A two-station stochastic inventory model with exact methods of computing optimal policies

A method is presented for calculating optimal inventory levels in a two-station transactions reporting inventory system. The criterion of optimality is the minimization of expected cost. The computational properties of the model are stressed and the solution method is precise. It is shown that when the model represents a central warehouse which supplies several retail outlets, stocks carried at the central location affect the capacity of the system to handle orders. Stocks carried at the retail level affect only the size of the retail order backlog.     

Quantifying optimal mesh and ring design costs

During the last decade telecommunication operators have been deploying WDM (Wavelength Division Multiplexing) technology to satisfy the exponential growth in global communication. While facilitating the advanced information society of today, this has also led to a higher dependency on the networks, and furthermore the high capacity utilization of optical fibers means that a single link failure will influence many users and enterprises. For these reasons, protection of network connections has become a major competitive parameter for the operators. Currently, the most popular protection method is ring protection, due to its simplicity, requiring only basic management functionality and operating with local restoration control. While many optical rings have been deployed, little work has been published on exactly what the cost of ring networks are, compared to general mesh networks. In this article we perform a quantitative comparison between ring protection and mesh protection, using real world network data and realistic prices for network components. Extending classic LP flow models to take rings and node costs into account, and using a link‐path based mesh network LP model, we are able to perform a total cost comparison of the two architectures, and of manual ring network design. The results suggest that the price of mesh network components must be reduced significantly to be competitive with ring based networks, and also that manual network design does not necessarily lead to the most cost‐efficient designs. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

Designing rewards-based crowdfunding campaigns for strategic (but distracted) contributors

In rewards-based crowdfunding, entrepreneurs solicit donations from a large number of individual contributors. If total donations exceed a prespecified funding target, the entrepreneur distributes nonmonetary rewards to contributors; otherwise, their donations are refunded. We study how to design such campaigns when contributors choose not just whether to contribute, but also when to contribute. We show that strategic contribution behavior—when contributors intentionally delay until campaign success is likely—can arise from the combination of nonrefundable (potentially very small) hassle costs and the risk of campaign failure, and can explain pledging patterns commonly observed in crowdfunding. Furthermore, such delays do not hurt the entrepreneur if contributors are perfectly rational, but they do if contributors are distracted, that is, if they might fail to return to the campaign after an intentional delay. To mitigate this, we find that an entrepreneur can use a simple menu of rewards with a fixed number of units sold at a low price, and an unlimited number sold at a higher price; this segments contributors over time based on the information they observe upon arrival. We show that, despite its simplicity, such a menu performs well compared to a theoretically optimal menu consisting of an infinite number of different rewards and price levels under many conditions.     

Evaluating appointment postponement in scheduling patients at a diagnostic clinic

Diagnostic clinics are among healthcare facilities that suffer from long waiting times which can worsen medical outcomes and increase patient no-shows. Reducing waiting times without significant capital investments is a challenging task. We tackle this challenge by proposing a new appointment scheduling policy that does not require significant investments for diagnostic clinics. The clinic in our study serves outpatients, inpatients, and emergency patients. Emergency patients must be seen on arrival, and inpatients must be given next day appointments. Outpatients, however, can be given later appointments. The proposed policy takes advantage of this by allowing the postponement of the acceptance of appointment requests from outpatients. The appointment scheduling process is modeled as a two-stage stochastic programming problem where a portion of the clinic capacity is allocated to inpatients and emergency patients in the first stage. In the second stage, outpatients are scheduled based on their priority classes. After a detailed analysis of the solutions obtained from the two-stage stochastic model, we develop a simple, non-anticipative policy for patient scheduling. We evaluate the performance of this proposed, easy-to-implement policy in a simulation study which shows significant improvements in outpatient indirect waiting times.     

Fitting Lanchester equations to the battles of Kursk and Ardennes

Lanchester equations and their extensions are widely used to calculate attrition in models of warfare. This paper examines how Lanchester models fit detailed daily data on the battles of Kursk and Ardennes. The data on Kursk, often called the greatest tank battle in history, was only recently made available. A new approach is used to find the optimal parameter values and gain an understanding of how well various parameter combinations explain the battles. It turns out that a variety of Lanchester models fit the data about as well. This explains why previous studies on Ardennes, using different minimization techniques and data formulations, have found disparate optimal fits. We also find that none of the basic Lanchester laws (i.e., square, linear, and logarithmic) fit the data particularly well or consistently perform better than the others. This means that it does not matter which of these laws you use, for with the right coefficients you will get about the same result. Furthermore, no constant attrition coefficient Lanchester law fits very well. The failure to find a good‐fitting Lanchester model suggests that it may be beneficial to look for new ways to model highly aggregated attrition. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.