Two enhanced differential evolution algorithms for job shop scheduling problems

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan, sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. This paper proposes two new differential evolution algorithms (DE) for solving the job shop scheduling problem (JSP) that minimises two single objective functions: makespan and total weighted tardiness. The proposed algorithms aim to enhance the efficiency of the search by dynamically balancing exploration and exploitation ability in DE and avoiding the problem of premature convergence. The first algorithm allows DE population to simultaneously perform different mutation strategies in order to extract the strengths of various strategies and compensate for the weaknesses of each individual strategy to enhance the overall performance. The second algorithm allows the whole DE population to change the search behaviour whenever the solutions do not improve. This study also introduces a modified local mutation operation embedded in the two proposed DE algorithms to promote exploitation in different areas of the search space. In addition, a local search technique, called Critical Block (CB) neighbourhood, is applied to enhance the quality of solutions. The performances of the proposed algorithms are evaluated on a set of benchmark problems and compared with results obtained from an efficient existing Particle Swarm Optimisation (PSO) algorithm. The numerical results demonstrate that the proposed DE algorithms yield promising results while using shorter computing times and fewer numbers of function evaluations. 1. Introduction Scheduling is the problem of allocating limited resources to different tasks. It plays a vital role in most manufacturing industries because an effective schedule enables a manufacturer to utilise its resources effectively to achieve the desired production objectives. The job shop scheduling problem (JSP) is well known to be one of the most difficult of a class of problems and so itself has been the subject of many research efforts for several decades. The classical JSP attempts to schedule …