Condition-based production and maintenance decisions

Machines used in production facilities deteriorate as a result of load and stress caused by production, and thus they eventually require maintenance to stay in an operating condition. The deterioration rate of a machine typically depends on the production rate, implying that we can control its deterioration, and thereby the maintenance planning, by dynamically adjusting the production rate.The main contribution of this thesis is to integrate the two research fields of production planning and condition-based maintenance. We introduce and explore the benefits of condition-based production and maintenance policies that exploit the relation between production and deterioration to improve the trade-off between the opposing targets of having high production outputs and low maintenance costs.The obtained insights are not only interesting from a theoretical point of view, but are also practically relevant due to the ongoing developments in the fields of sensor equipment and the internet of things. These developments enable operators to remotely monitor the deterioration of equipment and to control its usage in real-time, thereby allowing to implement automated condition-based production policies

Joint Condition-based Maintenance and Condition-based Production Optimization

Developments in sensor equipment and the Internet of Things increasingly allow production facilities to be monitored and controlled remotely and in real-time. Organizations can exploit these opportunities to reduce costs by employing condition-based maintenance (CBM) policies. Another recently proposed option is to adopt condition-based production (CBP) policies that control the deterioration of equipment remotely and in real-time by dynamically adapting the production rate. This study compares their relative performance and introduces a fully dynamic condition-based maintenance and production (CBMP) policy that integrates both policies. Numerical results show that the cost-effectiveness of the policies strongly depends on system characteristics such as the planning time for maintenance, the cost of corrective maintenance, and the rate and volatility of the deterioration process. Integrating condition-based production decisions into a condition-based maintenance policy substantially reduces the failure risk, while fewer maintenance actions are performed. Interestingly, in some situations, the combination of condition-dependent production and maintenance even yields higher cost savings than the sum of their separate cost savings. Moreover, particularly condition-based production is able to cope with incorrect specifications of the deterioration process. Overall, there is much to be gained by making the production rate condition-dependent, also, and sometimes even more so, if maintenance is already condition-based. These insights provide managerial guidance in selecting CBM, CBP, or the fully flexible CBMP policy

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

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

Robust Condition-Based Operations & Maintenance: Synergizing Multi-Asset Degradation Rate Interactions and Operations-Induced Degradation

Effective operations and maintenance (O&M) in modern production systems hinges on careful orchestration of economic and degradation dependencies across a multitude of assets. While the economic dependencies are well studied, degradation dependencies and their impact on system operations remain an open challenge. To address this challenge, we model condition-based production and maintenance decisions for multi-asset systems with degradation interactions. There is a rich literature on condition-based O&M policies for single-asset systems. These models fail to represent modern systems composed of multiple interacting assets. We are providing the first O&M model to optimize O&M in multi-asset systems with embedded decision-dependent degradation interactions. We formulate robust optimization models that inherently capture degradation and failure risks by embedding degradation signals via a set of constraints, and building condition-based uncertainty sets to model probable degradation scenarios. We offer multiple reformulations and a solution algorithm to ensure computational scalability. Performance of the proposed O&M model is evaluated through extensive experiments, where the degradation is either emulated or taken from vibration-based readings from a rotating machinery system. The proposed model provides significant improvements in terms of operation, maintenance, and reliability metrics. Due to a myriad of dependencies across assets and decisions, it is often difficult to translate asset-level failure predictions to system-level O&M decisions. This challenge puts a significant barrier to the return on investment in condition monitoring and smart maintenance systems. Our approach offers a seamless integration of data-driven failure modeling and mathematical programming to bridge the gap across predictive and prescriptive models

Joint Condition-based Maintenance and Load-Sharing Optimization for Multi-unit Systems with Economic Dependency

Many production facilities consist of multiple units of equipment, such as pumps or turbines, that are jointly used to satisfy a given production target. Such systems often have overcapacity to ensure high levels of reliability and availability. The deterioration rates of the units typically depend on their production rates, implying that the operator can control deterioration by dynamically reallocating load among units. In this study, we examine the value of condition-based load-sharing decisions for multi-unit systems with economic dependency. We formulate the system as a Markov decision process and provide optimal joint condition-based maintenance and production policies. Our numerical results show that, dependent on the system characteristics, substantial cost savings of up to 40% can be realized compared to the optimal condition-based maintenance policy under equal load-sharing. The structure of the optimal policy particularly depends on the maintenance setup cost and the penalty that is incurred if the production target is not satisfied. For systems with high setup costs, the clustering of maintenance interventions is improved by synchronizing the deterioration of the units. On the contrary, for low setup costs, the deterioration levels are desynchronized and the maintenance interventions are alternated