Gabriel R. Bitran

Bio: Gabriel R. Bitran is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Heuristics & Production planning. The author has an hindex of 41, co-authored 93 publications receiving 6653 citations.

Commissioned Paper: An Overview of Pricing Models for Revenue Management

Abstract: In this paper, we examine the research and results of dynamic pricing policies and their relation to revenue management. The survey is based on a generic revenue management problem in which a perishable and nonrenewable set of resources satisfy stochastic price sensitive demand processes over a finite period of time. In this class of problems, the owner (or the seller) of these resources uses them to produce and offer a menu of final products to the end customers. Within this context, we formulate the stochastic control problem of capacity that the seller faces: How to dynamically set the menu and the quantity of products and their corresponding prices to maximize the total revenue over the selling horizon.

Computational Complexity of the Capacitated Lot Size Problem

Abstract: In this paper we study the computational complexity of the capacitated lot size problem with a particular cost structure that is likely to be used in practical settings. For the single item case new properties are introduced, classes of problems solvable by polynomial time algorithms are identified, and efficient solution procedures are given. We show that special classes are NP-hard, and that the problem with two items and independent setups is NP-hard under conditions similar to those where the single item problem is easy. Topics for further research are discussed in the last section.

Periodic Pricing of Seasonal Products in Retailing

Abstract: This paper studies intertemporal pricing policies when selling seasonal products in retail stores. We first present a continuous time model where a seller faces a stochastic arrival of customers with different valuations of the product. For this model, we characterize the optimal pricing policies as functions of time and inventory. We use this model as a benchmark against which we compare more realistic models that consider periodic pricing reviews. We show that the structure of the optimal pricing policies in this case is consistent with the procedures observed in practice; retail stores successively discount the product during the season and promote a liquidation sale at the end of the planning horizon. We also show that the loss experienced when implementing periodic pricing reviews instead of continuous policies is small when the appropriate number of reviews is chosen. Several interesting economic insights emerge from our analysis. For example, uncertainty in the demand for new products leads to higher prices, larger discounts, and more unsold inventory. Finally, we study the effect of announced discount policies on prices and profits. We show that stores that have adopted this type of strategy usually set prices such that with high probability the merchandise is sold during the first periods and the largest discounts rarely take place.

On the Design of Hierarchical Production Planning Systems.

Abstract: : To provide effective managerial support to the decisions related to the production planning and scheduling processes, it is useful to partition this set of decisions in a hierarchical framework. In the resulting system higher level decisions impose constraints to lower level actions, and lower level decisions provide the necessary feedback to reevaluate higher level actions. The purpose of this paper is to suggest optimum procedures to deal with the resulting subproblems, and to analyze the interaction mechanisms among the different hierarchical levels. Computational results are given. (Author)

Hierarchical Production Planning: A Single Stage System

Abstract: This paper presents a hierarchical approach to plan and schedule production in a manufacturing environment that can be modeled as a single stage process. Initially, the basic tradeoffs inherent to production planning decisions are represented by means of an aggregate model, which is solved on a rolling horizon basis. Subsequently, the first solution of the aggregate plan is disaggregated, considering additional cost objectives and detailed demand constraints. Several improvements in the methodology related to hierarchical production planning are suggested. Special attention is given to alternative disaggregation procedures, problems of infeasibilities, and the treatment of high setup costs. Computational results, based on real life data, are presented and discussed.

Fundamentals of Queueing Theory

Abstract: Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Optimal dynamic pricing of inventories with stochastic demand over finite horizons

Abstract: In many industries, managers face the problem of selling a given stock of items by a deadline We investigate the problem of dynamically pricing such inventories when demand is price sensitive and stochastic and the firm's objective is to maximize expected revenues Examples that fit this framework include retailers selling fashion and seasonal goods and the travel and leisure industry, which markets space such as seats on airline flights, cabins on vacation cruises, and rooms in hotels that become worthless if not sold by a specific time We formulate this problem using intensity control and obtain structural monotonicity results for the optimal intensity resp, price as a function of the stock level and the length of the horizon For a particular exponential family of demand functions, we find the optimal pricing policy in closed form For general demand functions, we find an upper bound on the expected revenue based on analyzing the deterministic version of the problem and use this bound to prove that simple, fixed price policies are asymptotically optimal as the volume of expected sales tends to infinity Finally, we extend our results to the case where demand is compound Poisson; only a finite number of prices is allowed; the demand rate is time varying; holding costs are incurred and cash flows are discounted; the initial stock is a decision variable; and reordering, overbooking, and random cancellations are allowed