A multi-echelon inventory model for a low demand repairable item

Bibliography: p. 24.Stephen C. Graves

Regulatory Tailoring, Reliability, and Price Volatility with Stochastic Breakdowns

Although real-world energy supply systems are subject to stochastic failures, the impacts of proposed regulations affecting these systems have typically been evaluated using non-stochastic models. This paper develops an energy market model that explicitly allows for stochastic failures and demonstrates they play an important, or even dominant, role in determining the market impacts of environmental regulations that tailor product specifications to address local or regional conditions, such as fuel-formulation requirements specific to certain regional markets within the United States. While traditional non-stochastic analyses view the tailoring of regulatory requirements by location as an efficiency-enhancing alternative to a "one size fits all" regulatory approach, they fail to consider the adverse impact on reliability in all market segments resulting from the loss of product fungibility due to tailoring. We show that regulatory impact estimates developed without explicit consideration of reliability considerations may be highly inaccurate.reliability, boutique fuels, gasoline price spikes, stochastic failures, environmental regulation, tailored regulation

Development of Availability and Sustainability Spares Optimization Models for Aircraft Reparables

The Republic of Singapore Air Force (RSAF) conducts Logistics Support Analysis (LSA) studies in various engineering and logistics efforts on the myriad of weapon systems. In these studies, inventory spares provisioning, availability and sustainability analyses are key focus areas to ensure asset sustenance. In particular, OPUS10, a commercial-off-the-shelf software, is extensively used to conduct reparable spares optimization in acquisition programs. However, it is limited in its ability to conduct availability and sustainability analyses of time-varying operational demands, crucial in Operations & Support (O&S) and contingency planning. As the RSAF seeks force structure expansion to include more sophisticated weapon systems, the operating environment will become more complex. Agile and responsive logistics solutions are needed to ensure the RSAF engineering community consistently pushes for deepening competencies, particularly in LSA capabilities. This research is aimed at the development of a model solution that combines optimization and sustainability capabilities to meet the dynamic requirements in O&S and contingency planning. In particular, a unique dynamic operational profile conversion model was developed to realize these capabilities. It is envisaged that the research would afford the ease of use, versatility, speed and accuracy required in LSA studies, to provide the necessary edge in inventory reparable spares modeling

Efficient heuristics for two-echelon spare parts inventory systems with an aggregate mean waiting time constraint per local warehouse

This paper presents solution procedures for determining close-to-optimal stocking policies in a multi-item two-echelon spare parts inventory system. The system we consider consists of a central warehouse and a number of local warehouses, and there is a target for the aggregate mean waiting time per local warehouse. We develop four different heuristics and derive a lower bound on the optimal total cost. The effectiveness of each heuristic is assessed by measuring the relative gap between the heuristic’s total cost and the lower bound. The results of the computational experiments show that a greedy procedure performs most satisfactorily. It is accurate as indicated by relatively small gaps, easy to implement, and furthermore, the computational requirements are limited. The computational efficiency can be increased by using Graves’ approximate evaluation method instead of an exact evaluation method, while the results remain accurate

Approximate Order-up-to Policies for Inventory Systems with Binomial Yield

This paper studies an inventory policy for a retailer who orders his products from a supplier whose deliveries only partially satisfy the quality require- ments. We model this situation by an infinite-horizon periodic-review model with binomial random yield and positive lead time. We propose an order- up-to policy based on approximating the inventory model with unreliable supplier by a model with a reliable supplier and suitably modified demand distribution. The performance of the order-up-to policy is verified by com- paring it with both the optimal policy and the safety stock policy proposed in Inderfurth & Vogelgesang (2013). Further, we extend our approximation to a dual-sourcing model with two suppliers: the first slow and unreliable, and the other fast and fully reliable. Compared to the dual-index order- up-to policy for the model with full information on the yield, the proposed approximation gives promising results

The final order problem for repairable spare parts under condemnation

We consider a manufacturer of complex machines that offers service contracts to her customers, committing herself to repair failed spare parts throughout a fixed service period. The suppliers of spare parts often discontinue the production of some parts as technology advances and ask the manufacturer to place a final order. We address the problem of determining final orders for such spare parts. The parts that we consider are repairable, but they are subject to the risk of condemnation. We build a transient Markovian model to represent the problem for a repairable spare part with a certain repair probability and repair lead time and we present some approximations that allow for further real life characteristics to be included. Furthermore, an approximate model that can be computed more efficiently is presented, and the sensitivity of the results obtained with respect to the problem parameters for both of the models is discussed

Optimal data pooling for shared learning in maintenance operations

This paper addresses the benefits of pooling data for shared learning in maintenance operations. We consider a set of systems subject to Poisson degradation that are coupled through an a-priori unknown rate. Decision problems involving these systems are high-dimensional Markov decision processes (MDPs). We present a decomposition result that reduces such an MDP to two-dimensional MDPs, enabling structural analyses and computations. We leverage this decomposition to demonstrate that pooling data can lead to significant cost reductions compared to not pooling