A Hybrid Ant Colony Optimization and Simulated Annealing Algorithm for Multi-Objective Scheduling of Cellular Manufacturing Systems

Aidin Delgoshaei and Ahad Ali
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Aidin Delgoshaei: University Putra Malaysia, Seri Kembangan, Malaysia
Ahad Ali: Lawrence Technological University, Southfield, USA

International Journal of Applied Metaheuristic Computing (IJAMC), 2020, vol. 11, issue 3, 1-40

Abstract: During the last 2 decades, there have been many manufacturing companies in various industries that used the advantages of cellular manufacturing layouts. However, determining the best schedule for cellular layouts considering uncertain product demands is a big concern for scientists. In this research, a multi-objective decision-making model is proposed in the process of dynamic cellular production planning where the market demands are uncertain. In this regard, a non-linear mixed integer programming model is developed. The complexity of the model is high to consider the model as NP-hard. Therefore, a hybrid Ant colony Optimization and Simulated Annealing Algorithms are proposed to solve the problem. Then, the Taguchi method is used to estimate appropriate sets of parameters of the proposed algorithm. The results demonstrated that the proposed algorithm can generate the best part-routes of products in terms of time, cost and load variance in a reasonable time. The algorithm is then used for a cellular production plant which is the producer of heavy vehicles parts.

Date: 2020
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