Parallel machine scheduling with sequence dependent setup time for tire building process

Authors

  • Jakrawarn Kunadilok
  • Arada Chaiyacod

Keywords:

parallel machines, sequence-dependent setup time, mixed integer linear programming

Abstract

This research proposed a scheduling method for tire building machines with reducing total cost from labor cost and electrical cost as the goal. The production system in the tire building process consists of 32 parallel machines that responsible for producing 7,200 tires of 80 tire models per day, approximately. Each machine’s setup time when changing the models in production depends on the tire model that produced previously. Production scheduling in this process is a weekly planning activity for generating daily production schedules that meet all demands for each day in one week indiacated in the master production plan. Thus this scheduling problem is a parallel machine scheduling problem with sequence dependent setup time as processing restriction and minimizing total cost as the objective. A mixed integer linear programming model (MILP) was developed to solve this problem. A school bus routing problem formulation was used as the prototype model in developing the proposed MILP that included an overtime constraint and possible different initial jobs on each machine. The performance of the proposed MILP was evaluated by comparing with the real production schedules of 30 days generated by experience of the planner. The results revealed that the proposed MILP was able to find all feasible solutions. The total cost was decreased by 328,848 baht per month. The average number of machine used for production was reduced by 3.3 machines per day. The average setup time was reduced by 34.9 minutes per day. The average hour of production in overtime was reduced by 6.3 hours per day.

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Published

2021-12-21

How to Cite

[1]
J. Kunadilok and A. . Chaiyacod, “Parallel machine scheduling with sequence dependent setup time for tire building process”, TJOR, vol. 9, no. 2, pp. 104–116, Dec. 2021.