Two-objective robust job-shop scheduling with two problem-specific neighborhood structures

This paper discusses a two-objective robust job-shop scheduling problem with uncertain processing times described by discrete scenarios. The two objectives are to minimize the mean makespan and the worst-scenario makespan across all the scenarios. Two hybrid algorithms are developed by combining the elitist nondominated sorting genetic algorithm (NSGA-II) and tabu search (TS) operators. Two kinds of neighborhood structures are constructed based on the problem-specific knowledge for discrete scenarios and used in local TS operators, which are performed for each individual to improve offspring population. The hybrid algorithms with two specialized neighborhood structures are compared with each other, with the state-of-the-art existing algorithms for solving the discussed problem. The computational results verify the original intentions of designation of two neighborhood structures as well as the advantages of the developed hybrid algorithms for the proposed two-objective problem.

Automated summary

This paper discusses a two-objective robust job-shop scheduling problem with uncertain processing times described by discrete scenarios. Two hybrid algorithms are developed by combining NSGA-II and TS operators, with specialized neighborhood structures for discrete scenarios, which outperformed existing algorithms in computational results. The advantages of the developed hybrid algorithms for the proposed two-objective problem were verified through the comparison.