A multi-strategy integration Pareto-based artificial colony algorithm for multi-objective flexible job shop scheduling problem with the earliness and tardiness criterion

Abstract

This paper studies the multi-objective flexible job shop scheduling problem with the earliness and tardiness (E&T) criterion, explores the decoding and search strategies of algorithms under the coexistence of the mean E&T and makespan, and provides a makespan-constrainted three-phase decoding mechanism and local search strategies for both of them. Referencing to the flexibility of the artificial bee colony algorithm framework, multiple strategies are integrated properly in the algorithm to realise simultaneous optimisation of regular and irregular objectives. Through testing six benchmark instances of different scales with tight or loose delivery time for jobs, the distribution characteristics of the Pareto optimal solution set of the collaborative optimisation of the mean E&T and the makespan are explored. The proper integration of various search strategies can make the proposed algorithm have better performance.