Optimization of Job Shop Scheduling Problemusing Tabu Search Optimization Technique

The Job shop scheduling (JSS) problem consists of „n‟ jobs and „m‟ operations on each of the jobs and it is hardest combinatorial optimization problems for which it is extremely difficult to find optimal solutions. Past two decades, much attention has been made to general heuristics such as Genetic algorithm, Ant Colony Optimization, Tabu Search and Simulated Annealing to solve this type of combinatorial optimization problems. In this paper we present how the adaptive search algorithms namely Tabu search is applied to solve Job shop scheduling (JSS) problem. The method uses dispatching rules to obtain an initial solution and searches for new solutions in a neighborhood based on the critical paths of the jobs. Several benchmark problems are tested using this algorithm for the best makespan and the obtained results are encouraging when compared with benchmark values.