M.C. van der Heijden

Last time buy and repair decisions for spare parts

Original Equipment Manufacturers (OEMs) of advanced capital goods often offer service contracts for system support to their customers, for which spare parts are needed. Due to technological changes, suppliers of spare parts may stop production at some point in time. As a reaction to that decision, an OEM may place a so-called Last Time Buy (LTB) order to cover demand for spare parts during the remaining service period, which may last for many years. The fact that there might be other alternative sources of supply in the next periods complicates the decision on the LTB. In this paper, we develop a heuristic method to find the near-optimal LTB quantity in presence of an imperfect repair option of the failed parts that can be returned from the field. Comparison of our method to simulation shows high approximation accuracy. Numerical experiments reveal that repair is an excellent option as alternative sourcing, even if it is more expensive than buying a new part, because of the option to postpone the repair until the parts are needed. In addition, we show the impact of other key parameters on costs and LTB quantity.

Effects of finite repair capacity in multi-echelon, multi-indenture service part supply systems

In this paper, we consider multi-echelon, multi-indenture supply systems for repairable service parts with finite repair capacity. We show that the commonly used assumption of infinite capacity may seriously affect system performance and stock allocation decisions if the repair shop utilisation is relatively high. Both for the case of item-dedicated and shared repair shops, we modify the well-known VARI-METRIC method to allocate service part stocks in the network. The repair shops are modelled by (single or multi-class) multi-server queuing systems. We validate our procedure by comparison to results from discrete event simulation. This comparison shows that the accuracy of the technique presented in this article is on average more than five times as close to simulated values as the classical VARI-METRIC technique.

Approximations for the waiting-time distribution in an M/P H/c priority queue

We investigate the use of priority mechanisms when assigning service engineers to customers as a tool for service differentiation. To this end, we analyze a non-preemptive

Supply rationing in multi-echelon divergent systems

M/PH/c

Preventive maintenance and the interval availability distribution of an unreliable production system

M/PH/c priority queue with various customer classes. For this queue, we present various accurate and fast methods to estimate the first two moments of the waiting time per class given that all servers are occupied. These waiting time moments allow us to approximate the overall waiting time distribution per class. We subsequently apply these methods to real-life data in a case study.

Dynamic transport scheduling under multiple resource constraints

We consider a multi-item spare parts problem with multiple warehouses and two customer classes, where lateral transshipments are used as a differentiation tool. Specifically, premium requests that cannot be met from stock at their preferred warehouse may be satisfied from stock at other warehouses (so-called lateral transshipments). We first derive approximations for the mean waiting time per class in a single-item model with selective lateral transshipments. Next, we embed our method in a multi-item model minimizing the holding costs and costs of lateral and emergency shipments from upstream locations in the network. Compared to the option of using only selective emergency shipments for differentiation, the addition of selective lateral transshipments can lead to significant further cost savings (14% on average).

Analysing divergent logistic networks with local (R, S) inventory control

In this paper we determine a simple inventory control rule for multi-echelon distribution systems under periodic review without lot sizing. The primary focus is the two-echelon model with a stockless central depot, but several extensions (> 2 echelons, central stock allowed) are discussed as well. The control rule consists of two parameter sets: a set of rationing fractions at the central depot with the purpose to minimize inventory imbalance, and a set of order-up-to levels for the local stockpoints with the purpose to achieve a target service level (fill rate) per local stockpoint. The problem to calculate these control parameters is solved by decomposition in two subproblems. First the rationing fractions are determined such that the (approximate) expected system imbalance is minimized. Next the order-up-to levels are calculated such that the target fill rates are achieved. Numerical results show that this control rule, called Balanced Stock (BS) rationing, is accurate and more robust than the Consistent Appropriate Share (CAS) rationing policy.

Modeling Stochastic Lead Times in Multi-Echelon Systems

To avoid road congestion, we are developing a highly automated underground transport system using automatic guided vehicles (AGVs) around Schiphol Airport. It is unique in its scale, incorporating 16 to 25 km tubes connecting five to 20 terminals, and it includes 200 to 400 AGVs to transport an estimated 3.5 million tons of cargo in 2020 with different ordering priorities. According to the current plans, the system will run from 2006 on. Since 1997, we have used object-oriented simulations to plan the dimensions of the system (number of AGVs, terminal sizes) and to design the layout (network, terminals). We showed that an investment reduction of plus or minus 20 percent is feasible using periodically switched one-way tube sections. We developed a variety of logistics optimization algorithms and heuristics, including allocating AGVs between terminals, scheduling terminals, and controlling traffic. We used simulation control structures to test prototype AGVs on a test site. Performing distributed simulations with a mixture of simulated and real objects, we could reduce the risks of the new technology.