of a generic model structure built by common modeling components. Although the method seems promising in a few publications, it was lacking external and internal validity.This project presents simulation-based optimization on the factory level enabled by a model simplification method. By following the design science research methodology, several case-studies mainly in the automotive industry identify issues with the current implementation, propose additions to the method, and validates them.
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47.
  • Lidberg, Simon, MSc. 1986-, et al. (författare)
  • Model Simplification Methods for Coded Discrete-Event Simulation Models : A Systematic Review and Experimental Study
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • In an increasingly competitive market due to customer demands of customization and an increasing rate of new product variant introductions, companies need to explore new tools to support them to better predict and optimally re-configure their production networks. In terms of the factory flow level, discrete-event simulation and simulation-based optimization represent such kinds  of tools available at the disposal of production engineers or managers. For a complex factory consisting of multiple production lines, creating detailed simulation models of these lines and connecting them together can be used for optimization, but the computational complexity can be prohibitively large for acquiring results in time. Model simplification methods can be utilized to reduce the computational complexity of a model. In this study, a systematic literature review is conducted with the aim of identifying simplification methods for coded models, characteristics of the detailed model, type of industry, motivation, and validation measures. Based on the results of the literature review an experimental study wherein the limits of a specific simplification method are analyzed. We compare the output of a dynamically created model with the output of a simplified representation. A correlation can be observed between outputs for medium to large lines, but for smaller lines, there is a larger discrepancy. The simplification method allows for the reduction in simulation runtime, enabling simulation-based optimization of large lines or interconnected simplified models forming a production network, i.e., a factory, to be optimized and analyzed more efficiently, leading to competitive advantages for companies.
  •  
48.
  • Lidberg, Simon, MSc. 1986-, et al. (författare)
  • Multi-Level Optimization with Aggregated Discrete-Event Models
  • 2020
  • Ingår i: Proceedings of the 2020 Winter Simulation Conference. - : IEEE. - 9781728194998 - 9781728195001 ; , s. 1515-1526
  • Konferensbidrag (refereegranskat)abstract
    • Removing bottlenecks that restrain the overall performance of a factory can give companies a competitive edge. Although in principle, it is possible to connect multiple detailed discrete-event simulation models to form a complete factory model, it could be too computationally expensive, especially if the connected models are used for simulation-based optimizations. Observing that computational speed of running a simulation model can be significantly reduced by aggregating multiple line-level models into an aggregated factory level, this paper investigates, with some loss of detail, if the identified bottleneck information from an aggregated factory model, in terms of which parameters to improve, would be useful and accurate enough when compared to the bottleneck information obtained with some detailed connected line-level models. The results from a real-world, multi-level industrial application study have demonstrated the feasibility of this approach, showing that the aggregation method can represent the underlying detailed line-level model for bottleneck analysis.
  •  
49.
  • Lidell, Anton, et al. (författare)
  • The Current and Future Challenges for Virtual Commissioning and Digital Twins of Production Lines
  • 2022
  • Ingår i: SPS2022. - Amsterdam; Berlin; Washington, DC : IOS Press. - 9781643682686 - 9781643682693 ; , s. 508-519
  • Konferensbidrag (refereegranskat)abstract
    • The use of virtual commissioning has increased in the last decade, but there are still challenges before the software code validation method is widespread in use. One of the extensions to virtual commissioning is the digital twin technology to allow for further improved accuracy. The aim of this paper is to review existing standards and approaches to developing virtual commissioning, through a literature review and interviews with experts in the industry. First, the definitions and classifications related to virtual commissioning and digital twins are reviewed, followed by, the approaches for the development of virtual commissioning and digital twins reported in the literature are explored. Then, in three interviews with experts of varying backgrounds and competencies, the views of the virtual technologies are assessed to provide new insight for the industry. The findings of the literature review and interviews are, among others, the apparent need for standardisation in the field and that a sought-after standard in the form of ISO 23247-1 is underway. The key finding of this paper is that digital twin is a concept with a promising future in combination with other technologies of Industry 4.0. We also outline the challenges and possibilities of virtual commissioning and the digital twin and could be used as a starting point for further research in standardisations and improvements sprung from the new standard.
  •  
50.
  • Linnéusson, Gary, et al. (författare)
  • A hybrid simulation-based optimization framework supporting strategic maintenance to improve production performance
  • 2020
  • Ingår i: European Journal of Operational Research. - : Elsevier. - 0377-2217 .- 1872-6860. ; 281:2, s. 402-414
  • Tidskriftsartikel (refereegranskat)abstract
    • Managing maintenance and its impact on business results is increasingly complex, calling for more advanced operational research methodologies to address the challenge of sustainable decision-making. This problem-based research has identified a framework of methods to supplement the operations research/management science literature by contributing a hybrid simulation-based optimization framework (HSBOF), extending previously reported research.Overall, it is the application of multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively which allows maintenance activities to be pinpointed in the production system based on analyzes generating less reactive work load on the maintenance organization. Therefore, the application of the HSBOF informs practice by a multiphase process, where each phase builds knowledge, starting with exploring feedback behaviors to why certain near-optimal maintenance behaviors arise, forming the basis of potential performance improvements, subsequently optimized using DES+MOO in a standard software, prioritizing the sequence of improvements in the production system for maintenance to implement.Studying literature on related hybridizations using optimization the proposed work can be considered novel, being based on SD+MOO industrial cases and their application to a DES+MOO software.
  •  
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