Designing An Integrated Scheduling Model for Production and Maintenance and Repairs in an Open Workshop Production System in Stable Conditions

Abstract

Manufacturing success depends on flexibility to adapt to demand and design changes. Open workshop production systems offer greater adaptability than traditional methods. While existing research connects production planning and maintenance, there is a gap in studying integrated scheduling for production, maintenance, and repair in open workshops. This study aims to develop a mathematical model for integrated scheduling to enhance scheduling accuracy, equipment reliability, and production efficiency. The study compares the efficiency of the MOKA and MOSA algorithms to solve 12 generated problems, evaluating them based on criteria such as NPS, CPU time, MID, MS, and SNS.  The mathematical model validation covered three stages of production: injection and mold making, assembly, and testing, involving three devices and seven personnel at each stage. The analysis emphasized the importance of accurate scheduling and maintenance planning to optimize production and reduce downtime. Heuristic optimization techniques were used to assess dependencies between key objectives. The ϵ-constraint method, sensitivity analysis, and Taguchi's method were applied to optimize the model.

Results highlighted the critical role of preparation time, revealing that longer preparation times lead to a 10% cost increase, while shorter preparation times reduce production costs by 28%. The optimization of algorithms like MOSA and MOKA was key to improving performance. The study found that MOKA is more effective for smaller to medium-sized problems, while MOSA performs better for larger problems. Future work may focus on developing hybrid models that combine the strengths of both algorithms or dynamic parameter tuning to improve performance across different problem scales.