An effective teaching-learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time

In this paper, an effective teaching-learning-based optimization algorithm (TLBO) is proposed to solve the flexible job-shop problem with fuzzy processing time (FJSPF). First, a special encoding scheme is used to represent solutions, and a decoding method is employed to transfer a solution to a feasible schedule in the fuzzy sense. Second, a bi-phase crossover scheme based on the teaching-learning mechanism and special local search operators are incorporated into the search framework of the TLBO to balance the exploration and exploitation capabilities. Moreover, the influence of the key parameters on the TLBO is investigated using the Taguchi method. Finally, numerical results based on some benchmark instances and the comparisons with some existing algorithms are provided. The comparative results demonstrate the effectiveness and efficiency of the proposed TLBO algorithm in solving the FJSPF.