Volume V, Issue IV, April 2016 IJLTEMAS ISSN 2278 – 2540 www.ijltemas.in Page 86 Review Study to Minimize the Make Span Time for Job Shop Scheduling of Manufacturing Industry by Different Optimization Method Vineet Kumar 1 , Dr. Om Pal Singh 2 1 Research Scholar, PTU, Jalandhar 2 Professor, Mechanical Engineering Department. BCET, Gurdaspur Abstract: - Scheduling is one of the most important issues in the planning and operation of manufacturing system, and scheduling has gained much attention increasingly in the recent years. The flexible job shop scheduling problem (JSP) is one of the most difficult problems in this area. It consists of scheduling a set of jobs on a set of machines with the objective to minimize a certain make span time. Each machine is continuously available from time zero, processing one operation at a time without preemption. Each job has a specified processing order on the machine which are fixed and known in advance. Moreover, a processing time is also fixed and known. Different researcher use different algorithms to optimize the make span time. In this paper study has been focused on the different algorithms to optimize the make span time. Now a day’s different algorithms that are used are Genetic Algorithm, Artificial Neural Network, Ant Colony Optimization and Particle Swarm Optimization. Keywords: Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Job Shop Scheduling (JSS), Make Span Time. I. INTRODUCTION he job shop scheduling problem is to decide a schedule of jobs that is endowed with pre-set operation series in a multi-machine atmosphere. In the traditional job shop scheduling problem (JSP), n-jobs are processed to the finishing point on m-unrelated machines. For each and every task, technology limitations spell out an absolute and distinctive routing which is set and identified earlier. In addition, processing periods are set and identified previously. This synopsis deals with the situations in which the effectiveness measure (time, cost, distance, etc.) is a function of the order or schedule of performing a series of jobs (tasks). The selection of the appropriate order in which waiting customers may be served is called scheduling. Scheduling problems can be classified in two groups: 1. In the first group, there are n jobs to be performed, where each job requires processing on some or all of m different machines. The order in which these machines are to be used for processing each job as well as the expected or actual processing time of each job on each of the machines is known. We can also measure the effectiveness for any given schedule of jobs at each of the machines and we wish to select from the (n!) m theoretically feasible alternativeness measure(e.g., minimizes the total elapsed time from the start of the first job to the completion of the last job as well as idle time of machines). A technologically feasible sequence is one which satisfies the constraints (if any) on the order in which each job must be performed through the m machines. The technology of manufacturing processes renders many schedules technologically infeasible. For example, a part must be degreased before it is painted; similarly, a hole must be drilled before it is threaded. Although, theoretically, it is always possible to select the best schedule by testing each one; in practice, it is impossible because of the large number of computations involved. For example, if there are 4 jobs to be processed at each of the 5 machines (i.e., n=4 and m=5), the total number of theoretically possible different schedules will be (4!) 5 = 7,962,624. Of course, as already said, some of them may not be feasible because the required operations must be performed in a specified order. Obviously, any technique which helps us arrive at an optimal (or at least approximately so) schedule without trying all or most of the possibilities will be quite valuable. 2. The second group of problems deals with job shops having a number of machines and a list of tasks to be performed. Each time a task is completed by a machine, the next task to be started on it has got to be decided. Thus the list of tasks will change as fresh orders are received. Unfortunately, both types of problems are intrinsically difficult. While solutions are possible for some simple cases of the first type, only some empirical rules have been developed for the second type till now. In the scheduling problems, there are two or more customers to be served (or jobs to be done) and one or more facilities (machine) available for this purpose. We want to know when each job is to begin and what its due date is. We also want to know which facilities are required to be each job, in which T