An improved NSGA-II with local search for multi-objective integrated production and inventory scheduling problem

In the context of collaborative manufacturing, integrated optimization of spare parts production and inventory management is practically important. This paper investigates an integrated production and inventory scheduling (IPIS) problem based on condition-based maintenance. In respect to this problem, whereby inventory and direct supply decisions are made simultaneously to achieve a better reduction in total inventory holding costs, total tardiness and total makespan, a multi-objective IPIS model is developed. An improved non-dominated sorting genetic algorithm-II with local search (INSGA-II_LS) is proposed for the multi-objective IPIS model. In INSGA-II_LS, the encoding and population initialization suited for IPIS are designed. The detailed presentation of operators of crossover, mutation, and local search that designed for the proposed IPIS problem then follows. The mathematical programming solver CPLEX and three multi-objective evolutionary algorithms called SPEA2, PESA-II, MOEA/D are designed for comparisons against INSGA-II_LS. Experimental results show the superiority of the proposed INSGA-II_LS for the IPIS problem with respect to various multi-objective performance metrics, especially for large-scale instances.