Solving the Flexible Job Shop Scheduling Problem Using a Hybrid Artificial Bee Colony Algorithm

Abstract

In this work, a hybrid artificial bee colony algorithm is proposed for solving the flexible job shop scheduling problem (FJSP) which is a classification of the classical job shop scheduling problem (JSP) considered to NP-hard in nature. In FJSP, an operation can be processed on a set of capable machines with different processing times, thereby dealing with a routing and sequencing problem. The objective considered is to minimize the makespan. The basic artificial bee colony (ABC) algorithm stresses on the balance between global exploration and local exploitation. However, the drawback of the basic ABC algorithm is that it converges prematurely and may get trapped in the local optima. Hence to improve its exploration capability in local space, it is hybridized using a Tabu search (TS) algorithm. At first, initial solutions are generated with certain quality and diversity as food sources using multiple strategies in combination. Crossover and mutation operations are carried out for machine assignment and operation sequencing separately generating new neighboring solutions. Lastly, a local search strategy based on TS is proposed to enhance the local search capability. Kacem’s and Brandimarte’s benchmark instances are used to compare the performance of the proposed approach to five other well-known algorithms in the literature. Experimental results revealed the superiority of the proposed approach in solving FJSP.