TY - GEN
T1 - A tabu search-based memetic algorithm for the multi-objective flexible job shop scheduling problem
AU - Kefalas, Marios
AU - Limmer, Steffen
AU - Apostolidis, Asteris
AU - Olhofer, Markus
AU - Emmerich, Michael T M
AU - Bäck, Thomas H W
N1 - Funding Information:
This work is part of the research programme Smart Industry SI2016 with project name CIMPLO and project number 15465, which is partly financed by the Netherlands Organisation for Scientific Research (NWO).
Publisher Copyright:
© 2019 Association for Computing Machinery.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/7
Y1 - 2019/7
N2 - In this paper we propose a tabu search-based memetic algorithm (TSM) for the multi-objective flexible job shop scheduling problem (FJSSP), with the objectives to minimize the makespan, the total workload and the critical workload. The problem is addressed in a Pareto manner, which targets a set of Pareto optimal solutions. The novelty of our method lies in the use of tabu search (TS) as the local search method as well as a mutation operator and the use of the hypervolume indicator to avoid stagnation by increasing the flow of individuals in the local search. To the best of our knowledge, the use of TS in the context of multi-objective FJSSP has not been reported so far. We apply our algorithm on well known test instances and compare our results to state-of-the art algorithms. The results show that our approach yields competitive solutions in 6 of the 10 instances against two of their algorithms proving that the use of TS as a local search method can provide competitive results.
AB - In this paper we propose a tabu search-based memetic algorithm (TSM) for the multi-objective flexible job shop scheduling problem (FJSSP), with the objectives to minimize the makespan, the total workload and the critical workload. The problem is addressed in a Pareto manner, which targets a set of Pareto optimal solutions. The novelty of our method lies in the use of tabu search (TS) as the local search method as well as a mutation operator and the use of the hypervolume indicator to avoid stagnation by increasing the flow of individuals in the local search. To the best of our knowledge, the use of TS in the context of multi-objective FJSSP has not been reported so far. We apply our algorithm on well known test instances and compare our results to state-of-the art algorithms. The results show that our approach yields competitive solutions in 6 of the 10 instances against two of their algorithms proving that the use of TS as a local search method can provide competitive results.
KW - Flexible job shop
KW - Genetic algorithms
KW - Memetic
KW - Multi-objective optimization
KW - Scheduling
KW - Tabu search
UR - http://www.scopus.com/inward/record.url?scp=85070649681&partnerID=8YFLogxK
U2 - 10.1145/3319619.3326817
DO - 10.1145/3319619.3326817
M3 - Conference contribution
AN - SCOPUS:85070649681
T3 - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
SP - 1254
EP - 1262
BT - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Y2 - 13 July 2019 through 17 July 2019
ER -