Planning, Scheduling and Optimizing Job Shop Scheduling Problem Using Genetic Algorithm

Evolutionary algorithms are having a leading focus in solving several optimization problems. Job-shop scheduling problem (JSSP) is one among the common NP-hard combinatorial optimization problems used to allocate machines for a set of jobs over time and hence optimizing the processing time, waiting time, completion time, and makespan. In this paper an eminent approach based on the paradigm 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, the jobs are scheduled, in which the machines and jobs with respect to levels are planned. Scheduling is optimized using Genetic Algorithm (GA), which is a powerful search technique, built on a model of the biological evolution. Like natural evolution GA deal with a population of individuals rather than a single solution and fuzzy interface is applied for planning and scheduling of jobs. The Fisher and Thompson 10×10 instance (FT10) problem is selected as the experiment problem and the algorithm is simulated using the MATLAB R2008B software.