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Artificial Intelligence Techniques for Networked Manufacturing Enterprises ManagementMeta-heuristic Approaches for Multi-objective Simulation-based Optimization in Supply Chain Inventory Management

Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management:... [A supply chain (SC) is a complex network of facilities with dissimilar and conflicting objectives. Discrete-event simulation is often used to model and capture the dynamic interactions in SCs and to provide performance indicators. However, a simulator by itself is not an optimizer. Optimization algorithms can be coupled with a simulation module in order to find the most suitable SC policies. Nevertheless, because the simulation of an SC can take a considerable amount of time, the optimization tool must be well chosen. This chapter considers the hybridization of evolutionary algorithms, well known for their multi-objective capabilities, with an SC simulation module in order to determine the inventory policy (order-point or order-level) of a single product SC, taking into account two conflicting objectives: maximizing customer service level and minimizing total inventory cost. Four algorithms (SPEA-II, SPEA-IIb, MOPSO and NSGA-II) are evaluated on five different SC configurations to determine which algorithm gives the best results and makes the best use of the simulator. The results indicate that SPEA-2 favours a rapid convergence and that modifying its crossover or its archive truncation rule (variant SPEA-IIb) may improve the results even further.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Artificial Intelligence Techniques for Networked Manufacturing Enterprises ManagementMeta-heuristic Approaches for Multi-objective Simulation-based Optimization in Supply Chain Inventory Management

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/lp/springer-journals/artificial-intelligence-techniques-for-networked-manufacturing-wcYCH21g0C
Publisher
Springer London
Copyright
© Springer-Verlag London 2010
ISBN
978-1-84996-118-9
Pages
249 –269
DOI
10.1007/978-1-84996-119-6_9
Publisher site
See Chapter on Publisher Site

Abstract

[A supply chain (SC) is a complex network of facilities with dissimilar and conflicting objectives. Discrete-event simulation is often used to model and capture the dynamic interactions in SCs and to provide performance indicators. However, a simulator by itself is not an optimizer. Optimization algorithms can be coupled with a simulation module in order to find the most suitable SC policies. Nevertheless, because the simulation of an SC can take a considerable amount of time, the optimization tool must be well chosen. This chapter considers the hybridization of evolutionary algorithms, well known for their multi-objective capabilities, with an SC simulation module in order to determine the inventory policy (order-point or order-level) of a single product SC, taking into account two conflicting objectives: maximizing customer service level and minimizing total inventory cost. Four algorithms (SPEA-II, SPEA-IIb, MOPSO and NSGA-II) are evaluated on five different SC configurations to determine which algorithm gives the best results and makes the best use of the simulator. The results indicate that SPEA-2 favours a rapid convergence and that modifying its crossover or its archive truncation rule (variant SPEA-IIb) may improve the results even further.]

Published: Jan 1, 2010

Keywords: Supply Chain; Multiobjective Optimization; Inventory Policy; Strength Pareto Evolutionary Algorithm; Infinitesimal Perturbation Analysis

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