A memetic algorithm for a multistage capacitated lot-sizing problem |
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| Affiliation: | 1. School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan 2308, Australia;2. Faculdade de Engenharia Elétrica e de Computação, Universidade Estadual de Campinas, Campinas SP, CEP 13083-970, Brazil;1. Key Laboratory of Optoelectronic Technology and System of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China;2. Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang, Sichuan 621999, China;3. State Key Laboratory of Optoelectronic Materials and Technologies, School of Materials Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China;1. Laboratory of Host Defense, Center for Immunology and Inflammation, Feinstein Institute for Medical Research, Manhasset, New York;2. Division of Allergy and Immunology, Hofstra Northwell School of Medicine, Great Neck, New York;1. Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35294-0005, USA;2. Department of Medicine, University of California, San Diego, La Jolla, CA 92093-0676, USA;1. Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Northern Ireland, United Kingdom;2. Teagasc Food Research Centre, Ashtown, Dublin 15, Ireland |
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| Abstract: | We present a heuristic approach to solve a complex problem in production planning, the multistage lot-sizing problem with capacity constraints. It consists of determining the quantity to be produced in different periods in a planning horizon, such that an initially given demand forecast can be attained. We consider setup costs and setup times. Due the complexity to solve this problem, we developed methods based on evolutionary metaheuristics, more specifically a memetic algorithm. The proposed heuristics are evaluated using randomly generated instances and well-known examples in the literature. |
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