Optimal data pooling for shared learning in maintenance operations

We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider systems subject to Poisson input – the degradation or demand process – that are coupled through an unknown rate. Decision problems for these systems are high-dimensional Markov decision processes (MDPs) and are thus notoriously difficult to solve. We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. Leveraging this decomposition, we (i) show that pooling data can lead to significant cost reductions compared to not pooling, and (ii) prove that the optimal policy for the condition-based maintenance problem is a control limit policy, while for the spare parts management problem, it is an order-up-to level policy, both dependent on the pooled data

Optimal data pooling for shared learning in maintenance operations

We study optimal data pooling for shared learning in two common maintenance operations: condition-based maintenance and spare parts management. We consider systems subject to Poisson input – the degradation or demand process – that are coupled through an unknown rate. Decision problems for these systems are high-dimensional Markov decision processes (MDPs) and are thus notoriously difficult to solve. We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. Leveraging this decomposition, we (i) show that pooling data can lead to significant cost reductions compared to not pooling, and (ii) prove that the optimal policy for the condition-based maintenance problem is a control limit policy, while for the spare parts management problem, it is an order-up-to level policy, both dependent on the pooled data

Redundancy optimization for critical components in high-availability technical systems

We consider a user who buys a number of identical technical systems (e.g., medical, manufacturing, or communication systems) for which she must have very high availability. In such a situation, there are typically several options that the user can choose to facilitate this availability: cold standby redundancy for critical components, buying spare parts with the systems so failed parts can be replaced quickly, and/or application of an emergency procedure to expedite repairs when there is a stock out. To these options we introduce another: the possibility of initiating an emergency shipment when stock is one. Thus, the user may choose different combinations of the redundancy decision and the timing of applications of the emergency procedure, as well as how much spare parts inventory to purchase. We formulate the problem as the minimization of the total costs—acquisition, spare parts, and repair—incurred for the systems over their lifetimes, under a constraint for the total uptime of all systems. We optimally solve the problem by decomposing the multicomponent problem into single-component problems and then conducting exact analysis on these single-component problems. Using these, we construct an efficient frontier that reflects the trade-off between the uptime and the total costs of the systems. In addition, we provide a method to rank the components by the relative value of investing in redundancy. We illustrate these results through numerical example

Determining the set of the most important components for system reliability

Mere značajnosti (Importance measures) predstavlјaju načine merenja, tj. brojčanog iskazivanja značajnosti pojedinih komponenata u sistemu sa aspekta ukupne pouzdanosti sistema. Merama značajnosti je moguće odrediti (izdvojiti) komponente najznačajnije za pouzdanost sistema. Od šezdesetih godina, kada je koncept mera značajnosti prvi put uveden, do danas postoji neprekidno interesovanje za ovu oblast, tako da se, pored primene tradicionalnih mera značajnosti, neprestano uvode i definišu nove mere radi njihove primene na specifične sisteme. Opšti nedostatak mera značajnosti, nezavisno od kategorije kojoj pripadaju, je taj što se one utvrđuju za svaku pojedinačnu komponentu, a tek nakon toga se može izdvojiti skup najznačajnijih komponenata zadate kardinalnosti...Importance measures are numerical representations of the importance of each system’s component considering total system reliability. Using importance measures, the most important components for system reliability can be determined. Since the sixties, when the concept of importance measures was first introduced, there is a constant interest in this area, so that, in addition to the traditional importance measures, new measures for specific systems observed are continually introduced and defined. The general weak point of importance measures, irrespective of the category they belong to, is that they are determined for each individual component, and only afterwards a certain number of most important components can be set aside..

Optimization of systems reliability by metaheuristic approach

The application of metaheuristic approaches in addressing the reliability of systems through optimization is of greater interest to researchers and designers in recent years. Reliability optimization has become an essential part of the design and operation of largescale manufacturing systems. This thesis addresses the optimization of system-reliability for series–parallel systems to solve redundant, continuous, and combinatorial optimization problems in reliability engineering by using metaheuristic approaches (MAs). The problem is to select the best redundancy strategy, component, and redundancy level for each subsystem to maximize the system reliability under system-level constraints. This type of problem involves the selection of components with multiple choices and redundancy levels that yield the maximum benefits, and it is subject to the cost and weight constraints at the system level. These are very common and realistic problems faced in the conceptual design of numerous engineering systems. The development of efficient solutions to these problems is becoming progressively important because mechanical systems are becoming increasingly complex, while development plans are decreasing in size and reliability requirements are rapidly changing and becoming increasingly difficult to adhere to. An optimal design solution can be obtained very frequently and more quickly by using genetic algorithm redundancy allocation problems (GARAPs). In general, redundancy allocation problems (RAPs) are difficult to solve for real cases, especially in large-scale situations. In this study, the reliability optimization of a series–parallel by using a genetic algorithm (GA) and statistical analysis is considered. The approach discussed herein can be applied to address the challenges in system reliability that includes redundant numbers of carefully chosen modules, overall cost, and overall weight. Most related studies have focused only on the single-objective optimization of RAP. Multiobjective optimization has not yet attracted much attention. This research project examines the multiobjective situation by focusing on multiobjective formulation, which is useful in maximizing system reliability while simultaneously minimizing system cost and weight to solve the RAP. The present study applies a methodology for optimizing the reliability of a series–parallel system based on multiobjective optimization and multistate reliability by using a hybrid GA and a fuzzy function. The study aims to determine the strategy for selecting the degree of redundancy for every subsystem to exploit the general system reliability depending on the overall cost and weight limitations. In addition, the outcomes of the case study for optimizing the reliability of the series–parallel system are presented, and the relationships with previously investigated phenomena are presented to determine the performance of the GA under review. Furthermore, this study established a new metaheuristic-based technique for resolving multiobjective optimization challenges, such as the common reliability redundancy allocation problem. Additionally, a new simulation process was developed to generate practical tools for designing reliable series–parallel systems. Hence, metaheuristic methods were applied for solving such difficult and complex problems. In addition, metaheuristics provide a useful compromise between the amount of computation time required and the quality of the approximated solution space. The industrial challenges include the maximization of system reliability subject to limited system cost and weight, minimization of system weight subject to limited system cost and the system reliability requirements and increasing of quality components through optimization and system reliability. Furthermore, a real-life situation research on security control of a gas turbine in the overspeed state was explored in this study with the aim of verifying the proposed algorithm from the context of system optimization