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Evaluation on a Simulated Annealing based Model for Solving Job Shop Scheduling Problem

The stochastic optimization techniques were commonly used in solving several optimization problems. There are different types of these algorithms that were addressed in several previous works. In our previous work, we implemented a GA and PSO based stochastic optimization model for solving the job shop scheduling problem (JSSP). The impacts of these two kinds of initial condition on the performance of these two algorithms were studied using the convergence curve and the achieved makespan. In this work we will implement a simulated annealing (SA) based model for solving JSSP. We will compare the performance of SA based method with the two other methods which we presented in our earlier works.