Hybrid differential evolution integrated with probability learning for green distributed reentrant job shop scheduling
Aiming at the green distributed reentrant job shop scheduling problem(GDRJSSP),a hybrid differential evolution incorporated with probabilistic learning(HDE_PL)is proposed to minimize the maximum completion time and the total energy consumption.According to the problem characteristics of the GDRJSSP,the rule of job allocation among factories and the encoding and decoding rules are designed,and the differential evolution algorithm is used to perform global search to find high-quality solution regions.In order to guide the global search direction more clearly,a multi-dimensional probability model based on Bayesian network structure is designed to reasonably learn and accumulate the pattern information of high-quality solutions(i.e.,the better solutions in the current population).Combined with the structural characteristics of the problem solution,four neighborhoods based on the critical path are proposed to construct the local search,and an energy saving strategy based on the non-critical path is devised to enhance the ability of the algorithm to obtain low-power non-dominated solutions.Simulation experiments and algorithm comparisons verify that HDE_PL can effectively solve the GDRJSSP.
differential evolutiongreen schedulingdistributed schedulingreentrant job shop scheduling problem
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