Integrating system dynamics and multi-objective optimisation for manufacturing supply chain analysis

Aslam, Tehseen; Ng, Amos H. C.; Karlsson, Ingemar

doi:10.1504/ijmr.2014.059598

Cited by 6 publications

(4 citation statements)

References 15 publications

(18 reference statements)

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“…Poornikoo and Qureshi ( 2019 ) combined fuzzy modelling with SDS for decreasing the bullwhip effect in single-product, multi-echelon, multi-period manufacturing supply chains. Aslam et al ( 2014 ) combined multi-objective optimisation with SDS for minimising total inventory, backlog costs, and demand fluctuations in manufacturing supply chains.…”

Section: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Wofuru-Nyenke

Briggs

Aikhuele

2022

Process Integr Optim Sustain

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Managers at various echelons of supply chains are continuously faced with problems of examining and improving supply chain processes with the aim of improving productivity and customer service level, while reducing emissions and total costs simultaneously. This study is aimed at presenting the trends in sustainable manufacturing supply chain modelling in order to establish the modelling approaches which have predominantly been used for improving manufacturing supply chains from years 2010 to 2020. The study employs the systematic literature review methodology for reviewing articles published within the 11-year period. We proffer a classification approach for manufacturing supply chain models, grouping these models into mathematical models, simulation models, hybrid models, and their subcategories. The results showed that though there is a rising trend in the use of simulation and hybrid models, mathematical models have been used more for sustainable manufacturing supply chain modelling. The rise in the use of simulation and hybrid models can be explained by the fact that these models tend to handle uncertain and stochastic data better than mathematical models, which perform better with deterministic data. This research will aid other researchers in recognising the current gaps in manufacturing supply chain modelling in order to identify future research directions.

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Section: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Wofuru-Nyenke

Briggs

Aikhuele

2022

Process Integr Optim Sustain

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“…With R-NSGA-II those areas can be explored and the gaps can be filled. One such case is described in (Aslam, Karlsson, and Ng, 2012).…”

Section: Discussionmentioning

confidence: 99%

Reference point-based evolutionary multi-objective optimization for industrial systems simulation

Siegmund

Bernedixen

Pehrsson

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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In Multi-objective Optimization the goal is to present a set of Pareto-optimal solutions to the decision maker (DM). One out of these solutions is then chosen according to the DM preferences. Given that the DM has some general idea of what type of solution is preferred, a more efficient optimization could be run. This can be accomplished by letting the optimization algorithm make use of this preference information and guide the search towards better solutions that correspond to the preferences. One example for such kind of algorithms is the Reference point-based NSGA-II algorithm (R-NSGA-II), by which userspecified reference points can be used to guide the search in the objective space and the diversity of the focused Pareto-set can be controlled. In this paper, the applicability of the R-NSGA-II algorithm in solving industrial-scale simulation-based optimization problems is illustrated through a case study for the improvement of a production line. INTRODUCTIONSimulation-based multi-objective optimization problems are in many cases difficult to solve which is caused by the vast number of simulation runs that are needed in order to find a converged and diverse set of Pareto-optimal solutions. The standard goal usually pursued in multi-objective optimization is to find a converged set of solutions that is also featuring diversity. This goal requires to find a large number of solutions to cover the whole Pareto-front. Besides a complex problem structure the high number of simulation runs can also be caused by uncertainty in the input or output parameters of the model which has to be handled by simulating every solution multiple times and using the average objective values. Also a high number of problem objectives can make it necessary to execute many solution evaluations. This is because the Pareto-optimal front of a high-dimensional problem is a large subspace. In order to cover it sufficiently many Pareto-optimal solutions need to be found. For complex real-world simulation models the execution time usually is high so that the overall number of simulation runs that can be executed is limited. With this limited simulation time budget in many cases only an inferior Pareto-optimal front can be found. For many optimization problems however the DM has domain knowledge about which solutions are most interesting. After the optimization has been run only a single solution is chosen and implemented. If the DM can provide preference information that indicates where the DM expects to choose a solution the algorithm can exploit this information and guide the search towards this area. In this way only a small part of the Pareto-optimal front needs to be covered 978-1-4673-4781-5/12/$31.00 ©2012 IEEE

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“…Until now, the majority of the work done for the application of SD in manufacturing systems has focused mainly on performance analysis of the supply chain. This work has mainly considered how systems are designed to respond to unexpected customer demand, considering certain performance measures of the supply chain [34,[45][46][47]. Parnaby outlined a conceptualized SD model to manufacturing systems [48].…”

Section: System Dynamics and Its Application For Modelling And Analysmentioning

confidence: 99%

Towards a Generic Framework for the Performance Evaluation of Manufacturing Strategy: An Innovative Approach

Adane

Nicolescu

2018

JMMP

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To be competitive in a manufacturing environment by providing optimal performance in terms of cost-effectiveness and swiftness of system changes, there is a need for flexible production systems based on a well-defined strategy. Companies are steadily looking for methodology to evaluate, improve and update the performance of manufacturing systems for processing operations. Implementation of an adequate strategy for these systems' flexibility requires a deep understanding of the intricate interactions between the machining process parameters and the manufacturing system's operational parameters. This paper proposes a framework/generic model for one of the most common metal cutting operations-the boring process of an engine block machining system. A system dynamics modelling approach is presented for modelling the structure of machining system parameters of the boring process, key performance parameters and their intrinsic relationships. The model is based on a case study performed in a company manufacturing engine blocks for heavy vehicles. The approach could allow for performance evaluation of an engine block manufacturing system condition. The presented model enables a basis for other similar processes and industries producing discrete parts.

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Integrating system dynamics and multi-objective optimisation for manufacturing supply chain analysis

Cited by 6 publications

References 15 publications

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Reference point-based evolutionary multi-objective optimization for industrial systems simulation

Towards a Generic Framework for the Performance Evaluation of Manufacturing Strategy: An Innovative Approach

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