A Hybrid Differential Evolution Algorithm for Stochastic Flow Shop Scheduling with Limited Buffers

Abstract

A hybrid differential evolution(DE) algorithm,namely OHTDE,is proposed to solve the stochastic flow shop scheduling problem(FSSP) with limited buffers between consecutive machines.Two different criteria are considered.The first is to minimize a general earliness/tardiness cost function and the second is to minimize the total completion time.The proposed algorithm is a hybrid of DE and two techniques:the optimal computing budget allocation(OCBA) technique and hypothesis test(HT) .DE is used to execute both global and local search.OCBA is utilized to reasonably allocate a limited sampling budget for every solution,assigning more for better individuals to improve the confidence level of getting good solutions under noise environment.HT is adopted to perform a statistical comparison on solutions performances so that the repeated search on the similar region of the solution space can be avoided to some extent.Moreover,a special crossover,which is performed on some good solutions identified by OCBA and HT,is designed to enhance the capability of local search.Furthermore,the stochastic convergence property of OHTDE is analyzed by using the theory of finite Markov chain.Experimental results and comparison show the effectiveness and robustness of the proposed OHTDE.