Operations & Supply Chain Management

Low inventory, a crucial part of just-in-time (JIT) manufacturing systems, enjoys increasing application worldwide, yet the behavioral effects of such systems remain largely unexplored. Operations research (OR) models of low-inventory systems typically use a simplifying assumption that processing times of individual workers are independent random variables. This leads to predictions that low-inventory systems will exhibit production interruptions leading to lower productivity. Yet empirical results suggest that low-inventory systems do not exhibit the predicted productivity losses.

Stochastic Economic Lot Scheduling Problems (ELSPs) involve settings where several items need to be produced in a common facility with limited capacity, under significant uncertainty regarding demands, unit production times, setup times, or combinations thereof. We consider systems where some products are made-to-stock while another product line is made-to-order. We present a rich and effective class of strategies for which a variety of cost and performance measures can be evaluated and optimized efficiently by analytical methods.

This paper deals with Markov decision processes with a countable state space. We demonstrate that a single, relatively simple condition suffices to guarantee that the value-iteration method converges and that an optimal policy can be computed via this method, once the existence of a solution to the average cost optimality equation has been established via any of the many available sets of existence conditions.

This survey reviews the forty-year history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references.

We analyze a randomized version of the deterministic linear programming (DLP) method for computing network bid prices. The method consists of simulating a sequence of realizations of itinerary demand and solving deterministic linear programs to allocate capacity to itineraries for each realization. The dual prices from this sequence are then averaged to form a bid price approximation. This randomized linear programming (RLP) method is only slightly more complicated to implement than the DLP method.

Consider a category of product variants distinguished by some attribute such as color or flavor. A retailer must construct an assortment for the category, i.e., select a subset variants to stock and determine purchase quantities for each offered variant. We analyze this problem using a multinomial logit model to describe the consumer choice process and a newsboy model to represent the retailer's inventory cost. We show that the optimal assortment has a simple structure and provide insights on how various factors affect the optimal level of assortment variety.

We analyze a general model of dynamic vehicle dispatching systems in which congestion is the primary measure of performance. In the model, a finite collection of tours are dynamically dispatched to deliver loads that arrive randomly over time. A load waits in queue until it is assigned to a tour. This representation, which is analogous to classical set-covering models, can be used to study a variety of dynamic routing and load consolidation problems.