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Supply Chain Design Using Simulation-Based NSGA-II Approach

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Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

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Abstract

This chapter addresses the design of supply chain networks including both network configuration and related operational decisions such as order splitting, transportation allocation and inventory control. The goal is to achieve the best compromise between cost and customer service level. An optimisation methodology that combines a multi-objective genetic algorithm (MOGA) and simulation is proposed to optimise not only the structure of the network but also its operation strategies and related control parameters. A flexible simulation framework is developed to enable the automatic simulation of the supply chain network with all possible configurations and all possible control strategies. To illustrate its effectiveness, the proposed methodology is applied to two real-life case studies from automotive industry and textile industries.

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Author information

Authors and Affiliations

  1. INRIA, COSTEAM Project, ISGMP Bat. A, Ile Du Saulcy, 57000, Metz, France

    Lyes Benyoucef

  2. ENSM.SE, 158 Cours Fauriel, 42023, Saint-Etienne Cedex 2, France

    Xiaolan Xie

Authors
  1. Lyes Benyoucef
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  2. Xiaolan Xie
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Correspondence to Lyes Benyoucef .

Editor information

Editors and Affiliations

  1. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Lihui Wang

  2. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Amos H. C. Ng

  3. , Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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Benyoucef, L., Xie, X. (2011). Supply Chain Design Using Simulation-Based NSGA-II Approach. In: Wang, L., Ng, A., Deb, K. (eds) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-0-85729-652-8_17

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  • DOI: https://doi.org/10.1007/978-0-85729-652-8_17

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-617-7

  • Online ISBN: 978-0-85729-652-8

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