Journal of Industrial Engineering and Management Studies

Abstract This paper examines the joint optimization of production and maintenance planning for a single-machine deteriorating system. To achieve optimal performance and meet customer demand at the lowest cost, manufacturing companies need to carefully plan production and maintenance, considering various factors such as time, cost, output levels in any period and its impact on machine deterioration. In this research, we attempt to plan the production and maintenance process for a single-machine single-product system over multi-period. The machine has two operational states during production and gradually deteriorates as it ages. Maintenance operations restore the machine to healthy state and reduce the probability of producing defective products. We model the problem using the Markov decision process and employ the value iteration algorithm to determine the optimal policy, i.e., the best actions to take at each decision epoch. We evaluate the model's effectiveness by solving a numerical example and analyzing how changes in different parameters affect the results. The findings reveal the relationship between various parameters and the average cost rate. Changes in the mentioned rate due to changes in setup cost and the probability of producing conforming products are almost uniform without any drastic fluctuations. If the production cost of each item exceeds a certain threshold, the company's obligations are not enforceable.

Abstract Maintenance management along with production planning and control are two major components of production systems and operation management. In this paper, a production system that has two operational states plus a failure state is considered. In this system, maintenance actions are carried out based on the age-based policy which means the maintenance is performed after passing a specified time from the age of the system or after a system failure whichever occurs first. According to the random variables of the system failure, i.e., transition among the states of the system, two approaches are proposed to model the age-based policy considering different scenarios and their respective probabilities that may occur in the age-based policy. Both models aim to optimize the expected cost of the system per time unit, while the optimal time to terminate the production cycle and conduct preventive maintenance is determined as the main decision variable of the models. Some numerical examples employing different statistical distributions for the system failure mechanism, e.g., Weibull, gamma, normal, are also provided.

Abstract In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.