Design of a Fuzzy Multi-Objective Mathematical Model for Short-Term Preventive Maintenance Scheduling Using a Total Productive Maintenance Approach - Rajabpour, Foroud; Zeinalnezhad, Masoomeh; Hajipour, Vahid (Management Strategies and Engineering Sciences, 2027)
This study aimed to design a fuzzy multi-objective mathematical model for short-term preventive maintenance scheduling based on the Total Productive Maintenance approach to optimize production completion time, total cost, greenhouse gas emissions, and product quality under uncertainty. This applied quantitative study was conducted using mathematical modeling and computational optimization. The proposed model integrated production scheduling and preventive maintenance decisions within a short-term planning horizon and formulated four objective functions: minimization of completion time, total cost, and greenhouse gas emissions, and maximization of product quality. Uncertain parameters were modeled using a fuzzy approach to increase the realism of the scheduling environment. Five small-scale numerical instances with increasing complexity were generated and solved using GAMS as an exact optimization benchmark. The NSGA-II metaheuristic algorithm was implemented in MATLAB to solve the same instances and evaluate the model’s computational efficiency, accuracy, and stability. The inferential comparison between GAMS and NSGA-II indicated that the proposed algorithm generated near-optimal solutions across all objective functions. For the quality objective, deviations ranged from 0.39% to 2.78%, and for completion time, deviations ranged from 0.97% to 2.87%. For the cost objective, deviations ranged from 0.57% to 2.65%, while greenhouse gas emission deviations ranged from 0.36% to 2.93%. Repeated executions of the algorithm showed low dispersion between best, worst, and mean solutions, confirming acceptable convergence and stability. The best–worst deviation for the cost objective reached 3.02% only in the largest instance, while the quality objective remained below 1% across all instances. The proposed fuzzy multi-objective model provides an effective decision-support framework for short-term preventive maintenance scheduling in TPM-based production systems. The results confirm that NSGA-II can generate accurate, stable, and computationally efficient solutions close to exact optimization results while balancing economic, environmental, operational, and quality-related objectives.