A multiobjective grey wolf algorithm for stock cutting resource combination optimization under cloud manufacturing environment

Cloud manufacturing (CMfg) is a new intelligent manufacturing paradigm, and service composition and optimal selection (SCOS) is one of the core research areas. This paper combines the indicators of material utilization in the cutting industry to study the SCOS problem in the CMfg environment. A multi-objective optimization mathematical model with service cost, time and material utilization rate as indexes is introduced. An improved grey wolf algorithm for multi-objective problem is proposed for this model. We design an adaptive expansion hyper-grid and elite solution archive access strategy, which effectively improves the diversity of solution set and local search capabilities. Seven benchmark examples are tested, showing that this algorithm can obtain higher quality solutions than MOEA/D and SPEA2, which further demonstrates that the proposed algorithm is effective and superior in solving such problems.