1186 KB0 KB201420262021Cheng, S. M.Liu, H. Y.Ma, Q. Y.Shi, J. L.Wang, N. D.Wang, Y. J.Zhang, X. C.Zhou, M. J.letnik:16številka:2str. 240-252DOI:10.14743/apem2021.2.397ISSN:1854-6250COBISSID_HOST:269955587URN:URN:NBN:SI:doc-HDL9FY3AenFakulteta za strojništvo, Inštitut za proizvodno strojništvoAdvances in production engineering and managementassemblydisassemblyimproved genetic algorithmizboljšani genetski algoritmiline balancingmontažamulti-objective optimizationNP-hard problemobnovapredelava izdelkaproduct life cycleproduct recoveryrazstavljanjeremanufacturinguravnoteženje linijvečciljna optimizacijaživljenjski cikel izdelkaOptimization of disassembly line balancing using an improved multi-objective genetic algorithm|Disassembly activities take place in various recovery operations including remanufacturing, recycling, and disposal. Product disassembly is an effective way to recycle waste products, and it is a necessary condition to make the product life cycle complete. According to the characteristics of the product disassembly line, based on minimizing the number of workstations and balancing the idle time in the station, the harmful index, the demand index, and the number of direction changes are proposed as new optimization objectives. So based on the analysis of the traditional genetic algorithm into the precocious phenomenon, this paper constructed the multi-objective relationship of the disassembly line balance problem. The disassembly line balance problem belongs to the NP-hard problem, and the intelligent optimization algorithm shows excellent performance in solving this problem. Considering the characteristics of the traditional method solving the multi-objective disassembly line balance problem that the solution result was single and could not meet many objectives of balance, a multi-objective improved genetic algorithm was proposed to solve the model. The algorithm speeds up the convergence speed of the algorithm. Based on the example of the basic disassembly task, by comparing with the existing single objective heuristic algorithm, the multi-objective improved genetic algorithm was verified to be effective and feasible, and it was applied to the actual disassembly example to obtain the balance optimization scheme. Two case studies are given: a disassembly process of the automobile engine and a disassembly of the computer componentsTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Mariboru, Fakulteta za strojništvo