Solving Factory Maintenance Problem: A Case Study of a Semi-Finished Food Product Manufacturing and Distribution Company in Uttaradit Province
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Abstract
This research focuses on scheduling maintenance for a semi-finished food product manufacturing and distribution company in Uttaradit Province. Heuristic algorithms, neural networks (NNs), and local search (LS) were used to create a mathematical scheduling model that solves the preventive maintenance (PM) problem. This is needed for production to keep going. The researchers tested the developed program with 23 factorial experiments to find the appropriate parameter values for the answer. The research collected data on both small and large problems, and the program was able to find the answer value for scheduling maintenance efficiently. It obtained a makespan value, which was close to and matched the lower bound, reflecting the efficiency of the neural network. The local search method was employed to solve the problem. In addition, the data collected before and after the research for 6 months found that the total cost decreased to 1,915,062 baht, down from the original 422,396 baht, or 9.93 percent. The mean time between machine failures (MTBF) increased to 83.84 hours or 35.27 percent, showing a decrease in costs in terms of time and maintenance.
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