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Journal of Optimization
Volume 2018, Article ID 5852469, 15 pages
Research Article

Multiobjective Simulation-Based Optimization Based on Artificial Immune Systems for a Distribution Center

Department of Industrial & Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong

Correspondence should be addressed to Chris S. K. Leung; moc.oohay@sirhcksl

Received 10 October 2017; Revised 27 March 2018; Accepted 19 April 2018; Published 21 May 2018

Academic Editor: Efren Mezura-Montes

Copyright © 2018 Chris S. K. Leung and Henry Y. K. Lau. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Competitive market factors, such as more stringent government regulations, larger number of competitors, and shorter product life cycle, in recent years have created more significant pressure on the management in all supply chain parties. To this end, the ability of analyzing and evaluating systems and related operations involving the deployment of complex multiobjective material handling systems is vital for distribution practitioners. In this respect, simulation modeling techniques together with optimization have emerged as a very useful tool to facilitate the effective analysis of these complex operations and systems. In this paper, we apply a multiobjective simulation-based optimization framework consisting of a hybrid immune-inspired algorithm named Suppression-controlled Multiobjective Immune Algorithm (SCMIA) and a simulation model for solving a real-life multiobjective optimization problem. The results show that the framework is able to solve large scale problems with a large number of parameters, operators, and equipment involved.