Neural Network for Solving Job-Shop Scheduling Problem
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 6
Abstract
Abstract: The job shop scheduling is a very important scheduling problem, which is NP-complete in the strongsense and with well-known benchmark instances of relatively small size, which attest the practical difficulty insolving. Artificial neural network models have been successfully applied to solve such a job-shop schedulingproblem (JSSP) known as a Nonpolynomial (NP-complete) constraint satisfaction problem. Our maincontribution is an improvement of the algorithm proposed in the literature, which consists optimization of initialvalue of starting time. The main objective is to minimize the total weighted completion time of the jobs in thesystem that is the minimization of the makespan time by using the heuristic method. In this study, the heuristic method is used which gives a high quality approximate solution in reasonable time. The main advantage ofusing Hopfield Neural Network (HNN) is to improve the searching speed for getting an optimal or near optimalsolution of a deterministic JSSP for reducing the makespan time. The simulation of the proposed method hasbeen performed on various benchmarks. For two jobs and three machines (2/3/J/Cmax) dataset problem andany dataset problems, the simulation results shows the efficient with respect to the resolution speed, quality ofthe solution, and the reduction of the computation time which was not solved by Fanaiech et al. So, thesimulation results have revealed that proposed heuristic algorithm can find high quality solutions to large sizedinstances very quickly
Authors and Affiliations
Miss. Rukhsana G. Sache
Keywords
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