A hybrid multi-objective AIS-based algorithm applied to simulation-based optimization of material handling system

• Published in: Applied soft computing Vol. 71; pp. 553 - 567
• Main Authors: Leung, Chris Siu Kei, Lau, Henry Ying Kei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2018
• Subjects: Artificial immune systems; Hybrid algorithm; Material handling system; Multi-objective optimization; Simulation-based optimization; Material handling system; Multi-objective optimization; Artificial immune systems; Simulation-based optimization; Hybrid algorithm
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.07.034

Material Handling System. [Display omitted] •A hybrid multi-objective optimization algorithm derived from immunological and biological evolution concepts is proposed.•An optimization approach integrating the proposed algorithm with an industrial-grade simulator is implemented.•The optimization approach’s performance is examined via numerical benchmark problems and a real-life simulation study.•The results reveal its ability of helping management to find near optimal system operating conditions and parameters. Optimization of complex real-world problems often involves multiple objectives to be considered simultaneously. These objectives are often non-commensurable and competing. For example, material handling is a vital element of industrial processes, which involves a variety of operations including the movement, storage and control of materials throughout the processes of manufacturing, distribution, and disposal while having to satisfy multiple objectives. Having an efficient and effective material handling system (MHS) is of great importance to various industries, such as manufacturing and logistics industries, for maintaining and facilitating a continuous flow of materials through the workplace and guaranteeing that required materials are available when needed. In this paper, a hybrid multi-objective optimization algorithm largely based on Artificial Immune Systems (AIS) is applied to simulation-based optimization of material handling system. This proposed algorithm hybridizes the AIS with the Genetic Algorithm (GA) by incorporating the crossover operator derived from the biological evolution. The reason behind such hybridization is to further enhance the diversity of the clone population and the convergence of the algorithm. In this paper, other than conducting numerical experiments to assess the performance of the proposed algorithm by using several benchmark problems, the proposed algorithm is also applied to optimize a multi-objective simulation-based problem on a material handling system in order to demonstrate the applicability of the proposed algorithm in real-life cases. The results show that for most cases the proposed algorithm outperforms the other benchmark algorithms especially in terms of solution diversity.