Genetic Algorithm and Particle Swarm Optimization approaches to solve combinatorial job shop scheduling problems

In this paper an eminent approach based on the paradigms of evolutionary computation for solving job shop scheduling problem is proposed. The solution to the problem is alienated into three phases; planning, scheduling and optimization. Initially, fuzzy logic is applied for planning and then scheduling is optimized using evolutionary computing algorithms such as Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The well known Adams, Balas, and Zawack 10 × 10 instance (ABZ10) problem is selected as the experimental benchmark problem and simulated using MATLAB R2008b. The results of the optimization techniques are compared with the parameters like makespan, waiting time, completion time and elapse time. The performance evaluation of optimization techniques are analysed and the superior evolutionary technique for solving job shop scheduling problem is determined.

I. Introduction

Job Shop Scheduling Problem (JSSP) is a famous combinatorial optimization problem, which is used in complex equipment manufacturing system to validate the performance of heuristic algorithms. JSSP can be thought of as the allocation of resources over a specified time to perform a predetermined collection of tasks. A distinguished approach based on the paradigms of computational intelligence such as Genetic Algorithm and Particle Swarm Optimization for solving job shop scheduling problem is proposed to optimize the feasible schedule with minimum makespan.