Simulation of sequencing rules in a five-similar-machine job shop

Abstract

Nowadays, simulation is essential when researching manufacturing process or designing production system. Line performance and equipment utilization are two major points of interest for every manufacturing company in order to increase competitiveness in the global market. The job shop scheduling is the allocation of a number of machines to perform a set of jobs. Job shop scheduling problem exists in most of the manufacturing systems in various form. Due to its high mix and low volume manufacturing environment, priority selecting among the jobs is challenging. This project is a real case study which involving a job shop with five similar CNC milling machines. A total of six jobs were performed and each of them consists of a different set of operation. The sequence of the six jobs to enter the system was determined by the sequencing rules including shortest setup time (SST), shortest processing time (SPT), shortest processing time + setup time (SPST), lowest volume (LV), least process (LP) and earliest due date (EDD). The setup time was taken into consideration to make the results more realistic. Due to the complexity of the model, WITNESS was used to simulate all the sequencing rules and the results are obtained. The best rules approach for the company in this study can be determined by comparing the results for each rule. By doing this, the company will be able to make faster and better decision about which job should be processed first instead of pick it randomly among the jobs. The results indicate that no single rule is excellent in all criteria. SPST rule was recommended to the company as it performed the best in terms of total completion time.