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By simultaneously considering practical constraints of sequence dependent setup times, incompatible job families and job dependent batch processing time, we formulate this problem into a simulation optimization problem based on the disjunctive graph representation. In order to find scheduling policies that minimise the expectation of mean weighted tardiness, we propose a genetic programming based hyper heuristic to generate efficient dispatching rules. And then, based on the nested partition framework together with the optimal computing budget allocation technique, a hybrid rule selection algorithm is proposed for searching machine group specified rule combinations. Numerical results show that the proposed algorithms outperform benchmark algorithms in both solution quality and robustness. <\/jats:p>","DOI":"10.1142\/s021759591950026x","type":"journal-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T05:47:48Z","timestamp":1564120068000},"page":"1950026","source":"Crossref","is-referenced-by-count":5,"title":["Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes"],"prefix":"10.1142","volume":"36","author":[{"given":"Lingxuan","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering & Management, Peking University, BJ 100871, P. R. 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