bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal Pilfering Policies for Dynamic Continuous Thieves
Authors: Sethi Suresh P. 
Published: November 2008

This paper considers the problems of a dynamic continuous thief, such as a habitual shoplifter or a gas siphoner, who must choose the pilfering rate (which increases the probability of his arrest over time) to maximize the present value of his total expected gain over a given finite or infinte horizon. The gain function incorporates the amount of pilfering, the one-shot penalty at the time of arrest and the continuous punishment subsequent to arrest. A thief is classified as a profit-maximizer, a kleptomaniac, or a risk-averter depending on whether he considers his gain function to be linear, convex, or concave in the pilfering rate, respectively. Optimal pilfering rates are obtained for each of these different types of thieves by using the methods of optimal control. It is shown that the optimal pilfering policy of a profit-maximizing thief is bang-bang, i.e., either to pilfer at the maximum possible rate or not to pilfer at all. In the particular case of a finitely-lived profit-maximizing thief, the optimal policy is not to pilfer while the thief is young and then to pilfer at the maximum rate toward the end of his life so that the continuous punishment subsequent to his arrest will not last too long. In the infinite horizon cases, both the profit-maximizing thieves and kleptomaniacs will either never pilfer or pilfer at the maximum rate depending on whether the parameters of the problem they are facing are unfavorable or favorable. The risk-averse thief, on the other hand, would settle at an intermediate pilfering rate throughout the infinite horizon. Finally, it is expected that knowledge of these optimal policies will have implications for the allocation of money between police, courts and penal institutions.
Group Preference Aggregation Rules Based on Strength of Preference
Authors: Dyer James S. , Sarin Rakesh K. 
Published: November 2008

In many decision problems, the consequences of an action may impact several individuals, and the decision may be based on the preferences of those who are affected. The rule for aggregating these individual preferences is called a group preference aggregation rule. In this paper we present a theory for group preference aggregation rules based on the concept of strength of preference. This concept is operationalized by asking an individual to order differences in his strength of preference between pairs of alternatives. A preference representation function that reproduces this ordering is called a measurable value function. In general we cannot expect a measurable value function and a utility function obtained from lottery questions to bear any particular relation to each other. A group preference aggregation rule based on the measurable value functions of individuals can be used under conditions of certainty without introducing lotteries into the preference assessment procedure. In addition, it facilitates the difficult problem of making interpersonal utility comparisons. We also establish several relationships between the group preference aggregation rules based on measurable value functions and previous work based on risky utility functions. These relationships allow either one of these preference aggregation rules to be transformed into the other after obtaining only a minimal amount of information from a group member. These relationships also raise several fundamental questions about the effects of the introduction of risk on the preferences of an individual and on the preferences of a group. We offer some preliminary comments and results regarding these issues.
A Conceptual Framework for the Design of Organizational Control Mechanisms
Authors: Ouchi William G. 
Published: November 2008

The problem of organization is the problem of obtaining cooperation among a collection of individuals or units who share only partially congruent objectives. When a team of individuals collectively produces a single output, there develops the problem of how to distribute the rewards emanating from that output in such a manner that each team member is equitably rewarded. If equitable rewards are not forthcoming, members will, in future cooperative ventures, adjust their efforts in such a manner that all will be somewhat worse off (cf. Simon [Simon, H. A. 1964. On the concept of organizational goal. Admin. Sci. Quart. 9 (1, June) 1–22.], Marschak [Marschak, Thomas A. 1965. Economic theories of organization. J. G. March, ed. Handbook of Organizations. Rand McNally, Chicago, Ill., 423–450.], Alchian and Demsetz [Alchian, Armen A., Harold Demsetz. 1972. Production, information costs, and economic organization. Amer. Econom. Rev. 62 777–795.). It is the objective of this paper to describe three fundamentally different mechanisms through which organizations can seek to cope with this problem of evaluation and control. The three will be referred to as markets, bureaucracies, and clans. In a fundamental sense, markets deal with the control problem through their ability to precisely measure and reward individual contributions; bureaucracies rely instead upon a mixture of close evaluation with a socialized acceptance of common objectives; and clans rely upon a relatively complete socialization process which effectively eliminates goal incongruence between individuals. This paper explores the organizational manifestations of these three approaches to the problem of control. The paper begins with an example from a parts distribution division of a major company which serves to give some flesh to what might otherwise be overly-abstract arguments. Through the example, each of the three mechanisms is explicated briefly and discussed in terms of two prerequisite conditions, one social and the other informational. The more concrete organization design features of the three forms are considered, along with some consideration of the unique costs accompanying each form.
A Markovian Queue with N Servers Subject to Breakdowns and Repairs
Authors: Neuts Marcel F. , Lucantoni David M. 
Published: November 2008

We study a queue with N servers, who may break down and require repair at a facility, which has c repair crews. Under exponential assumptions, this model has an algorithmically tractable solution. It is then in fact a particular case of the M/M/N queue in a Markovian environment. The purpose of this paper is twofold. As a novel methodological contribution, the stationary distributions of various waiting times are discussed. Although fairly involved, these distributions may be computed by classical numerical methods. The second and primary purpose is to demonstrate the utility of interactive computation in answering questions on the behavior, design and control of certain service systems. By numerical examples, we shall show that during periods when most servers are down, large build-ups may occur which affect the queue adversely for a long time afterwards. We also find that such build-ups are aggravated by reducing the number of repair crews, but may be attenuated by reducing the arrival rates during periods when the service has deteriorated. Potential applications are in manpower planning, as in a typing pool where persons may be absent and in determining the size of a battery of machines, where machines may be inoperative due to maintenance and repair. More generally, this paper draws attention to qualitative behavior of queues with randomly varying service rates. Such queues may exhibit substantial and long-lived fluctuations, which are not always apparent from simulation runs and which are also typically not present in the more classical queueing models.
Comparison of a Multi-Pass Heuristic Decomposition Procedure with Other Resource-Constrained Project Scheduling Procedures
Authors: Holloway Charles A. , Nelson Rosser T. , Suraphongschai Vichit 
Published: November 2008

The purpose of this paper is to evaluate a heuristic procedure for solving the resource-constrained project scheduling problem. Projects that involve completing a sequence of well defined activities, some of which can be done in parallel, often are characterized by capacity limitations on some resources. This means schedules generated using standard critical path methods often are not feasible. Resource leveling procedures have been developed to improve the schedule. Formulating the problem with explicit resource constraints is another, more direct, way to accommodate limited capacities. Solution procedures to this problem include simple, single-pass heuristics and branch-and-bound optimization procedures. The former are easy to apply but may yield poor results. The latter provide optimal results but require a substantial amount of computation even for small problems. The procedure evaluated here is representative of a class of multi-pass procedures based on problem decomposition. A brief outline of the heuristic procedure and the method of decomposition is given in the paper. It is applicable to single or multiple project networks. Comparisons were made between this procedure and single-pass heuristics and branch-and-bound procedures. Solutions found using the multi-pass procedure were comparable to those obtained with a branch-and-bound optimization algorithm while computing times were significantly shorter. Analysis based on the sample of problems tested indicates the procedure is much less sensitive to problem size than the branch-and-bound algorithm. The evaluation, consisting of testing procedures using a set of test problems, is described in detail. The overall results suggest that the method and procedure used may provide an effective means of computing schedules for resource-constrained projects.
Simple Ranking Methods for Allocation of One Resource
Authors: Zipkin Paul H. 
Published: November 2008

This paper considers optimization problems with a nonlinear-additive objective function and a single linear constraint. Such models have numerous direct applications and serve as subproblems in procedures for more complex problems. Some important portfolio selection problems can be expressed in this form, and the problem also arises in economic theory. Several authors have noticed independently that special cases and variants of the problem can be solved exactly by surprisingly simple, finite algorithms. The major purpose of this paper is to present these results in a unified framework, which then permits substantial generalizations and extensions. The results lend themselves to an appealing managerial interpretation, similar to the rate-of-return cutoff rules of capital budgeting.
The Impact of the Mode of Information Presentation on Learning and Performance
Authors: Lucas Henry C. , Nielsen Norman R. 
Published: November 2008

A logistics management game was modified to provide an information/decision system that would serve as a framework for experimentally investigating the impact of the mode of information presentation. Subjects played the game by making a series of weekly decisions during each of two playing sessions. The experimental treatments were varied between sessions to permit the testing of a set of hypotheses pertaining to the impact that information and its mode of presentation might have upon performance and the rate of performance change. Data on player decisions and simulated results (performance) were collected automatically by the modified gaming system while data on player backgrounds were collected via questionnaires. The experimental data provide some support for hypotheses relating to the superiority of display terminals and the influence of user background on exhibited learning. The data does not, however, provide solid support for hypotheses relating to the impact of additional information or of graphical forms of information presentation. Analysis of the experimental data suggests the need to consider individual and user-group differences in systems design. User background differences may also considerably complicate the design of graphical output for information systems. Experimental research involving human interaction with information systems is a very difficult undertaking. The experiences gained from this project and reported in this paper should aid other researchers in designing effective experiments. The findings discussed also point toward several areas offering potential for further research. Many different variables influence the development and use of computer-based information systems. This paper presents the results of an experiment designed to examine the impact of the amount of information provided, the mode of presentation of that information, and the differences in personal backgrounds on the performance and rate of performance change for users of a computer-based system.
Models for Affirmative Action Planning and Evaluation
Authors: Hopkins David S. P. 
Published: November 2008

Two models are presented as aids in setting realistic numerical goals for the employment of women and minority persons, and in evaluating progress towards meeting such goals. By modelling personnel flows in terms of hiring and persistence rates for various job categories, we develop a consistent structure for projecting future employment patterns as a function of hiring goals. These hiring goals themselves are made to depend on the growth-rate of staff size, the relative availability of women and minority candidates in the potential pool of job applicants, and the organization's policies towards equity in hiring. The second model considers a small organizational unit with fixed staff size. By formulating the process of attrition and replacement with new hires as a Markov chain, we are able to make probability statements about the number of positions that will be occupied by women and/or minority persons at some future time. The relationship of the these probabilities to the distribution among job applicants is made explicit. Applications of these models to faculty and staff planning at Stanford University are briefly discussed.
Turnpike Horizons for Production Planning
Authors: Thompson Gerald L. , Sethi Suresh P. 
Published: November 2008

A quadratic model for production-inventory planning was made famous by Holt, Modigliani, Muth, and Simon in 1960 in (Holt, C. C., F. Modigliani, J. F. Muth, H. A. Simon. 1960. Planning Production, Inventories, and Work Force. Prentice-Hall, Englewood Cliffs, New Jersey.), especially for its application to a paint factory. A discrete control version of a related quadratic production-inventory model was studied by Kleindorfer, Kriebel, Thompson, and Kleindorfer in (Kleindorfer, P. R., C. H. Kriebel, G. L. Thompson, G. B. Kleindorfer. 1975. Discrete optimal control of production plans. Management Sci. 22 261–273.). In the present paper we solve a continuous version of the model in Kleindorfer, Kriebel, Thompson, and Kleindorfer (Kleindorfer, P. R., C. H. Kriebel, G. L. Thompson, G. B. Kleindorfer. 1975. Discrete optimal control of production plans. Management Sci. 22 261–273.) in complete detail. The reason we are able to obtain a complete solution (which can rarely be done in control models) is that the linear decision rule, which is optimal here as in other quadratic models, permits the elimination of the adjoint function from the state variable equation after one differentiation of the latter. Thus the difficult two-point boundary value problem which usually arises in control problems is converted into an ordinary second order differential equation, which is readily solved. One advantage of having a complete solution to the problem is that it is possible to determine turnpike horizon points. These correspond to zeros of the adjoint function, and have the property that if they are known exactly, then a production-inventory plan which is optimal up to the next horizon point also forms part of the overall optimal plan. In the case of cyclic demand these turnpike horizon points usually occur every half cycle. Similar horizons are likely to exist in real production-inventory problems. A planning procedure for a real problem which extends only as far as a suspected horizon has a good chance of producing an optimal or near optimal solution for that period of time. A second advantage of having the complete solution available is that it is possible to develop a practical production-inventory system which intermingles a prediction procedure (such as the use of a finite Fourier series) with the solution procedure so that a comparison between predicted and actual inventories can be made continuously. Whenever the discrepancy between these two becomes sufficiently large, the model suggests proper corrective actions to be taken.
Integer Programming Models for Sales Resource Allocation
Authors: Zoltners Andris A. , Sinha Prabhakant 
Published: November 2008

A practical conceptual framework for sales resource allocation modeling is presented in this paper. A literature review of sales resource allocation models is described in terms of this framework. The conceptual framework also lends itself to several integer programming models which may be used to address the variety of sales resource allocation decisions faced by every sales organization. A general model for sales resource allocation is developed which incorporates multiple sales resources, multiple time periods and carryover effects, non-separability, and risk. Several actual model implementations are discussed which illustrate the practical application of the integer programming models. The model implementations utilize recent advances in integer programming theory which enables sales managers and sales representatives to quickly develop and evaluate alternative sales resource allocation strategies.
Scheduling of Network Television Programs
Authors: Horen Jeffrey H. 
Published: November 2008

Maximizing average audience size is a major objective of national television networks. This paper examines the effect of scheduling programs on audience ratings. We determine the predictability of ratings and the major factors which influence ratings. We measure the extent to which ratings can be changed by rescheduling programs, and then test commonly discussed scheduling strategies and propose new strategies. Finally, we ascertain the extent to which the audience's benefit is served by a system of three major networks, each competing to maximize individual ratings. A forecasting model based on five years of historical data estimates ratings for hypothetical schedules. A heuristic fitting procedure yields a model which has consistent estimated parameters and explains about 70% of the variance. The previous year's ratings of a program and competing programs in its time slot are the best predictors of its rating. Also important are the day, time, and lead-in audience. The resulting estimates are incorporated in a combinatorial model, representing the decision of a program scheduler seeking to maximize ratings. A typical sized problem of scheduling 23 programs of various lengths into a week of 40 half-hour time slots can be formulated as a 65 × 800 assignment-type integer program. The optimizations show that rescheduling programs can, on the average, substantially increase a network's audience size (11.6%) and advertising revenue ($61 million per year). The optimal program scheduling for one network generally decreases the ratings of competing networks. The forecasting and optimization models are then used to evaluate commonly discussed scheduling strategies: Protecting Newcomers, Starting Fast, Homogeneity, Counterprogramming, and Bridging. Of these, Counterprogramming is the only strategy that is presently and optimally used and justified by the model. An additional strategy, termed “Avoidance,” is discovered and theoretically justified by subadditivity in ratings. This strategy increases total audience size and benefits the viewing audience.
PBDS: A Decision Support System for Regional Blood Management
Authors: Prastacos Gregory P. , Brodheim Eric 
Published: November 2008

Human blood is a perishable product: in the U.S. it has a legal lifetime of 21 days from collection, during which it can be used for transfusion to a patient of the same type, and after which it has to be discarded. It is collected in units of one pint from volunteer donors at various collection sites such as a Regional Blood Center, and after a series of typing and screening tests it is shipped to Hospital Blood Banks in the region. Once at the Hospital Blood Bank, a unit is stored and is available to satisfy the random daily demand for transfusions to patients. Since not all units demanded for a patient are generally used, a unit can be issued several times during its lifetime until transfused or outdated. Efficient management of the regional blood resources is a difficult task. In addition to the complexity of the problem, introduced by the factors outlined above, the Regional Blood Center is faced with some typical characteristics of a Health Care management problem: (i) the performance of a regional blood management system can be evaluated in terms of more than one criteria, some of which are conflicting (e.g., shortages vs. outdates), and (ii) quantitative cost measurement of the system's performance, and, therefore, comparison of alternative policies using simple cost criteria, is very difficult since the estimation of many costs involved (e.g., unavailability of blood) is purely subjective. The two most common performance measures of a blood region are the shortage rate (i.e., the percentage of days when “supplementary” deliveries have to be made to satisfy a hospital's demand), and the outdate rate (i.e., the percentage of a hospital's supply that becomes outdated) for the hospitals in the region. The system described in this paper (PBDS) has been designed so as to provide a decision support mechanism to the Regional Blood Center to address the following fundamental inventory management questions related to these measures: (i) what are the minimum achievable outdate and shortage targets that can be set for the region, (ii) what is the distribution policy to achieve those targets, and (iii) what should the level of regional supply be in order to achieve alternative targets. The system is based on a mathematical programming model whose primary objective is to optimize the allocation of the regional blood resources while observing policy constraints. It is characterized by (i) a centralized management of blood, rather man management by individual hospitals, (ii) prescheduled deliveries, and (iii) a distribution system according to which blood is “rotated” among the hospitals. PBDS has been implemented in the 38 hospital region of Long Island, New York, where it has been operational for 3 years. It has established a routine management, and has drastically reduced the outdating and shortage incidents in the region. By now, it is completely computerized, and plans are being made for its introduction to other regions in the U. S. and abroad.
Marketing Applications of the Analytic Hierarchy Process
Authors: Wind Yoram , Saaty Thomas L. 
Published: November 2008

Several marketing applications of the Analytic Hierarchy Process (AHP) are reviewed. The paper starts with a brief description of this process, which was developed by Thomas Saaty in 1971, including an eight-point outline of how to apply it. The thrust of the paper is a discussion of a number of illustrative applications of the AHP covering the following areas: the portfolio decisions of a firm whose management is concerned with the determination of the desired target portfolio and allocation of resources among its components, determination of the directions for new product development, and generation and evaluation of marketing mix strategies. Various suggestions for additional research on the AHP and its marketing applications are highlighted.