An optimal approach for the joint problem of level of repair analysis and spare parts stocking

We propose a method that can be used when deciding on how to maintain capital goods, given a product design and the layout of a repair network. Capital goods are physical systems that are used to produce products or services. They are expensive and technically complex and have high downtime costs. Examples are manufacturing equipment, defense systems, and medical devices

An iterative method for the simultaneous optimization of repair decisions and spare parts stocks

In the development process of a capital good, it should be decided how to maintain it once it is in the field. The level of repair analysis (LORA) is used to answer the questions: 1) which components to repair upon failure, and which to discard, 2) at which locations in the repair network to perform the repairs, and 3) at which locations to deploy resources, such as repair equipment. Next, it should be decided what amount of spare parts to store at each location in the network in order to guarantee a certain availability of the product. Usually, the LORA and the spare parts stocking problem are solved sequentially. However, solving the LORA first can lead to high spare parts costs. Therefore, we propose an iterative approach to solve the two problems jointly. We find that the total costs are lowered with 3.2% on average and almost 35% at maximum in our experiments. A cost reduction of a few percent may be worth hundreds of thousands of euros over the life cycle of a capital good

An approximate approach for the joint problem of level of repair analysis and spare parts stocking

For the spare parts stocking problem, generally METRIC type methods are used in the context of capital goods. A decision is assumed on which components to discard and which to repair upon failure, and where to perform repairs. In the military world, this decision is taken explicitly using the level of repair analysis (LORA). Since the LORA does not consider the availability of the capital goods, solving the LORA and spare parts stocking problems sequentially may lead to suboptimal solutions. Therefore, we propose an iterative algorithm. We compare its performance with that of the sequential approach and a recently proposed, so-called integrated algorithm that finds optimal solutions for twoechelon, single-indenture problems. On a set of such problems, the iterative algorithm turns out to be close to optimal. On a set of multi-echelon, multi-indenture problems, the iterative approach achieves a cost reduction of 3%on average (35%at maximum) as compared to the sequential approach. Its costs are only 0.6 % more than those of the integrated algorithm on average (5 % at maximum). Considering that the integrated algorithm may take a long time without guaranteeing optimality, we believe that the iterative algorithm is a good approach. This result is further strengthened in a case study, which has convinced Thales Nederland to start using the principles behind our algorithm

Practical extensions to the level of repair analysis

The level of repair analysis (lora) gives answers to three questions that are posed when deciding on how to maintain capital goods: 1) which components to repair upon failure and which to discard, 2) at which locations in the repair network to perform each type of repairs, and 3) at which locations in the network to deploy resources, such as test equipment. The goal is to achieve the lowest possible life cycle costs. Various models exist for the lora problem. However, these models tend to be restrictive in that specic business situations cannot be incorporated, for example, having repair equipment with a capacity restriction or the occurrence of unsuccessful repairs.We discuss and model various practically relevant extensions to an existing minimum cost \ud ow formulation for the lora problem. We show the added value of these model renements in an extensive numerical experiment

A Minimum Cost Flow model for Level of Repair Analysis

Given a product design and a repair network for capital goods, a level of repair analysis determines for each component in the product (1) whether it should be discarded or repaired upon failure and (2) at which location in the repair network to do this. In this paper, we show how the problem can be modelled as a minimum cost ow problem with side constraints. Advantages are that (1) solving our model requires less computational effort than solving existing models and (2) we achieve a high model exibility, i.e., many practical extensions can be added. Furthermore, we analyse the added value of modelling the exact structure of the repair network, instead of aggregating all data per echelon as is common in the literature. We show that in some cases, cost savings of over 7% can be achieved. We also show when it is sufficient to model the repair network by echelons only, which requires less input data

On two-echelon inventory systems with Poisson demand and lost sales

We derive approximations for the service levels of two-echelon inventory systems with lost sales and Poisson demand. Our method is simple and accurate for a very broad range of problem instances, including cases with both high and low service levels. In contrast, existing methods only perform well for limited problem settings, or under restrictive assumptions.\u

Closed-loop two-echelon repairable item systems

In this paper we consider closed loop two-echelon repairable item systems with repair facilities both at a number of local service centers (called bases) and at a central location (the depot). The goal of the system is to maintain a number of production facilities (one at each base) in optimal operational condition. Each production facility consists of a number of identical machines which may fail incidentally. Each repair facility may be considered to be a multi-server station, while any transport from the depot to the bases is modeled as an ample server. At all bases as well as at the depot, ready-for-use spare parts (machines) are kept in stock. Once a machine in the production cell of a certain base fails, it is replaced by a ready-for-use machine from that base's stock, if available. The failed machine is either repaired at the base or repaired at the central repair facility. In the case of local repair, the machine is added to the local spare parts stock as a ready-for-use machine after repair. If a repair at the depot is needed, the base orders a machine from the central spare parts stock to replenish its local stock, while the failed machine is added to the central stock after repair. Orders are satisfied on a first-come-first-served basis while any requirement that cannot be satisfied immediately either at the bases or at the depot is backlogged. In case of a backlog at a certain base, that base's production cell performs worse. To determine the steady state probabilities of the system, we develop a slightly aggregated system model and propose a special near-product-form solution that provides excellent approximations of relevant performance measures. The depot repair shop is modeled as a server with state-dependent service rates, of which the parameters follow from an application of Norton's theorem for Closed Queuing Networks. A special adaptation to a general Multi-Class MDA algorithm is proposed, on which the approximations are based. All relevant performance measures can be calculated with errors which are generally less than one percent, when compared to simulation results. \u