A Cross Entropy-Genetic Algorithm for m-Machines No-Wait Job-ShopScheduling Problem

Abstract

No-wait job-shop scheduling (NWJSS) problem is one of the classicalscheduling problems that exist on many kinds of industry with no-waitconstraint, such as metal working, plastic, chemical, and food industries.Several methods have been proposed to solve this problem, both exact(i.e. integer programming) and metaheuristic methods. Cross entropy(CE), as a new metaheuristic, can be an alternative method to solveNWJSS problem. This method has been used in combinatorial optimization,as well as multi-external optimization and rare-event simulation.On these problems, CE implementation results an optimal value withless computational time in average. However, using original CE tosolve large scale NWJSS requires high computational time. Consideringthis shortcoming, this paper proposed a hybrid of cross entropy withgenetic algorithm (GA), called CEGA, on m-machines NWJSS. The resultsare compared with other metaheuritics: Genetic Algorithm-SimulatedAnnealing (GASA) and hybrid tabu search. The results showed thatCEGA providing better or at least equal makespans in comparison withthe other two methods.