| 1. |
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| 2. |
- Adane, Tigist Fetene
(författare)
-
Manufacturing Dynamics and Performance Evaluation
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Manufacturing companies are striving to remain competitive in the market and maintain their economic growth and productivity. Uncertainties regarding the changes in product demand, workpiece material, product design, and technological advancement, have imposed pressure on manufacturing systems. Market uncertainties force manufacturing companies to be flexible and responsive in producing different parts, by adapting the existing system without the need for a substantial investment. The market is characterized by time variations in product quantities and varieties while manufacturing systems remain inherently fixed. To sustain competitive manufacturing, a company has to adopt to new production requirements and be responsive to market changes quickly. Conscious decisions have to be made for a system to respond to market fluctuations. In order to respond to the dynamic changes, there is a need for developing methodologies that analyse, evaluate and control performance of manufacturing system at the system and/or process levels.The primary focus of the thesis is to develop a novel generic framework for modelling and controlling manufacturing systems intending for improvement of the performance of manufacturing and make companies more competitive. The framework incorporates the complex interrelations between the process and system parameters, i.e., the dynamics of the system. Thus, provides a quantitative and qualitative analysis for performance evaluation and for optimizing performance of manufacturing system. The generic framework can further be adapted for studying specific manufacturing systems in discrete manufacturing. Three case studies are presented. The case studies are performed in an automotive company where the effect of various levels of control is investigated in manufacturing systems configured as transfer line or as a flexible manufacturing system.Two aspects of the dynamic nature of manufacturing system are investigated in this thesis: (1) The engineering nature of the system, i.e., the selection of appropriate process parameters to manufacture a product according to the design specification, and (2) The business nature of the system, i.e., the selection of system parameters with respect to the way the product is manufactured. At the process level, the parameters are controlled within the process capability limits to adapt to the changes of the system parameters in response to the market dynamics. At the system level, operational parameters are controlled to satisfy performance criteria.A case study for resource use analysis during primary processes has also been investigated and presented. The critical operations and the operations that have the highest energy consumptions and the potential for energy savings have been identified.The methodology developed for analysing the performance of the dynamic manufacturing system is based on a system dynamics modelling approach. Results obtained from different modelling approaches are presented and compared based on the selected performance metrics.
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| 3. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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A framework for simulation-based multi-objective optimization and knowledge discovery of machining process
- 2018
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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
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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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| 4. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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Metamodel based multi-objective optimization of a turning process by using finite element simulation
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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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| 5. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
-
Metamodel based multi-objective optimization of a turning process by using finite element simulation
- 2020
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Ingår i: Engineering optimization (Print). - : Taylor & Francis Group. - 0305-215X .- 1029-0273. ; 52:7, s. 1261-1278
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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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| 6. |
- Amouzgar, Kaveh, 1980-
(författare)
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Metamodel Based Multi-Objective Optimization with Finite-Element Applications
- 2018
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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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| 7. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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Multi-objective optimisation of tool indexing problem : a mathematical model and a modified genetic algorithm
- 2021
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Ingår i: International Journal of Production Research. - : Taylor & Francis Group. - 0020-7543 .- 1366-588X. ; 59:12, s. 3572-3590
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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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| 8. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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Optimizing index positions on CNC tool magazines considering cutting tool life and duplicates
- 2020
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; 93, s. 1508-1513
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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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| 9. |
- Amouzgar, Kaveh, 1980-, et al.
(författare)
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Radial basis functions with a priori bias as surrogate models : A comparative study
- 2018
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier. - 0952-1976 .- 1873-6769. ; 71, s. 28-44
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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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| 10. |
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| 11. |
- Andersson, Martin, 1981-
(författare)
-
A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing : Understanding optimization algorithms through optimization
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Most optimization problems found in the real world cannot be solved using analytical methods. For these types of difficult optimization problems, an alternative approach is needed. Metaheuristics are a category of optimization algorithms that do not guarantee that an optimal solution will be found, but instead search for the best solutions using some general heuristics. Metaheuristics have been shown to be effective at finding “good-enough” solutions to a wide variety of difficult problems. Most metaheuristics involve control parameters that can be used to modify how the heuristics perform its search. This is necessary because different problems may 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. The problem of finding these optimal control parameter values is known as parameter tuning and is the main topic of this thesis. This thesis uses a bilevel optimization approach to solve parameter tuning problems. In this approach, the parameter tuning problem itself is formulated as an optimization problem and solved with an optimization algorithm. The parameter tuning problem formulated as a bilevel optimization problem is challenging because of nonlinear objective functions, interacting variables, multiple local optima, and noise. However, it is in precisely this kind of difficult optimization problem that evolutionary algorithms, which are a subclass of metaheuristics, have been shown to be effective. That is the motivation for using evolutionary algorithms for the upper-level optimization (i.e. tuning algorithm) of the bilevel optimization approach. Solving the parameter tuning problem using a bilevel optimization approach is also computationally expensive, since a complete optimization run has to be completed for every evaluation of a set of control parameter values. It is therefore important that the tuning algorithm be as efficient as possible, so that the parameter tuning problem can be solved to a satisfactory level with relatively few evaluations. Even so, bilevel optimization experiments can take a long time to run on a single computer. There is, however, considerable parallelization potential in the bilevel optimization approach, since many of the optimizations are independent of one another. This thesis has three primary aims: first, to present a bilevel optimization framework and software architecture for parallel parameter tuning; second, to use this framework and software architecture to evaluate and configure evolutionary algorithms as tuners and compare them with other parameter tuning methods; and, finally, to use parameter tuning experiments to gain new insights into and understanding of how optimization algorithms work and how they can used be to their maximum potential. The proposed framework and software architecture have been implemented and deployed in more than one hundred computers running many thousands of parameter tuning experiments for many millions of optimizations. This illustrates that this design and implementation approach can handle large parameter tuning experiments. Two types of evolutionary algorithms, i.e. differential evolution (DE) and a genetic algorithm (GA), have been evaluated as tuners against the parameter tuning algorithm irace. The as pects of algorithm configuration and noise handling for DE and the GA as related to the parameter tuning problem were also investigated. The results indicate that dynamic resampling strategies outperform static resampling strategies. It was also shown that the GA needs an explicit exploration and exploitation strategy in order not become stuck in local optima. The comparison with irace shows that both DE and the GA can significantly outperform it in a variety of different tuning problems.
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| 12. |
- Andersson, Martin, 1981-, et al.
(författare)
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A Parallel Computing Software Architecture for the Bilevel Parameter Tuning of Optimization Algorithms
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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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| 13. |
- Andersson, Marcus, et al.
(författare)
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A web-based simulation optimization system for industrial scheduling
- 2007
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Ingår i: Proceedings of the 39th conference on Winter simulation. - : IEEE Press. - 1424413060 ; , s. 1844-1852
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Konferensbidrag (refereegranskat)abstract
- Many real-world production systems are complex in nature and it is a real challenge to find an efficient scheduling method that satisfies the production requirements as well as utilizes the resources efficiently. Tools like discrete event simulation (DES) are very useful for modeling these systems and can be used to test and compare different schedules before dispatching the best schedules to the targeted systems. DES alone, however, cannot be used to find the "optimal" schedule. Simulation-based optimization (SO) can be used to search for optimal schedules efficiently without too much user intervention. Observing that long computing time may prohibit the interest in using SO for industrial scheduling, various techniques to speed up the SO process have to be explored. This paper presents a case study that shows the use of a Web-based parallel and distributed SO platform to support the operations scheduling of a machining line in an automotive factory.
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| 14. |
- Andersson, Martin, 1981-, et al.
(författare)
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Evolutionary Simulation Optimization of Personnel Scheduling
- 2014
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Ingår i: 12th International Industrial Simulation Conference 2014. - : Eurosis. - 9789077381830 ; , s. 61-65
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Konferensbidrag (refereegranskat)abstract
- This paper presents a simulation-optimization system for personnel scheduling. The system is developed for the Swedish postal services and aims at finding personnel schedules that minimizes both total man hours and the administrative burden of the person responsible for handling schedules. For the optimization, the multi-objective evolutionary algorithm NSGA-II is implemented. The simulation-optimization system is evaluated on a real-world test case and results from the evaluation shows that the algorithm is successful in optimizing the problem.
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| 15. |
- Andersson, Martin, 1981-, et al.
(författare)
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On the Trade-off Between Runtime and Evaluation Efficiency In Evolutionary Algorithms
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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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| 16. |
- Andersson, Martin, 1981-, et al.
(författare)
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Parameter tuned CMA-ES on the CEC'15 expensive problems
- 2015
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Ingår i: 2015 IEEE Congress on Evolutionary Computation (CEC). - : IEEE conference proceedings. - 9781479974924 - 9781479974917 ; , s. 1950-1957
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Konferensbidrag (refereegranskat)abstract
- Evolutionary optimization algorithms have parameters that are used to adapt the search strategy to suit different optimization problems. Selecting the optimal parameter values for a given problem is difficult without a-priori knowledge. Experimental studies can provide this knowledge by finding the best parameter values for a specific set of problems. This knowledge can also be constructed into heuristics (rule-of-thumbs) that can adapt the parameters for the problem. The aim of this paper is to assess the heuristics of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm. This is accomplished by tuning CMA-ES parameters so as to maximize its performance on the CEC'15 problems, using a bilevel optimization approach that searches for the optimal parameter values. The optimized parameter values are compared against the parameter values suggested by the heuristics. The difference between specialized and generalized parameter values are also investigated.
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| 17. |
- Andersson, Martin, 1981-, et al.
(författare)
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Parameter Tuning Evolutionary Algorithms for Runtime versus Cost Trade-off in a Cloud Computing Environment
- 2018
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Ingår i: Simulation Modelling Practice and Theory. - : Elsevier. - 1569-190X. ; 89, s. 195-205
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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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| 18. |
- Andersson, Martin, 1981-, et al.
(författare)
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Parameter Tuning of MOEAs Using a Bilevel Optimization Approach
- 2015
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Ingår i: Evolutionary Multi-Criterion Optimization. - Cham : Springer International Publishing Switzerland. - 9783319159331 - 9783319159348 ; , s. 233-247
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Konferensbidrag (refereegranskat)abstract
- The performance of an Evolutionary Algorithm (EA) can be greatly influenced by its parameters. The optimal parameter settings are also not necessarily the same across different problems. Finding the optimal set of parameters is therefore a difficult and often time-consuming task. This paper presents results of parameter tuning experiments on the NSGA-II and NSGA-III algorithms using the ZDT test problems. The aim is to gain new insights on the characteristics of the optimal parameter settings and to study if the parameters impose the same effect on both NSGA-II and NSGA-III. The experiments also aim at testing if the rule of thumb that the mutation probability should be set to one divided by the number of decision variables is a good heuristic on the ZDT problems. A comparison of the performance of NSGA-II and NSGA-III on the ZDT problems is also made.
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| 19. |
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| 20. |
- Andersson, Marcus, et al.
(författare)
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Simulation Optimization for Industrial Scheduling Using Hybrid Genetic Representation
- 2008
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Ingår i: Proceedings of the 2008 Winter Simulation Conference. - : IEEE conference proceedings. - 9781424427086 ; , s. 2004-2011
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Konferensbidrag (refereegranskat)abstract
- Simulation modeling has the capability to represent complex real-world systems in details and therefore it is suitable to develop simulation models for generating detailed operation plans to control the shop floor. In the literature, there are two major approaches for tackling the simulation-based scheduling problems, namely dispatching rules and using meta-heuristic search algorithms. The purpose of this paper is to illustrate that there are advantages when these two approaches are combined. More precisely, this paper introduces a novel hybrid genetic representation as a combination of both a partially completed schedule (direct) and the optimal dispatching rules (indirect), for setting the schedules for some critical stages (e.g. bottlenecks) and other non-critical stages respectively. When applied to an industrial case study, this hybrid method has been found to outperform the two common approaches, in terms of finding reasonably good solutions within a shorter time period for most of the complex scheduling scenarios.
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| 21. |
- Andersson, Martin, et al.
(författare)
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Towards Optimal Algorithmic Parameters for Simulation-Based Multi-Objective Optimization
- 2016
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Ingår i: 2016 IEEE Congress on Evolutionary Computation (CEC). - New York : IEEE. - 9781509006236 - 9781509006229 - 9781509006243 ; , s. 5162-5169
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Konferensbidrag (refereegranskat)abstract
- The use of optimization to solve a simulation-based multi-objective problem produces a set of solutions that provide information about the trade-offs that have to be considered by the decision maker. An incomplete or sub-optimal set of solutions will negatively affect the quality of any subsequent decisions. The parameters that control the search behavior of an optimization algorithm can be used to minimize this risk. However, choosing good parameter settings for a given optimization algorithm and problem combination is difficult. The aim of this paper is to take a step towards optimal parameter settings for optimization of simulation-based problems. Two parameter tuning methods, Latin Hypercube Sampling and Genetic Algorithms, are used to maximize the performance of NSGA-II applied to a simulation-based problem with discrete variables. The strengths and weaknesses of both methods are analyzed. The effect of the number of decision variables and the function budget on the optimal parameter settings is also studied.
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| 22. |
- Andersson, Martin, et al.
(författare)
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Tuning of Multiple Parameter Sets in Evolutionary Algorithms
- 2016
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Ingår i: GECCO'16. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450342063 ; , s. 533-540
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Konferensbidrag (refereegranskat)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 NSGA-II 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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| 23. |
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| 24. |
- Aslam, Tehseen, et al.
(författare)
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Agent-based Simulation and Simulation-based Optimisation for Supply Chain Management
- 2010
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Ingår i: Enterprise Networks and Logistics for Agile Manufacturing. - London : Springer London. - 9781849962438 - 9781849962445 ; , s. 227-247
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Agent-based simulation (ABS) represents a paradigm in the modelling and simulation of complex and dynamic systems distributed in time and space. Since manufacturing and logistics operations are characterised by distributed activities as well as decision making - in both time and in space - and can be regarded as complex, the ABS approach is highly appropriate for these types of systems. The aim of this chapter is to present a new framework of applying ABS and simulation-based optimisation techniques to supply chain management, which considers the entities (supplier, manufacturer, distributor and retailer) in the supply chain as intelligent agents in a simulation. This chapter also gives an outline on how these agents pursue their local objectives/goals as well as how they react and interact with each other to achieve a more holistic objective(s)/goal(s).
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| 25. |
- Aslam, Tehseen, 1981-
(författare)
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Analysis of manufacturing supply chains using system dynamics and multi-objective optimization
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. Hence, due to the multiple performance measures, supply chain decision making is much more complex than treating it as a single objective optimization problem. Thus, the aim of the doctoral thesis is to address the supply chain optimization problem within a truly Pareto-based multi-objective context and utilize knowledge extraction techniques to extract valuable and useful information from the Pareto optimal solutions. By knowledge extraction, it means to detect hidden interrelationships between the Pareto solutions, identify common properties and characteristics of the Pareto solutions as well as discover concealed structures in the Pareto optimal data set in order to support managers in their decision making. This aim is addressed through the SBO-framework where the simulation methodology is based on system dynamics (SD) and the optimization utilizes multi-objective optimization (MOO). In order to connect the SD and MOO software, this doctoral thesis introduced a novel SD and MOO interface application which allow the modeling and optimization applications to interact. Additionally, this thesis work also presents a novel SD-MOO methodology that addresses the issue of curse off dimensionality in MOO for higher dimensional problems and with the aim to execute supply chain SD-MOO in a computationally cost efficient way, in terms of convergence, solution intensification and accuracy of obtaining the Pareto-optimal front for complex supply chain problems. In order to detect evident and hidden structures, characteristics and properties of the Pareto-optimal solutions, this work utilizes Parallel Coordinates, Clustering and Innovization, which are three different types of tools for post-optimal analysis and facilitators of discovering and retrieving knowledge from the Pareto-optimal set. The developed SD-MOO interface and methodology are then verified and validated through two academic case studies and a real-world industrial application case study. While not all the insights generated in these application studies can be generalized for other supply-chain systems, the analysis results provide strong indications that the methodology and techniques introduced in this thesis are capable to generate knowledge to support academic SCM research and real-world SCM decision making, which to our knowledge cannot be performed by other methods.
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| 26. |
- Aslam, Tehseen, et al.
(författare)
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Combining system dynamics and multi-objective optimization with design space reduction
- 2016
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Ingår i: Industrial management & data systems. - : Emerald Group Publishing Limited. - 0263-5577 .- 1758-5783. ; 116:2, s. 291-321
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Tidskriftsartikel (refereegranskat)abstract
- Purpose The purpose of this study is to introduce an effective methodology for obtaining Pareto-optimal solutions, when combining System Dynamics (SD) and Multi-Objective Optimization (MOO) for supply chain problems.Design/methodology/approach This paper proposes a new approach that combines SD and MOO within a simulation-based optimization framework to generate the efficient frontier that supports decision- making in SupplyChain Management (SCM). It also addresses the issue of the curse of dimensionality, commonly found in practical optimization problems, through design space reduction.Findings The integrated MOO and SD approach has been shown to be very useful in revealing how the decision variables in the Beer Game affect the optimality of the three common SCM objectives, namely, the minimization of inventory, backlog, and the bullwhip effect. The results of the in-depth Beer Game study clearly show that these three optimization objectives are in conflict with each other, in the sense that a supply chain manager cannot minimize the bullwhip effect without increasing the total inventory and total backlog levels.Practical implications Having a methodology that enables the effective generation of optimal trade-off solutions, in terms of computational cost, time, as well as solution diversity and intensification, not only assists decision makers to make decisions on time, but also presents a diverse and intense solution set to choose from.Originality/value This paper presents a novel supply chain MOO methodology that helps to find Pareto-optimal solutions in a more effective manner. In order to do so, the methodology tackles the so-called curse of dimensionality, by reducing the design space and focusing the search of the optimization to regions of interest. Together with design space reduction, it is believed that the integrated SD and MOOapproach can provide an innovative and efficient method for the design and analysis of manufacturing supply chain systems in general.
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| 27. |
- Aslam, Tehseen, et al.
(författare)
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Integrating system dynamics and multi-objective optimisation for manufacturing supply chain analysis
- 2014
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Ingår i: International Journal of Manufacturing Research. - : InderScience Publishers. - 1750-0605. ; 9:1, s. 27-57
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this paper is to address the dilemma of supply chain management (SCM) within a truly Pareto-based multi-objective context. This is done by introducing an integration of system dynamics and multi-objective optimisation. An extended version of the well-known pedagogical SCMproblem, the Beer Game, originally developed at MIT since the 1960s, has been used as the illustrative example. As will be discussed in the paper, the integrated multi-objective optimisation and system dynamics model has been shown to be very useful for revealing how the parameters in the Beer Game affect the optimality of the three common SCM objectives, namely, the minimisation of inventory cost, backlog cost, and the bullwhip effect.
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| 28. |
- Aslam, Tehseen, et al.
(författare)
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Multi-objective Optimisation in Manufacturing Supply Chain Systems Design : A Comprehensive Survey and New Directions
- 2011
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Ingår i: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. - London : Springer London. - 9780857296177 - 9780857296528 ; , s. 35-70
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Bokkapitel (refereegranskat)abstract
- Research regarding supply chain optimisation has been performed for a long time. However, it is only in the last decade that the research community has started to investigate multi-objective optimisation for supply chains. Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. In this chapter, we present a comprehensive literature review of existing multi-objective optimisation applications, both analytical-based and simulation-based, in supply chain management publications. Later on in the chapter, we identify the needs of an integration of multi-objective optimisation and system dynamics models, and present a case study on how such kind of integration can be applied for the investigation of bullwhip effects in a supply chain.
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| 29. |
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| 30. |
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| 31. |
- Aslam, Tehseen, et al.
(författare)
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Multi-Objective Optimization for Supply Chain Management : A Literature Review and New Development
- 2010
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Ingår i: SCMIS 2010 - Proceedings of 2010 8th International Conference on Supply Chain Management and Information Systems. - : The Hong Kong Polytechnic University. - 9789623676977 - 9789623676960 ; , s. Article number 5681724-
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Konferensbidrag (refereegranskat)abstract
- Research regarding supply chain optimization has been performed for a long time. However, it’s only in the last decade that the research community has started to investigate multi-objective optimization for supply chains. Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. In this paper, we present a literature review of existing multi-objective optimization applications, both analytical-based and simulation-based, in supply chain management publications. Based on the literature review, the need for research in a multi-objective and multi-level optimization framework for supply chain management is proposed. Such a framework considers not only the optimization of the overall supply chain, but also for each entity within the supply chain, in a multi-objective optimization context.
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| 32. |
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| 33. |
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| 34. |
- Aslam, Tehseen, et al.
(författare)
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Strategy evaluation using system dynamics and multi-objective optimization for an internal supply chain
- 2015
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Ingår i: Proceedings of the 2015 Winter Simulation Conference. - Piscataway, NJ, USA : IEEE Press. - 9781467397438 ; , s. 2033-2044
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Konferensbidrag (refereegranskat)abstract
- System dynamics, which is an approach built on information feedbacks and delays in the model in order to understand the dynamical behavior of a system, has successfully been implemented for supply chain management problems for many years. However, research within in multi-objective optimization of supply chain problems modelled through system dynamics has been scares. Supply chain decision making is much more complex than treating it as a single objective optimization problem due to the fact that supply chains are subjected to the multiple performance measures when optimizing its process. This paper presents an industrial application study utilizing the simulation based optimization framework by combining system dynamics simulation and multi-objective optimization. The industrial study depicts a conceptual system dynamics model for internal logistics system with the aim to evaluate the effects of different material flow control strategies by minimizing total system work-on-process as wells as total delivery delay.
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| 35. |
- Aslam, Tehseen, 1981-, et al.
(författare)
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Towards an industrial testbed for holistic virtual production development
- 2018
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Ingår i: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 369-374
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Konferensbidrag (refereegranskat)abstract
- Virtual production development is adopted by many companies in the production industry and digital models and virtual tools are utilized for strategic, tactical and operational decisions in almost every stage of the value chain. This paper suggest a testbed concept that aims the production industry to adopt a virtual production development process with integrated tool chains that enables holistic optimizations, all the way from the overall supply chain performance down to individual equipment/devices. The testbed, which is fully virtual, provides a mean for development and testing of integrated digital models and virtual tools, including both technical and methodological aspects.
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| 36. |
- Ayani, Mikel, et al.
(författare)
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Digital Twin : Applying emulation for machine reconditioning
- 2018
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; 72, s. 243-248
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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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| 37. |
- Ayani, Mikel, et al.
(författare)
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Optimizing Cycle Time and Energy Efficiency of a Robotic Cell Using an Emulation Model
- 2018
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Ingår i: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 411-416
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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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| 38. |
- Bandaru, Sunith, 1984-, et al.
(författare)
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An empirical comparison of metamodeling strategies in noisy environments
- 2018
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Ingår i: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018). - New York, NY, USA : ACM Digital Library. - 9781450356183 ; , s. 817-824
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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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| 39. |
- Bandaru, Sunith, et al.
(författare)
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Data mining methods for knowledge discovery in multi-objective optimization : Part A - Survey
- 2017
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 70, s. 139-159
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Forskningsöversikt (refereegranskat)abstract
- Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems.
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| 40. |
- Bandaru, Sunith, et al.
(författare)
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Data mining methods for knowledge discovery in multi-objective optimization : Part B - New developments and applications
- 2017
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 70, s. 119-138
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Tidskriftsartikel (refereegranskat)abstract
- The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision maker's preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences.
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| 41. |
- Bandaru, Sunith, et al.
(författare)
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Generalized higher-level automated innovization with application to inventory management
- 2015
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Ingår i: European Journal of Operational Research. - : Elsevier. - 0377-2217 .- 1872-6860. ; 243:2, s. 480-496
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Tidskriftsartikel (refereegranskat)abstract
- This paper generalizes the automated innovization framework using genetic programming in the context of higher-level innovization. Automated innovization is an unsupervised machine learning technique that can automatically extract significant mathematical relationships from Pareto-optimal solution sets. These resulting relationships describe the conditions for Pareto-optimality for the multi-objective problem under consideration and can be used by scientists and practitioners as thumb rules to understand the problem better and to innovate new problem solving techniques; hence the name innovization (innovation through optimization). Higher-level innovization involves performing automated innovization on multiple Pareto-optimal solution sets obtained by varying one or more problem parameters. The automated innovization framework was recently updated using genetic programming. We extend this generalization to perform higher-level automated innovization and demonstrate the methodology on a standard two-bar bi-objective truss design problem. The procedure is then applied to a classic case of inventory management with multi-objective optimization performed at both system and process levels. The applicability of automated innovization to this area should motivate its use in other avenues of operational research.
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| 42. |
- Bandaru, Sunith, et al.
(författare)
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Metamodel-based prediction of performance metrics for bilevel parameter tuning in MOEAs
- 2016
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Ingår i: 2016 IEEE Congress on Evolutionary Computation (CEC)<em></em>. - New York : IEEE. - 9781509006236 - 9781509006229 - 9781509006243 ; , s. 1909-1916
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Konferensbidrag (refereegranskat)abstract
- We consider a bilevel parameter tuning problem where the goal is to maximize the performance of a given multi-objective evolutionary optimizer on a given problem. The search for optimal algorithmic parameters requires the assessment of several sets of parameters, through multiple optimization runs, in order to mitigate the effect of noise that is inherent to evolutionary algorithms. This task is computationally expensive and therefore, in this paper, we propose to use sampling and metamodeling to approximate the performance of the optimizer as a function of its parameters. While such an approach is not unheard of, the choice of the metamodel to be used still remains unclear. The aim of this paper is to empirically compare 11 different metamodeling techniques with respect to their accuracy and training times in predicting two popular multi-objective performance metrics, namely, the hypervolume and the inverted generational distance. For the experiments in this pilot study, NSGA-II is used as the multi-objective optimizer for solving ZDT problems, 1 through 4.
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| 43. |
- Bandaru, Sunith, et al.
(författare)
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On the Performance of Classification Algorithms for Learning Pareto-Dominance Relations
- 2014
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Ingår i: Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC). - : IEEE Press. - 9781479914883 - 9781479966264 ; , s. 1139-1146
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Konferensbidrag (refereegranskat)abstract
- Multi-objective evolutionary algorithms (MOEAs)are often criticized for their high-computational costs. Thisbecomes especially relevant in simulation-based optimizationwhere the objectives lack a closed form and are expensive toevaluate. Over the years, meta-modeling or surrogate modelingtechniques have been used to build inexpensive approximationsof the objective functions which reduce the overall number offunction evaluations (simulations). Some recent studies however,have pointed out that accurate models of the objective functionsmay not be required at all since evolutionary algorithms onlyrely on the relative ranking of candidate solutions. Extendingthis notion to MOEAs, algorithms which can ‘learn’ Paretodominancerelations can be used to compare candidate solutionsunder multiple objectives. With this goal in mind, in thispaper, we study the performance of ten different off-the-shelfclassification algorithms for learning Pareto-dominance relationsin the ZDT test suite of benchmark problems. We considerprediction accuracy and training time as performance measureswith respect to dimensionality and skewness of the training data.Being a preliminary study, this paper does not include results ofintegrating the classifiers into the search process of MOEAs.
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| 44. |
- Bandaru, Sunith, 1984-, et al.
(författare)
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On the scalability of meta-models in simulation-based optimization of production systems
- 2015
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Ingår i: Proceedings of the 2015 Winter Simulation Conference. - Piscataway, NJ : IEEE Press. - 9781467397438 ; , s. 3644-3655
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Konferensbidrag (refereegranskat)abstract
- Optimization of production systems often involves numerous simulations of computationally expensive discrete-event models. When derivative-free optimization is sought, one usually resorts to evolutionary and other population-based meta-heuristics. These algorithms typically demand a large number of objective function evaluations, which in turn, drastically increases the computational cost of simulations. To counteract this, meta-models are used to replace expensive simulations with inexpensive approximations. Despite their widespread use, a thorough evaluation of meta-modeling methods has not been carried out yet to the authors' knowledge. In this paper, we analyze 10 different meta-models with respect to their accuracy and training time as a function of the number of training samples and the problem dimension. For our experiments, we choose a standard discrete-event model of an unpaced flow line with scalable number of machines and buffers. The best performing meta-model is then used with an evolutionary algorithm to perform multi-objective optimization of the production model.
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| 45. |
- Bandaru, Sunith, 1984-, et al.
(författare)
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Trend Mining : A Visualization Technique to Discover Variable Trends in the Objective Space
- 2019
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Ingår i: Evolutionary Multi-Criterion Optimization. - Cham, Switzerland : Springer. - 9783030125974 - 9783030125981 ; , s. 605-617
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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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| 46. |
- Barrera Diaz, Carlos Alberto, 1987-, et al.
(författare)
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An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems
- 2023
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Ingår i: Mathematics. - : MDPI. - 2227-7390. ; 11:6
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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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| 47. |
- Barrera Diaz, Carlos Alberto, 1987-, et al.
(författare)
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Enabling Knowledge Discovery from Simulation-Based Multi-Objective Optimization in Reconfigurable Manufacturing Systems
- 2022
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Ingår i: Proceedings of the 2022 Winter Simulation Conference. - : IEEE. - 9781665476614 - 9781665476621 ; , s. 1794-1805
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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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| 48. |
- Barrera Diaz, Carlos Alberto, 1987-, et al.
(författare)
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Optimizing reconfigurable manufacturing systems : A Simulation-based Multi-objective Optimization approach
- 2021
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; 104, s. 1837-1842
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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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| 49. |
- Barrera Diaz, Carlos Alberto, 1987-, et al.
(författare)
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Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes : A Simulation-Based Multi-Objective Approach
- 2021
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 9, s. 144195-144210
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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.
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| 50. |
- Barrera Diaz, Carlos Alberto, 1987-, et al.
(författare)
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Simulation-based multi-objective optimization for reconfigurable manufacturing system configurations analysis
- 2020
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Ingår i: Proceedings of the 2020 Winter Simulation Conference. - : IEEE. - 9781728194998 - 9781728195001 ; , s. 1527-1538, s. 1527-1538
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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.
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