Selected Publications
Turrini, Laura and Joern Meissner (2019): Spare parts inventory management: New evidence from distribution fitting, European Journal of Operational Research, 273 (1): 118-130.
Abstract: Spare parts are necessary for ensuring the functioning of the critical equipment of many companies, and as such, they play a central role in these companies’ operations. Inventory control of spare parts is particularly challenging due to the nature of their demand, which is usually slow-moving, erratic and lumpy. As inventory policies rely on the forecasted lead-time demand distribution and this choice impacts the performance of the system, an ill-suited hypothesized distribution may result in high preventable costs. In this study, we contribute to the empirical literature by analyzing what distributions best fit spare parts demand. We use the Kolmogorov Smirnov (K–S) goodness-of-fit test to find the best-fitting distributions to our data and compare our results to those in the literature. Furthermore, we implement a slightly modified K–S test that places greater emphasis on differences in the right tail of the distribution, mirroring real-world inventory applications, and less emphasis on the left tail. Finally, we link the goodness-of-fit of the distributions to their inventory performance. Our first dataset comes from the German renewable energy industry and is composed of the weekly demand for more than 4000 items over the period 2011–2013. The second dataset comes from the Royal Air Force. It is composed of monthly demand for 5000 items over the period 1996–2002.
Meissner, Joern and Olga V. Senicheva (2018): Approximate dynamic programming for lateral transshipment problems in multi-location inventory systems, European Journal of Operational Research, 265 (1): 49-64.
Abstract: Companies commonly allocate their inventories across multiple locations based on their historical sales rates. However, random fluctuations in customer purchases, such as those caused by weather conditions and other external factors, might cause significant deviations from expected demand, leading to excess stock in some locations and stockouts in others. To fix this mismatch, companies often turn to lateral transshipments, e.g., the movement of stock between locations of the same echelon. In this paper, we examine multi-location inventory systems under periodic review with multiple opportunities for proactive transshipments within one order cycle. If stockouts occur, demand is lost with no opportunity to backorder. The objective of our model is to find an optimal policy that indicates the sources and the destinations of transshipments as well as the number of units, to maximise the profit of the network. We create a dynamic program that can, in principal, be solved to optimality using Bellman’s equation. However, the size of the state and decision spaces makes it impossible to find the optimal policy for real-world sized problem instances. Thereby, we use forward approximate dynamic programming to find a near-optimal transshipment policy. Finally, we conduct an extensive numerical study to gauge the performance of our transshipment policy. For small size instances, we compare our policy to the optimal one. For larger scale instances, we consider other practically oriented heuristics. Our numerical experiments show that our proposed algorithm performs very well compared to state-of-the-art methods in the literature.
Meissner, Joern and Arne K. Strauss (2012): Network revenue management with inventory-sensitive bid prices and customer choice, European Journal of Operational Research, 216 (2): 459-468.
Abstract: We develop an approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a non-linear function which is separable across resource inventory levels. This approximation can exhibit significantly improved accuracy compared to currently available methods. It further allows for arbitrary aggregation of inventory units and thereby reduction of computational workload, yields upper bounds on the optimal expected revenue that are provably at least as tight as those obtained from previous approaches. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach can outperform some recently proposed alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.
Federgruen, Awi, Joern Meissner and Michal Tzur (2007): Progressive Interval Heuristics for the Multi-Item Capacitated Lot Sizing Problems, Operations Research, 55 (3): 490-502.
Abstract: We consider a family of N items which are produced in or obtained from the same production facility. Demands are deterministic for each item and each period within a given horizon of T periods. If in a given period an order is placed, setup costs are incurred. The aggregate order size is constrained by a capacity limit. The objective is to find a lot-sizing strategy that satisfies the demands for all items over the entire horizon without backlogging, and which minimizes the sum of inventory carrying, fixed and variable order costs. All demands, cost parameters and capacity limits may be time-dependent. In the basic (JS)-model, the setup cost of an order does not depend on the composition of the order. The (JIS)-model allows for item-dependent setup costs in addition to the joint setup costs. We develop and analyze a class of so-called progressive interval heuristics. A progessive interval heuristic solves a (JS) or (JIS) problem over a progressively larger time-interval, always starting with period 1, but fixing the setup variables of a progressively larger number of periods at their optimal values in earlier iterations. Different variants in this class of heuristics allow for different degrees of flexibility in adjusting continuous variables determined in earlier iterations of the algorithm. For the (JS)-model and the two basic implementations of the progressive interval heuristics, we show under some mild parameter conditions, that the heuristics can be designed to be epsilon-optimal for any desired value of epsilon > 0 with a running time that is polynomially bounded in the size of the problem. They can also be designed to be simultaneously asymptotically optimal and polynomially bounded. A numerical study covering both the (JS) and the (JIS) model, shows that a progressive interval heuristic generates close-to-optimal solutions with modest computational effort and that it can be effectively used to solve large-scale problems.
Maglaras, Constantinos and Joern Meissner (2006): Dynamic Pricing Strategies for Multi-Product Revenue Management Problems, Manufacturing & Service Operations Management, 8 (2): 136-148.
Abstract: This chapter reviews multi-product dynamic pricing models for a revenue maximizing monopolist firm. The baseline model studied in this chapter is of a seller that owns a fixed capacity of a resource that is consumed in the production or delivery of some type of product. The seller selects a dynamic pricing strategy for the offered product so as to maximize its total expected revenues over a finite time horizon. We then review how this model can be extended to settings where the firm is selling multiple products that consume this firm's capacity, and finally highlight a connection between these dynamic pricing models and the closely related model where prices are fixed, and the seller dynamically controls how to allocate capacity to requests for the different products. Methodologically, this chapter reviews the dynamic programming formulations of the above problems, as well as their associated deterministic (fluid) analogues. It highlights some of the key insights and pricing heuristics that are known for these problems, and briefly mentions possible extensions and areas of current interest.