Multi-objective flexible job shop scheduling can be divided into two sub-problems
namely job assignment and sorting
which are often multi-objective optimization problems. Aiming at this situation
this paper presents a layered Pareto optimization frame for multi-objective flexible job shop problem and proposes a two-stage hybrid Pareto ant colony algorithm for multi-objective operation assignment(OA)and operation sequencing(OS)sub-problems. Embedding multiple scheduling rules in GT algorithm is used to evaluate and filter the assignment solutions. The global optimal non-dominated front of the original problem is obtained by scheduling optimization as the elite archive of assignments. Each Pareto ant colony algorithm is combined with the neighborhood search strategies related to different objectives. The co-evolutionary can obtain high-quality solutions to multi-objective FJSP. Finally
by solving four benchmark instances considering minimizing the mean weighted tardiness time
the effectiveness of the method is testified. The simulation results show that the layered Pareto optimization frame helps to reduce complexity of the problem
and compared with other literatures
the proposed algorithm can obtain the Pareto non-dominant solutions of each instance
providing a new way for solving complex multi-objective scheduling problems.
ZHANG Chaoyong, DONG Xing, WANG Xiaojuan, et al. Improved NSGA-II for the multi-objective flexible job-shop scheduling problem [J]. Journal of Mechanical Engineering, 2010, 46(11): 156-164.
MOSLEHI G, MAHNAM M. A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search [J]. International Journal of Production Economics, 2011, 129(1): 14-22.
GAO K Z, SUGANTHAN P N, PAN Q K. Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling [J]. Information Sciences, 2014, 289(1): 76-79.
LI Junqing, PAN Q, LIANG Y C. An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems [J]. Computers Industrial Engineering, 2010, 59(4): 647-662.
CHIANG T C, LIN H J. A simple and effective evolutionary algorithm for multi-objective flexible job shop scheduling [J]. International Journal of Production Economics, 2013, 141(1): 87-98.
GARCIA-MARTINEZ C, CORDON O, HERRERA F. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP [J]. European Journal of Operational Research, 2007, 180(1): 116-148.
DOERNER K, GUTJAHR W J. Pareto ant colony optimization: a meta-heuristic approach to multi-objective portfolio selection [J]. Annals of Operations Research, 2004, 131(1): 79-99.