Optimal Job Shop Scheduling Performance Enhancement Through Computer Based Simulated Annealing Technique

Timely and cost effective production is becoming increasingly important in today's global competitive market. The key problem faced by today's manufacturing industries is feasible allocation of various jobs to available resources i.e., machines. This problem can be outwitted by a proper scheduling technique, adopted by industries. A typical job shop scheduling problem has a set of jobs to be processed in a set of machines, with certain constraints and objective functions to be achieved. The most commonly considered objectives are the minimization of make span (Cmax), minimization of tardiness (T) which leads to minimization of late penalty cost, and to maximize machine utilization. Machine shop scheduling can be done using various techniques like standard dispatching rules, heuristic techniques like Simulated annealing, Tabu Search, Genetic algorithm, etc,. In this article a typical job shop scheduling problem is solved using algorithmic simulated annealing (SA) technique, a heuristic search algorithm. SA is a generic neighborhood search algorithm used to locate optimal solution very nearer to a global optimal solution. As a part of this article, a software based program is developed in VB platform and realistic test instances were performed. Experimental results obtained were further tuned by varying input job parameters and optimal results were obtained.