Local Search Methods to Solve Multiple Objective Function


In this paper we considered the problem of scheduling n jobs on a single machine. Ouraim in this study is to find the near optimal solution to minimize the cost of total flow timeand maximum earliness with unequal ready times.Different local search methods: (Descent Method, Adjacent Pairwise InterchangeMethod, Simulated Annealing, Genetic Algorithm) are developed, compared, and tested forthe problem. We investigate the influence of the parameters variance for these local searchmethods, and empirically analyze their starting solutions. Computational experience foundthat these local search algorithms can solve the problem up to (23000) jobs with reasonabletime. Also we found that: the Genetic algorithm is the best local search heuristic algorithmfor our problem when the size is less than or equal to (1500) jobs, and for problems of largesize the Simulated Annealing was recommended.