An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan |
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| Authors: | Pedro Gómez-Gasquet Carlos Andrés Francisco-Cruz Lario |
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| Affiliation: | 1. Centro de Investigación de Gestión e Ingeniería de la Producción, Universitat Politècnica de València, Cno. de Vera s/n, Valencia 46022, Spain;2. Research Group in Reengineering, Operations Management, Group Work and Logistics Excellence, Universitat Politècnica de València, Cno. de Vera s/n, Valencia 46022, Spain;1. Department of Management Science and Engineering, Economics and Management School, Wuhan University, Wuhan, China;2. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong;1. Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran;2. Department of Industrial Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir, Turkey;1. School of Automation, Wuhan University of Technology, Wuhan 430070, Hubei Province, PR China;2. School of Economic and Management, Southwest Jiaotong University, Chengdu, Sichuan Province, PR China;1. Department of Statistics, Feng Chia University, Taichung, Taiwan, ROC;2. Department of Industrial and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan, ROC;1. Industrial Engineering Department, Yasar University, Bornova, Izmir, Turkey;2. College of Computer Science, Liaocheng University, PR China;3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore |
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| Abstract: | This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset. |
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