Solving a New Multi-objective Unrelated Parallel Machines Scheduling Problem by Hybrid Teaching-learning Based Optimization

Abstract

This paper considers a scheduling problem of a set of independent jobs on unrelated parallel machines (UPMs) that minimizesthe maximum completion time (i.e., makespan or ), maximum earliness ( ), and maximum tardiness ( ) simultaneously. Jobs have non-identical due dates, sequence-dependent setup times and machine-dependentprocessing times. A multi-objective mixed-integer linear programming (MILP) is considered then solved with the ε-constraint method in small-sized problems.The related results are compared with the results obtained by meta-heuristic algorithms.Furthermore, an effectivehybrid multi-objective teaching–learningbased optimization (HMOTLBO) algorithm is proposed, whose performance is compared with a non-dominated sorting genetic algorithm (NSGA-II) fortest problems generated at random. The associated results show that the proposed HMOTLBO outperformsthe NSGA-II in terms of different metrics.