Editorial about the redesign of Management Science, in effect from the first issue of 1993.

In this paper we consider linear multiple objective programs with coefficients of the criteria given by intervals. This class of problems is of practical interest since in many instances it is difficult to determine precisely the coefficients of the objective functions. A subproblem to test if a feasible extreme point is efficient in the problem considered is obtained. A branch and bound algorithm to solve the subproblem as well as computational results are provided. Extensions are discussed.

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.

This paper presents simple approximations for networks of queues with overtime operation at some stations. This type of network is commonly encountered in several manufacturing applications. We provide bounds on the performance of the approximations for single and multiple machine stations. Our results suggest that the methods perform satisfactorily. These approximations can be used in conjunction with parametric decomposition methods to analyze queueing networks. The computational results indicate that the performance of the decomposition approach does not deteriorate when combined with the methods proposed in this paper.

In this paper we consider a single-server station processing jobs belonging to multiple product classes. The processing at the station is in batches of fixed size, r. Job arrivals in different product classes are independent of the arrivals in other classes. The arrivals within each product class and the service times are assumed to have general, independent and identical distributions. Based on approximate analyses, we present an estimate for the mean number of jobs and provide two complementary characterizations of the product departure streams. We present methods to compute the squared coefficient of variation of the departure intervals and the probability distribution of the lot sizes of product departures. The computational results reported in this paper demonstrate that the accuracy of the approximations is acceptable in most applications. Based on these results, we identify conditions under which the estimates can be expected to perform well. The methods developed in this paper complement the decomposition approach for open queueing networks proposed by Shanthikumar and Buzacott (Shanthikumar, J. G., J. A. Buzacott. 1981. Open queueing network models of job shops. Internat. J. Production Res. 19(3) 255–266.) and Whitt (Whitt, W. 1983. The queueing network analyzer. Bell System Tech. J. 62(9) 2779–2815.) and permit analysis of networks with some types of batch processing.

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.

In this paper we present a mathematical programming model for the Kanban system in a deterministic multi-stage capacitated assembly-tree-structure production setting. We discuss solution procedures to the problem and address three special cases of practical interest.

We discuss an approximation scheme for the multi-item lot size problem. It is based on an optimal basic solution of a linear programming problem derived from the original problem. The approximate solution is obtained by taking a linear convex combination of the optimal solution of the linear programming problem. We express error bounds of the approximation as a function of some parameters that can be easily estimated in practice. When set-up times are positive, the approximation may result in an infeasible solution. We take the same approach to show that the infeasibility of the approximation is small. The analysis is extended to a variable capacity problem with overtime. As an auxiliary result, we provide a bound on the duality gap of the Lagrangian dual problem.

The allocation of a specific amount of a given resource among competitive alternatives can often be modelled as a knapsack problem. This model formulation is extremely efficient because it allows convex cost representation with bounded variables to be solved without great computational efforts. Practical applications of this problem abound in the fields of operations management, finance, manpower planning, marketing, etc. In particular, knapsack problems emerge in hierarchical planning systems when a first level of decisions need to be further allocated among specific activities which have been previously treated in an aggregate way. In this paper we provide a recursive procedure to solve such problems. The method differs from classical optimization algorithms of convex programming in that it determines at each iteration the optimal value of at least one variable. Applications and computational results are presented.

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.