Condition-Based Production Planning:Adjusting Production Rates to Balance Output and Failure Risk

Problem Definition: Many production systems deteriorate over time as a result of load and stress caused by production. The deterioration rate of these systems typically depends on the production rate, implying that the equipment's deterioration rate can be controlled by adjusting the production rate. We introduce the use of condition monitoring to dynamically adjust the production rate to minimize maintenance costs and maximize production revenues. We study a single-unit system for which the next maintenance action is scheduled upfront. Academic/Practical Relevance: Condition-based maintenance decisions are frequently seen in the literature. However, in many real-life systems, maintenance planning has limited flexibility and cannot be done last minute. As an alternative, we are the first to propose using condition information to optimize the production rate, which is a more flexible short-term decision. Methodology: We derive structural optimality results from the analysis of deterministic deterioration processes. A Markov decision process formulation of the problem is used to obtain numerical results for stochastic deterioration processes. Results: The structure of the optimal policy strongly depends on the (convex or concave) relation between the production rate and the corresponding deterioration rate. Condition-based production rate decisions result in significant cost savings (by up to 50%), achieved by better balancing the failure risk and production output. For several systems a win-win scenario is observed, with both reduced failure risk and increased expected total production. Furthermore, condition-based production rates increase robustness and lead to more stable profits and production output. Managerial Implications: Using condition information to dynamically adjust production rates provides opportunities to improve the operational performance of systems with production-dependent deterioration

Condition-Based Production for Stochastically Deteriorating Systems: Optimal Policies and Learning

Production systems deteriorate stochastically due to usage and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This deterioration behavior is affected by the system's production rate. While producing at a higher rate generates more revenue, the system may also deteriorate faster. Production should thus be controlled dynamically to trade-off deterioration and revenue accumulation in between maintenance moments. We study systems for which the relation between production and deterioration is known and the same for each system as well as systems for which this relation differs from system to system and needs to be learned on-the-fly. The decision problem is to find the optimal production policy given planned maintenance moments (operational) and the optimal interval length between such maintenance moments (tactical). For systems with a known production-deterioration relation, we cast the operational decision problem as a continuous-time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies and we partially characterize the structure of the optimal interval length, thereby enabling efficient joint optimization of the operational and tactical decision problem. For systems that exhibit variability in their production-deterioration relations, we propose a Bayesian procedure to learn the unknown deterioration rate under any production policy. Our extensive numerical study indicates significant profit increases of our approaches compared to the state-of-the-art

Integrated planning of asset-use and dry-docking for a fleet of maritime assets

In maritime industry, moving assets (e.g., naval ships, dredgers, pilot vessels) are subject to obligatory inspections based on calendar time. These inspections consist of exhaustive operations that need the assets to be towed into specialized facilities referred to as dry-docks. In addition, there are maintenance operations needed as a result of usage-related deterioration of the assets, also requiring the assets to be dry-docked. In practice, a common approach for a fleet of assets is to synchronize these inspection and maintenance operations to avoid unnecessary dry-dockings. However, when and how these operations, some of which are calendar-based and some of which are usage-based, should be synchronized, and whether synchronizing them is always optimal remain as important questions. Since how an asset is used influences when it requires maintenance, answering these questions requires solving an integrated planning problem that combines the planning of asset-use and the planning of dry-docking. Operational constraints such as the locations of assets, limited dry-docking capacity, and the requirement to meet the demand for asset-use in each location make the problem even more challenging. This real-life problem is formulated as a mixed integer linear programming model which minimizes the total discounted cost for a finite time horizon and ensures the full satisfaction of the demand in every time period. The resulting optimal policy is compared with a sequential planning approach to quantify the economic benefit of integrated planning for asset-use and dry-docking. Additionally, two alternative planning approaches are presented for large problem instances. Results of the numerical analysis show that integrated planning can save up to 28.5% of the total cost