Integrated production and maintenance planning for a deteriorating system under uncertain demands

To deal with today's tough competitions, many companies have put investments into highly automated production systems with sophisticated machines. To achieve optimum performance and economic benefits, the production system is desired to operate at the maximum capacity. To keep the production process at a low cost and to satisfy customer demands, manufacturing companies have to lay appropriate production plans. In most existing studies, it is often assumed that the production process is perfect and no machine failure occurs during production planning horizon. This, however, is not the case in practice. During production many machines deteriorate due to aging or wearing, and eventually lead to failures. When a failure occurs, maintenance actions have to be performed, which decreases the capacity of the machine and disturb the initial production plan. Perturbation of production planning in an emergency situation is costly and leads to deterioration of the product quality and the service level. Therefore, it is vital to integrate the production planning and maintenance policy into a coherent strategy so as to hedge against the unexpected failures and production re-planning. In this paper, we aim to address the issue of jointly optimizing production and maintenance planning considering production capacity and service level constraint. Maintenance actions influence the production process in such a way that maintenance actions (either preventive or corrective) reduce the available production capacity in each period. Preventive maintenance is scheduled in advance and minimal repair is carried out at unexpected machine failures. We use the static-uncertainty strategy to determine the optimal cycle length of preventive maintenance and production quantity in each period, so as to minimize the expected total production and maintenance cost. Service level constraint is introduced to ensure that the customer demand in each period should be satisfied with a high probability. Copyright (C) 2020 The Authors.