Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes

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@Article{Liu:2019:APJOR,

• author = "Lingxuan Liu and Leyuan Shi",
• title = "Simulation Optimization on Complex Job Shop Scheduling with Non-Identical Job Sizes",
• journal = "Asia-Pacific Journal of Operational Research",
• year = "2019",
• volume = "36",
• number = "5",
• pages = "1950026--1950026--26",
• month = oct # " 3",
• keywords = "genetic algorithms, genetic programming, complex job shop scheduling, non-identical job sizes, stochastic simulation, nested partition",
• publisher = "World Scientific Publishing Co. and Operational Research Society of Singapore",
• bibsource = "OAI-PMH server at oai.repec.org",
• identifier = "RePEc:wsi:apjorx:v:36:y:2019:i:05:n:s021759591950026x",
• oai = "oai:RePEc:wsi:apjorx:v:36:y:2019:i:05:n:s021759591950026x",
• URL = "http://www.worldscientific.com/doi/abs/10.1142/S021759591950026X",
• DOI = "doi:10.1142/S021759591950026X",
• size = "26 pages",
• abstract = "This paper addresses the complex job shop scheduling problem with the consideration of non-identical job sizes. 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 optimisation 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.",

}

Genetic Programming entries for Lingxuan Liu Leyuan Shi

Citations