No abstract

Companies increasingly employ dual-channeling strategies with online and offline channels to reach customers. The combination of high return rates in e-commerce and the possibility for customers to return products ordered online at any offline store may result in unbalanced inventories. Transshipments can be used to deal with these unbalanced inventories. In this paper we study dynamic policies for transshipment of products that are returned cross-channel from online to offline stores. At the end of each period in a finite sales season, cross-channel returned products can be transshipped back to the online store or kept on-hand at the offline store. Optimal transshipment policies are obtained using a Markov decision process. We introduce a well-performing heuristic based on the expected costs during the sales season, with a maximum deviation of 1.59% from the optimal costs in experiments. Furthermore, we show that in all instances our heuristic outperforms static policies in which products are either always or never shipped back to the online store. We observe that dynamic transshipment policies are more effective than static policies in dealing with imbalances in the initial stock. Dynamic transshipment of cross-channel returns seems to open up possibilities for more effective demand fulfillment of dualchannel companies.

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In shared warehouse and transportation networks, dynamic shipments of inventories are carried out based on upto-date inventory information. This paper studies the effect of network structures on optimal decision-making. We propose a discrete time modeling framework with stochastic demand, capturing a wide variety of network structures. Using Markov decision processes, we obtain optimal order and dynamic shipment decisions for small networks. We compare optimal solutions of different four-node network structures. Results indicate product characteristics significantly influence the effectiveness of network structures. Surprisingly, two-echelon networks are occasionally costlier than any other network. Moreover, dynamic shipments yield considerable gains over static shipments.

Public library organizations often utilize depots for carrying out shipments to libraries in case of stockouts and for storing low demand rental items at low cost. Similar systems may be employed by rental companies for other rental products such as tools, DVDs, and jewelry. Since shipments deplete the depot's inventory, stock must be taken back from the libraries in order to deal with future shipment requests. These shipment and take-back operations are carried out periodically, e.g. daily or weekly. This work focuses on optimizing the decisions for shipments and take-backs. We model the system by means of a Markov decision process and investigate its optimal policy for various problem instances. For the takeback decision, we distinguish between so-called threshold, reactive, and preventive take-backs. We use the insights from the MDP to develop a three-phase take-back heuristic. In experiments, our heuristic performs within 1% on average from the optimal solution. For settings with a large number of libraries, it is shown that an acceptable performance can be achieved by setting a base-stock level at the depot and taking back sufficient stock from the libraries to achieve this level.