文章基本信息

标题：Hybrid genetic algorithm to solve a joint production maintenance model
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作者：H. Beheshti Fakher ; H. Beheshti Fakher ; M. Nourelfath 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：3
页码：747-754
DOI：10.1016/j.ifacol.2015.06.172
语种：English
出版社：Elsevier
摘要：Abstract This paper proposes a solution approach based on heuristic methods for a model integrating production planning and maintenance scheduling in imperfect production systems. Performance of population-based heuristics is considerably influenced by the population management tools that are generally aimed to keep the diversity and to force the search process to examine unvisited areas within the solution space. We use genetic algorithm hybridize with a standard tabu search as well as population management strategies to efficiently solve the complicated nonlinear model. Exploiting population data in order to define new solutions from unvisited sections of response space, learning from the population to extract the specifications of promising solutions and maintaining the population diversity are the most important particularities of the proposed approach. Comparing the algorithm with conventional implementation of GA and tabu search, demonstrates its surpassing performance in terms of solution time and quality.
关键词：KeywordsProduction planningPreventive maintenanceHeuristicsGenetic algorithmsManagement systems