Abstract
This paper describes the implementation of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm to minimize the total completion time of jobs called makespan in parallel line Job Shop Scheduling Problem (PJSSP). PJSSP is one of the scheduling methods in manufacturing industry for increasing plant utilization, reducing cycle time and to find the optimal allocation of jobs in multiple processing lines. Each job is assigned to a particular line and the job should be completed only in that assigned line. Also, the job should be processed in a particular order. PJSSP is always a challenging task in the combinatorial research and it requires a heuristic approach. Results show that the performance of PSO is superior to GA in order to find optimal solution with minimum makespan for PJSSP.
Highlights
In the manufacturing system, scheduling and planning a production order plays an important role.The scheduling and planning problems are usually combinatorial
Genetic algorithm is applicable to real world problems, if they are suitably encoded
The results reveals that the Particle Swarm
Summary
In the manufacturing system, scheduling and planning a production order plays an important role. The scheduling and planning problems are usually combinatorial. Scheduling problems are defined as the process of assigning a set of jobs to resources over a period of time (Zhang et al, 2013). In recent years, scheduling problems exist in real world industrial situations. Performance criteria such as machine utilization, manufacturing lead times, processing time, inventory cost and meeting due date, customer satisfaction and quality of products are dependent on the efficient way the jobs are scheduled (Tang and Dai, 2015). It becomes increasingly important to develop an effective job shop scheduling approach. The strategies and parameters of tabu search for job-shop scheduling.
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