Joint Optimization of Production and Maintenance Using Monte Carlo Method and Metaheuristic Algorithms

Abstract

In the competitive business environment, manufacturers are seeking strategies to improve the product quality and the system reliability while reducing the costs. This paper addresses the problem of finding the optimal production and maintenance schedules for a deteriorating manufacturing system with the objective of minimizing the expected cost per unit time. The system consists of one machine which deteriorates with time and it may shift from an in-control state to an out-of-control state with a larger proportion of imperfect products. In addition, the hedging point policy is applied as the production-inventory control policy. The predictive maintenance is performed based on process inspections, whose sampling intervals are variable. To deal with the proposed problem, we build a joint model that coordinates production, inventory, maintenance, and quality control with 16 scenarios. Then we propose a novel approach speeding up the Monte Carlo simulation to calculate the objective function. Thus it becomes feasible to optimize the objective function by metaheuristic algorithms. Then we use the genetic algorithm to illustrate its feasibility. Next, the advantage of the proposed approach is verified by comparing with the traditional integral method. Finally, a sensitivity analysis with an orthogonal experiment is conducted to help managers find the factors with the most significant effect on the cost.