Abstract
The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the proposed algorithm has a powerful search capability in solving the flexible job shop scheduling problem compared with other heuristic algorithms, such as the genetic algorithm, tabu search and ant colony optimization. Moreover, the results also show that the improved strategies could improve the performance of the algorithm effectively.
Highlights
The scheduling of operations has a vital role in the planning and managing of manufacturing processes
This paper presents an improved shuffled frog-leaping algorithm (SFLA) with some strategies for flexible job-shop scheduling problem (FJSP)
The objective of the research is to minimize the makespan in FJSP
Summary
The scheduling of operations has a vital role in the planning and managing of manufacturing processes. The flexible job-shop scheduling problem (FJSP) is an extension of the conventional JSP, in which operations are allowed to be processed on any one of the existed machines. Fattahi et al [11] proposed a mathematical model for the flexible job shop scheduling problem, and two heuristic approaches (tabu search and simulated annealing heuristics) are introduced to solve the real size problems. Gao et al [12] developed a hybrid genetic algorithm to solve the flexible job shop scheduling problem. Yao et al [2] presented an improved ant colony optimization to solve FJSP, and an adaptive parameter, crossover operation and pheromone updating strategy are used to improve the performance of the algorithm.
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