Article Published In IJMSER

Optimization of Job Scheduling in Flow Shop Environment using Genetic Algorithm Considering Sequence Dependent Setup Times

Author : Rajdeep Singh, Upender Dhull, Sanjeev Sharma and Sandeep Jindal

Download PDF



Among typical production scheduling problems, job shop scheduling is one of the strong NP-complete combinatorial optimization problems. Using an enhanced genetic algorithm, an effective crossover operation for real coded job-based representation can be used to guarantee the feasibility of solutions, which are decoded into active schedules during the search process. This paper attempts to assign an optimum job sequence on a machine considering the objective of minimizing total makespan time in a flow shop environment based on the philosophy of Genetic Algorithms. The setup time or changeover time is taken as the main factor involved in the makespan time where setups are sequence dependent. For analyzing the results simulations are performed with various population sizes and crossover probabilities.

Keywords: Job-Shop Scheduling, Flow-Shop Scheduling, Scheduling Strategies, Genetic Algorithm ps.