| 1. |
- Abourraja, Mohamed Nezar, et al.
(författare)
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A simulation-based decision support framework devoted to Ro-Ro terminals : Design, implementation and evaluation
- 2023
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Ingår i: Computers & industrial engineering. - : Elsevier BV. - 0360-8352 .- 1879-0550. ; 180, s. 109248-
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Tidskriftsartikel (refereegranskat)abstract
- As a type of intermodal terminal, pure Ro-Ro terminals are one of the most important logistic hubs in the supply chain for rolling freight stored in containerized and wheeled steel boxes. These large-scale systems are highly complex, with nonlinear and hard-to-predict behavior evolving in a stochastic environment. Consequently, making decisions about any problem thereof is no mean feat, particularly for terminal planners. To assist them in decision-making, a pool of relevant models and tools have been developed over the years in the literature. Nevertheless, models that are oriented toward specific objectives dominate, and generic ones are rare. This paper tries to fill this gap and proposes a generic framework to be used as a factory to create specific decision support models based on simulation for pure Ro-Ro terminals. This framework is formulated following two artifacts: (1) the known classification of key performance indicators combined with the typical functional and physical organization of pure Ro-Ro terminals; (2) the three main arteries of harbor systems, namely flows, decisions and operations. Then a scalable way of making decisions based on a flexible form of the cost function weighted according to a set of coefficients is integrated. These designed coefficients allow decision-makers a wide flexibly in choosing how the best solutions are determined. An application of this framework is illustrated through a real case study, where the weights are estimated using an expert-profiling based approach then pushed into the OptQuest optimizer to be calibrated before analyzing the results. These results are aggregated, then expressed as scores on a scale of 0 to 1. This is to help terminal planners to easily identify the worst and best planning scenarios as well as the relationships and compatibilities between the involved handling rules to suggest different alternatives for managing operations.
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| 2. |
- Agrawal, Tarun Kumar, et al.
(författare)
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Blockchain-based Framework for Supply Chain Traceability : A Case Example of Textile and Clothing Industry
- 2021
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Ingår i: Computers & industrial engineering. - : Elsevier BV. - 0360-8352 .- 1879-0550.
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Tidskriftsartikel (refereegranskat)abstract
- Traceability has emerged as a prime requirement for a multi-tier and multi-site production. It enables visibility and caters to the consumer requirements of transparency and quality assurance. Textile and clothing industry is one such example that requires traceability implementation to address prevailing problems of information asymmetry and low visibility. Customers find it difficult to access product data that can facilitate ethical buying practices or assure product authenticity. Besides, it is challenging for stakeholders to share crucial information in an insecure environment with risk of data manipulations and fear of losing information advantage. In this context, this study investigates and proposes a blockchain-based traceability framework for traceability in multi-tier textile and clothing supply chain. It conceptualizes the interaction of supply chain partners, and related network architecture at the organizational level and smart contract and transaction validation rules at the operational level. To illustrate the application of the proposed framework, the study presents an example of organic cotton supply chain using blockchain with customized smart contract and transaction rules. It finally demonstrates the applicability of the developed blockchain by testing it under two parameters. The proposed system can build a technology-based trust among the supply chain partners, where the distributed ledger can be used to store and authenticate supply chain transactions. Further, the blockchain-based traceability system would provide a unique opportunity, flexibility, and authority to all partners to trace-back their supply network and create transparent and sustainable supply chain.
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| 3. |
- Akillioglu, Hakan, et al.
(författare)
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Characterization of continuous precise workload control and analysis of idleness penalty
- 2016
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Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 102, s. 351-358
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Tidskriftsartikel (refereegranskat)abstract
- The variability in the market conditions is growing in terms of its frequency of change and range of diversity. In response to this new industrial panorama, research on production systems is aiming to achieve highly reconfigurable shop floors. Frequent changes in such systems require also frequent re-planning with updated information. In this regard the Continuous Precise Workload Control method, is a recent approach aiming at precise control of workload in shop floor with the use of direct load graphs. Supported by a multi-agent platform, it generates dynamic non-periodic release decisions exploiting real time shop floor information. The study in this paper is two folded; (1) the presented workload approach is defined in terms of eight dimensions of the workload control concept in order to highlight its distinctive characteristics and (2) the impact of idleness penalty factor is analyzed by an experiment design in order to investigate its effect on the job release decision. The results show that the idleness penalty factor decreases the idleness of the resources up to a point where the adverse effect is initiated.
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| 4. |
- Alzghoul, Ahmad, et al.
(författare)
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Data stream forecasting for system fault prediction
- 2012
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Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 62:4, s. 972-978
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Tidskriftsartikel (refereegranskat)abstract
- Competition among today’s industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing. In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM). The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.
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| 5. |
- Alzghoul, Ahmad, et al.
(författare)
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Increasing availability of industrial systems through data stream mining
- 2011
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Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 60:2, s. 195-205
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Tidskriftsartikel (refereegranskat)abstract
- Improving industrial product reliability, maintainability and thus availability is a challenging task for many industrial companies. In industry, there is a growing need to process data in real time, since the generated data volume exceeds the available storage capacity. This paper consists of a review of data stream mining and data stream management systems aimed at improving product availability. Further, a newly developed and validated grid-based classifier method is presented and compared to one-class support vector machine (OCSVM) and a polygon-based classifier.The results showed that, using 10% of the total data set to train the algorithm, all three methods achieved good (>95% correct) overall classification accuracy. In addition, all three methods can be applied on both offline and online data.The speed of the resultant function from the OCSVM method was, not surprisingly, higher than the other two methods, but in industrial applications the OCSVMs' comparatively long time needed for training is a possible challenge. The main advantage of the grid-based classification method is that it allows for calculation of the probability (%) that a data point belongs to a specific class, and the method can be easily modified to be incremental.The high classification accuracy can be utilized to detect the failures at an early stage, thereby increasing the reliability and thus the availability of the product (since availability is a function of maintainability and reliability). In addition, the consequences of equipment failures in terms of time and cost can be mitigated.
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| 6. |
- Beldar, Pedram, et al.
(författare)
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Non-identical parallel machines batch processing problem with release dates, due dates and variable maintenance activity to minimize total tardiness
- 2022
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Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 168
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Tidskriftsartikel (refereegranskat)abstract
- Combination of job scheduling and maintenance activity has been widely investigated in the literature. We consider a non-identical parallel machines batch processing (BP) problem with release dates, due dates and variable maintenance activity to minimize total tardiness. An original mixed integer linear programming (MILP) model is formulated to provide an optimal solution. As the problem under investigation is known to be strongly NP-hard, two meta-heuristic approaches based on Simulated Annealing (SA) and Variable Neighborhood Search (VNS) are developed. A constructive heuristic method (H) is proposed to generate initial feasible solutions for the SA and VNS. In order to evaluate the results of the proposed solution approaches, a set of instances were randomly generated. Moreover, we compare the performance of our proposed approaches against four meta-heuristic algorithms adopted from the literature. The obtained results indicate that the proposed solution methods have a competitive behaviour and they outperform the other meta-heuristics in most instances. Although in all cases, H + SA is the most performing algorithm compared to the others.
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| 7. |
- Cao, Cejun, et al.
(författare)
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Digital twin-driven robust bi-level optimisation model for COVID-19 medical waste location-transport under circular economy
- 2023
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Ingår i: Computers & industrial engineering. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0360-8352 .- 1879-0550. ; 186
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Tidskriftsartikel (refereegranskat)abstract
- COVID-19 medical waste collection-transport system, including the location of the related facilities, transport, and disposal, is a critical component in the circular economy. To promote the circularity of the waste management system and mitigate the spread of novel coronavirus, how to optimise COVID-19 medical waste location-transport strategies remains an open but urgent issue. In this paper, a novel digital twin-driven conceptual framework is proposed to improve the strategic decision on the location of temporary disposal centres and, subsequently, the operational decision on the transport of COVID-19 medical waste in the presence of hierarchical relationships amongst stakeholders, circular economy, environmental regulations, service level, and uncertainty in infection probability. The polyhedral uncertainty set is introduced to characterise stochastic infection probability. Digital twin technology is further used to estimate the upper and lower bound of the uncertainty set. Such a problem is formulated as a digital twin-driven robust bi-level mixed-integer programming model to minimise total infection risks on the upper level and total costs on the lower level. A hybrid solution strategy is designed to combine dual theory, Karush-Kuhn-Tucker (KKT) conditions, and a branch-and-bound approach. Finally, a real case study from Maharashtra in India is presented to evaluate the proposed model. Results demonstrate that the solution strategy performs well for such a complex problem because the CPU time required to conduct all experiments is less than one hour. Under a given uncertainty level of 36 and perturbation ratio of 20%, a regional transport strategy is preferred from generation points to transfer points, while a cross regional one is usually implemented from transfer points to disposal centres. It is of significance to determine the bound of available temporary disposal centres. Using digital technology (e.g., digital twin) to accurately estimate the amount of COVID-19 medical waste is beneficial for controlling the pandemic. Reducing infection risks relative to cost is the prioritised goal in cleaning up COVID-19 medical waste within a relatively long period.
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| 8. |
- Cebecauer, Matej, et al.
(författare)
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A versatile adaptive aggregation framework for spatially large discrete location-allocation problems
- 2017
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Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 111, s. 364-380
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Tidskriftsartikel (refereegranskat)abstract
- We propose a versatile concept of the adaptive aggregation framework for the facility location problems that keeps the problem size in reasonable limits. Most location-allocation problems are known to be NP-hard. Thus, if a problem reaches the critical size, the computation exceeds reasonable time limits, or all computer memory is consumed. Aggregation is a tool that allows for transforming problems into smaller sizes. Usually, it is used only in the data preparation phase, and it leads to the loss of optimality due to aggregation errors. This is particularly remarkable when solving problems with a large number of demand points. The proposed framework embeds the aggregation into the solving process and it iteratively adjusts the aggregation level to the high quality solutions. To explore its versatility, we apply it to the p-median and to the lexicographic minimax problems that lead to structurally different patterns of located facilities. To evaluate the optimality errors, we use benchmarks which can be computed exactly, and to explore the limits of our approach, we study benchmarks reaching 670,000 demand points. Numerical experiments reveal that the adaptive aggregation framework performs well across a large range of problem sizes and is able to provide solutions of higher quality than the state-of-the-art exact methods when applied to the aggregated problem.
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| 9. |
- Englund, Cristofer, et al.
(författare)
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Ink flow control by multiple models in an offset lithographic printing process
- 2008
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Ingår i: Computers & industrial engineering. - Oxford : Pergamon Press. - 0360-8352 .- 1879-0550. ; 55:3, s. 592-605
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Tidskriftsartikel (refereegranskat)abstract
- A multiple model-based controller has been developed aiming at controlling the ink flow in the offset lithographic printing process. The control system consists of a model pool of four couples of inverse and direct models. Each couple evaluates a number of probable control signals and the couple, generating the most suitable control signal is used to control the printing press, at that moment. The developed system has been tested at a newspaper printing shop during normal production. The results show that the developed modelling and control system is able to drive the output of the printing press to the desired target levels.
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| 10. |
- Fredin, Johan, et al.
(författare)
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Holistic methodology using computer simulation for optimisation of machine tools
- 2012
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Ingår i: Computers & industrial engineering. - : Pergamon Elsevier. - 0360-8352 .- 1879-0550. ; 63:1, s. 294-301
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Tidskriftsartikel (refereegranskat)abstract
- Virtual machine concepts supporting optimisation of machine tools have been developed in earlier work. The virtual machine concept is a tool that can describe the behaviour of a machine tool while considering the interaction between mechanics of the machines and the control system. Considerable amount of work has been done proving the concept and showing the potential of such a design tool in different contexts. Several studies have shown the potential of using the virtual machine concept, although, no work has been found that is exploring the potential of a full optimisation study. The aim of this work is to show the potential of the virtual machine concept in an optimisation study of the complete machine tool, including the mechanical system, parameters in the control system, the NC-code as well as choice of servo and drive systems. An efficient optimisation strategy is presented, making it possible to solve the complex optimisation problem within a reasonable amount of time. A combination of optimisation algorithms is used to achieve a fast and accurate way of solving the complex task to optimise the complete machine tool. Genetic algorithms, gradient based algorithms and more traditional hands on engineering are used for solving the optimisation problem. Post processing and data mining is suggested as a way of extracting as much information as possible from optimisation results with the aim to increase the knowledge about the studied system. An important conclusion is that the virtual machine should support the decision making in product development, not replace the product developers as regards decision making.
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