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Combining system dynamics and multi-objective optimization with design space reduction
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. (Production and Automation Engineering)ORCID iD: 0000-0002-0880-2572
University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. (Simulation-Based Optimization)ORCID iD: 0000-0003-0111-1776
2016 (English)In: Industrial management + data systems, ISSN 0263-5577, E-ISSN 1758-5783, Vol. 116, no 2, 291-321 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2016. Vol. 116, no 2, 291-321 p.
Keyword [en]
Multi-objective optimization, System Dynamics, Supply Chain
National Category
Engineering and Technology
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-11989DOI: 10.1108/IMDS-05-2015-0215OAI: oai:DiVA.org:his-11989DiVA: diva2:908797
Available from: 2016-03-03 Created: 2016-02-29 Last updated: 2016-06-08Bibliographically approved

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Aslam, TehseenNg, Amos H. C.
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Citation style
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