Dynamic demand fulfillment in spare parts networks with multiple customer classes

Abstract

We study real-time demand fulfillment for networks consisting of multiple local warehouses, where spare parts of expensive technical systems are kept on stock for customers with different service contracts. Each service contract specifies a maximum response time in case of a failure and hourly penalty costs for contract violations. Part requests can be fulfilled from multiple local warehouses via a regular delivery, or from an external source with ample capacity via an expensive emergency delivery. The objective is to minimize delivery cost and penalty cost by smartly allocating items from the available network stock to arriving part requests. We propose a dynamic allocation rule that belongs to the class of one-step lookahead policies.To approximate the optimal relative cost, we develop an iterative calculation scheme that estimates the expected total cost over an infinite time horizon, assuming that future demands are fulfilled according to a simple static allocation rule. In a series of numerical experiments, we compare our dynamic allocation rule with the optimal allocation rule, and a simple but widely used static allocation rule. We show that the dynamic allocation rule has a small optimality gap and that it achieves an average cost reduction of 7.9% compared to the static allocation rule on a large test bed containing problem instances of real-life size.

System-oriented inventory models for spare parts

“…Another type of information that may be beneficial to use, is about the actual on hand stocks of neighboring local warehouses when a lateral transshipment is needed. It then makes sense to fulfil a demand from a neighboring local warehouse that has still two or more parts on stock rather than from a warehouse that has only one part left, see, e.g., Jalil (2011) and Tiemessen et al (2013).…”

Mitigating Spillover in Online Retailing via Replenishment

“…In specifying the replenishment policy, we assumed a myopic fulfillment policy; one might determine how to recast the replenishment policy, allowing for one of the more sophisticated fulfillment policies mentioned in the literature review. Indeed, there is evidence that a smarter fulfillment policy can lead to significant cost reductions in comparison to a myopic rule (Acimovic and Graves 2015;Tiemessen et al 2013). Additionally, one would like to relax the assumption of a deterministic and common lead time to allow for uncertain and heterogeneous lead times.…”

Service differentiation through selective lateral transshipments

“…We stop once we cannot find further policies with negative reduced costs. Let ( ) and ( ) denote the shadow price values for constraints (16) and (17) respectively, resulting from solving (P2) for a given set of item policies. The reduced costs ( ) for a policy are now found as follows, with suffix (i.e., the policy index) omitted for simplicity:…”

“…At an operational level, the amount of literature is much more limited and comprises a few multilocation models. Jalil [10] and Tiemessen et al [16] consider single-item models with multiple warehouses and multiple customer classes, where a request can often be met from more than one warehouse. Differentiation occurs by not necessarily satisfying a low priority request from the nearest warehouse (or from any warehouse in the system) to reserve stock for premium requests.…”