Optimization of class of multi-stage coupled integrated scheduling problem with process constraints
Multi-stage Coupled Integrated Scheduling Problem with Process Constraints(MCISP_PC)is prevalent in organizations with mixed-mode manufacturing.For the three stages of processing-transport-assembly,by combining with the multi-stage coupling characteristics,a Hybrid Estimation of Distribution Algorithm with Rule Heuristics(HEDA_RH)was proposed to solve MCISP_PC with the optimization objective of minimizing makespan.On the ba-sis of sufficiently exploring the hierarchies and time-series correlation of the problem coupling constraints,the cod-ing mode only for the processing stage was adopted,two rules were presented for each stages,and the better rule group was identified to complete decoding through the experiment.In HEDA_RH,by combining two block struc-ture characteristics,Homotype Gather Block(HGB)and Isomerism Gather Block(IGB)that were found in individ-uals,the probabilistic model updating mechanism and two sampling methods were designed from a global perspective to better guide the search direction and increase the solving efficiency.A matrix cubic learning model was designed from a local perspective,which adaptively selected to change the search depth and rule execution strategy for impro-ving the solution quality by accumulating quality information from six heuristics search operations.The effectiveness of HEDA_RH for solving MCISP_PC was verified with simulation comparison experiments.
rule heuristicshybrid estimation of distribution algorithmintegrated schedulingmulti-stage coupling
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