Integrated EPQ and periodic condition-based maintenance

In this paper, a stochastically deteriorating production system is studied under condition-based maintenance. Periodic monitoring is carried out to observe the degradation level of the system. If the degradation level exceeds failure threshold, nonconforming items are produced and a high corrective maintenance cost is incurred. Preventive maintenance actions are performed to reduce the possibility of failures. By considering inspection interval, preventive maintenance level and lot-size as decision variables, an integrated model is developed to minimize long-run average cost rate consisting of inspection costs, maintenance cost, cost of producing nonconforming items, inventory holding cost and setup costs. An illustrative example is presented to analyze the model

Joint optimization of dynamic lot-sizing and condition-based maintenance

This study investigates the dynamic lot-sizing problem integrated with Condition-based maintenance (CBM) for a stochastically deteriorating production system. The main difference of this work and the previous literature on the joint optimization of lot-sizing and CBM is the relaxation of the constant demand assumption. In addition, the influence of the lot-size quantity on the evolution of the equipment degradation is considered. To optimally integrate production and maintenance, a stochastic dynamic programming model is developed that optimizes the total expected production and maintenance cost including production setup cost, inventory holding cost, lost sales cost, preventive maintenance cost and corrective maintenance cost. The algorithm is run on a set of instances and the results show that the joint optimization model provides considerable cost savings compared to the separate optimization of lot-sizing and CBM

Kısmi gözlemlenebilen ortamdaki envanter problemi için dinamik ve doğrusal olmayan modeller.

In this study, a single-item periodic-review inventory system is considered in a partially observable environment with finite capacity, random yield and Markov modulated demand and supply processes for finite-horizon. The exact state of the real process, which determines the distribution of the demand and supply, is unobservable so the decisions must be made according to the limited observations called observed process. Partially Observable Markov Decision Process is used to model this problem. As an alternative to the dynamic programming model, a nonlinear programming model is developed to find optimal policies. The optimal policies of the nonlinear program is more practical to obtain and use compared to the dynamic programming model. Computational study is performed for the three data sets in order to compare the results of the two models. The results show that the optimal policies of the two models are the same.M.S. - Master of Scienc

Integrated production/inventory and condition-based maintenance control of a multi-item production system under stochastic demand

This paper studies the joint production/inventory and condition-based maintenance control for a multi-product manufacturing system with setup and maintenance times under stochastic product demands. The problem is modelled as a semi-Markov decision process (SMDP). The objective is to find a joint production and maintenance policy that minimizes the long run expected discounted cost including setup, holding, lost sales, preventive and corrective maintenance costs. A Q-learning method with state aggregation (QLA) is proposed to find near-optimal policies for large-scale problems that cannot be solved to optimality due to the curse of dimensionality. The numerical results show that QLA provides well-performing policies in a reasonable computational time

Integrated condition-based maintenance and lot-size planning

The lot-size problem has been studied frequently in literature. An efficient lot-size planning achieves a proper balance between setup costs, inventory holding costs, and lost sales costs. Nevertheless, a lot-size planning may turn out to be worthless when the machine that produces these products breaks down. A solution may be to add sensors to the machine, monitor the condition (degradation) of the machine, and take that condition into account in the planning problem. Although lot-size planning and condition-based maintenance have been studied by scientists, integrated lot-size and condition-based maintenance (CBM) planning is still a largely unexplored research topic. The few papers that consider this integration only consider lot-sizing in case of one product. Currently, however, machines have to produce various products due to the trend of shifting from mass production to mass customization. Therefore, we study the integrated lot-size and CBM planning problem in a multi-product setting. We develop a Markov Decision Process model where the state captures machine condition and inventory levels of the products. The objective is to obtain the optimal policy, that is the policy that minimizes the long-run discounted total cost. As the state space is exponential in the number of products, traditional dynamic programming methods are inadequate in computing the optimal policy. We, therefore, adopt reinforcement learning methods where experience is obtained through simulation

Integrated condition-based maintenance and multi-item lot-sizing with stochastic demand

This paper studies the problem of integrated lot-sizing and maintenance decision making in case of multiple products and stochastic demand. The problem is formulated as a Markov decision process, in which the goal is to find a joint production and maintenance policy that minimizes the long run expected total discounted cost. Therefore, the classic Q-learning algorithm is adopted, and a decomposition-based approximate Q-value heuristic is developed to obtain near-optimal solutions in a reasonable time. To accelerate the convergence of the Q-learning algorithm, a hybrid Q-learning method is proposed in which the Q-values are initiated by the output of the decomposition-based approximate Q-value heuristic. The numerical experiments reveal that the approximate Q-value heuristic is outperformed by the classic and hybrid Q-learning algorithms in terms of accuracy and that the hybrid Q-learning method converges much faster than the classic Q-learning method. However, these so-called tabular methods do not scale to larger problems with more than four products. Hence, based on the problem structure, three state aggregation schemes are developed and applied to the Q-learning algorithm to solve the large-scale problems. The numerical study demonstrates that Q-learning with the third state aggregation scheme performs nearly as good as the hybrid Q-learning method while significantly reducing the computational time and being scalable to large-scale problems