Gabriel R. Bitran

Commissioned Paper: An Overview of Pricing Models for Revenue Management

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

Hierarchical Production Planning: A Single Stage System

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.

Multiproduct queueing networks with deterministic routing: decomposition approach and the notion of interference

Queueing networks have been used to model the performance of a variety of complex systems. Since exact results exist for only a limited class of networks, the decomposition methodology has been used extensively to obtain approximate results. In this paper, we consider open queueing networks with multiple product classes, deterministic routings and general arrival and service distributions. We examine the decomposition method for such systems and show that it provides estimates of key parameters with an accuracy that is not acceptable in many practical settings. Recognizing this weakness, we enrich the approach by modeling a phenomenon previously ignored. We consider interference among products and describe its effect on the variance of the departure streams. The recognition of this effect significantly improves the performance of this methodology. We provide extensive experimental results based on the data of a manufacturer of semiconductor devices.

An Application of Yield Management to the Hotel Industry Considering Multiple Day Stays

In this paper we study optimal strategies for renting hotel rooms when there is a stochastic and dynamic arrival of customers from different market segments. We formulate the problem as a stochastic and dynamic programming model and characterize the optimal policies as functions of the capacity and the time left until the end of the planning horizon. We consider three features that enrich the problem: we make no assumptions concerning the particular order between the arrivals of different classes of customers; we allow for multiple types of rooms and downgrading; and we consider requests for multiple nights. We also consider implementations of the optimal policy. The properties we derive for the optimal solution significantly reduce the computational effort needed to solve the problem, yet in the multiple product and/or multiple night case this is often not enough. Therefore, heuristics, based on the properties of the optimal solutions, are developed to find good solutions for the general problem. We also derive upper bounds which are useful when evaluating the performance of the heuristics. Computational experiments show a satisfactory performance of the heuristics in a variety of scenarios using real data from a medium size hotel.

Hierarchical Production Planning: A Two-Stage System

This paper presents a hierarchical approach to plan and schedule production in a manufacturing environment that can be modeled as a two-stage process. A conceptual framework for this approach is described. The specific mathematical models proposed for the various hierarchical levels are discussed. The methodology is evaluated in an actual setting. The performance of the hierarchical system is contrasted with an MRP design. Encouraging results are reported.

Managing Hotel Reservations with Uncertain Arrivals

Based on our interactions with managers at two large hotels, we present a realistic model of the hotel reservation problem. Unlike traditional models, ours does not assume that all customers arrive simultaneously on the targeted booking date. We explain why this assumption may not be appropriate for the hotel industry and develop a model of reservation booking which explicitly includes the room allocation decisions which are made on the targeted booking date. Based on observations of how the problem is solved in practice as well as the insights gained from this analysis, we develop simple heuristic procedures for accepting reservations. Computational results demonstrate that these heuristics perform well relative to an upper bound that is based on perfect information about reservations requests and customer arrivals.

A structured product development perspective for service operations

In this paper we review the literature on product development from a services perspective. We identify similarities in the creation and evolution of products and services, and discuss three types of knowledge that are commonly required in a development process: the sequence of steps or procedural plan that must be followed; the understanding of what components integrate the design and how they interact (architectural knowledge); and the principles and models that describe physical or human behavior in the system that is being designed. For each step of a generic development process we review the methods and tools that are widely used in product development and may be successfully applied to service development. To illustrate the notion of architectural knowledge in the service context, we introduce an example of a service operation structure and discuss important aspects of its components. Finally we explain the role of models in the development of products and services and argue how they can help design intangible elements. We conclude the paper by identifying gaps in the literature and suggesting directions for future research.

Production Planning of Style Goods with High Setup Costs and Forecast Revisions

In this paper we study a problem, common to a wide variety of manufacturing companies, of determining the production schedule of style goods, such as clothing and consumer durables, under capacity constraints. Demand for items is stochastic and occurs in the last season of the planning horizon. Demand estimates are revised in each period. We exploit the problems two-level hierarchical structure, which is characterized by families and items. Production changeover costs from one family to another are high, compared to other costs. However, changeover costs between items in the same family are negligible. We first formulate this problem as a difficult-to-solve stochastic mixed integer programming problem. Then, exploiting the problems hierarchical structure, we formulate a deterministic, mixed integer programming problem and solve it by means of an algorithm that provides an approximate solution. A lower bound is obtained by applying generalized linear programming to the approximate problem. We illustrate the procedure using the disguised data of a consumer electronics company. The computational results demonstrate the effectiveness of the proposed approach in a practical setting.

Coordinating Clearance Markdown Sales of Seasonal Products in Retail Chains

In this paper we propose a methodology to set prices of perishable items in the context of a retail chain with coordinated prices among its stores and compare its performance with actual practice in a real case study. We formulate a stochastic dynamic programming problem and develop heuristic solutions that approximate optimal solutions satisfactorily. To compare this methodology with current practices in the industry, we conducted two sets of experiments using the expertise of a product manager of a large retail company in Chile. In the first case, we contrast the performance of the proposed methodology with the revenues obtained during the 1995 autumn-winter season. In the second case, we compare it with the performance of the experienced product manager in a simulation-game setting. In both cases, our methodology provides significantly better results than those obtained by current practices.

Theory and algorithms for linear multiple objective programs with zero–one variables

A new algorithm and theoretical results are presented for linear multiple objective programs with zero–one variables. A procedure to identify strong and weak efficient points as well as an extension of the main problem are analyzed. Extensive computational results are given and several topics for further research are discussed.