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Since multi-stage supply chain networks have been recognized as NP-hard problems, applying priority-based encoding and a four-step backward decoding procedure, a meta-heuristic algorithm, namely GAIWO, based on the best features of genetic algorithm (GA) and invasive weed optimization (IWO) is designed to solve the problem. In small size problems, the efficiency of the GAIWO is checked by solutions of GAMS software. For larger size problems, the performance of the proposed approach is compared with four evolutionary algorithms in both aspects of the structure of the GAIWO and the efficiency of the proposed encoding\u2013decoding procedure. Besides usual evaluation criteria, Wilcoxon test and a chess rating system are used for evaluating and ranking the algorithms. The results show higher efficiency of the proposed approach. <\/jats:p>","DOI":"10.1142\/s0217595917500087","type":"journal-article","created":{"date-parts":[[2017,3,17]],"date-time":"2017-03-17T08:02:30Z","timestamp":1489737750000},"page":"1750008","source":"Crossref","is-referenced-by-count":16,"title":["Hybrid Genetic Algorithm and Invasive Weed Optimization via Priority Based Encoding for Location-Allocation Decisions in a Three-Stage Supply Chain"],"prefix":"10.1142","volume":"34","author":[{"given":"Mohammad Saeid","family":"Atabaki","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering Faculty of Engineering, Kharazmi University, Mofatteh Ave, Tehran, 1571914911, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering Faculty of Engineering, Kharazmi University, Mofatteh Ave, Tehran, 1571914911, 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