bluuebunny/crossref_metadata_2025 huggingface dataset:
A Note on the Use of Nonparametric Statistics in Stochastic Linear Programming
Authors: Tintner Gerhard , Rama Sastry M. V.
Published: November 2008

An ordinary linear programming problem is formulated as [Formula: see text] under the constraints [Formula: see text] where A is a matrix with m rows and n columns, x and c are column vectors with n elements, and b is a column vector with n elements. The theory of stochastic linear programming first suggested by Tintner [Tintner, G. 1955. Stochastic linear programming with applications to agricultural economics. Sympos. Linear Programming, Vol. 1. National Bureau of Standards, Washington, D. C., 197 ff.] uses the following approach. The elements of b, c and the matrix A are assumed to be random variables with a known probability distribution. Two possible ways of deriving the distribution of z are known as direct and indirect methods.

In this article, some nonparametric statistics were applied to test the difference between the distributions derived by the direct and indirect methods. The non-parametric tests include the Kolmogorov-Smirnov statistic and Alfred Rényi's statistics.
A Comparative Study of Methods for Long-Range Market Forecasting
Authors: Armstrong J. Scott , Grohman Michael C.
Published: November 2008

The following hypotheses about long-range market forecasting were examined:

H1 Objective methods provide more accuracy than do subjective methods.

H2 The relative advantage of objective over subjective methods increases as the amount of change in the environment increases.

H3 Causal methods provide more accuracy than do naïve methods.

H4 The relative advantage of causal over naïve methods increases as the amount of change in the environment increases.

Support for these hypotheses was then obtained from the literature and from a study of a single market. The study used three different models to make ex ante forecasts of the U.S. air travel market from 1963 through 1968.

These hypotheses imply that econometric methods are more accurate for long-range market forecasting than are the major alternatives, expert judgment and extrapolation, and that the relative superiority of econometric methods increases as the time span of the forecast increases.
A Note on Sharpe's Algorithm for Minimizing the Sum of Absolute Deviations in a Simple Regression Problem
Authors: Rao M. R. , Srinivasan V.
Published: November 2008

A recent article in this Journal provides an efficient computational scheme for obtaining a regression line that minimizes the sum of absolute deviations of a set of two-dimensional points. The present note interprets that procedure as the solution of the parametric dual to the linear programming formulation of the problem. An alternate and about equally efficient procedure for solving the same problem is also provided.
A Matrix Solution for the Two Server Queue with Overflow
Authors: Disney Ralph L.
Published: November 2008

A complete solution to the two-server Markov queue in which arrivals to the second server are the stream of overflows from the first server is given. It is shown that the solution can be determined from a particular second order difference equation in vectors subject to boundary conditions defined by the problem. An example provides more details. A means of finding various marginal and conditional probabilities is given.
Solution of a Satisficing Model for Random Payoff Games
Authors: Cassidy R. G. , Field C. A. , Kirby M. J. L.
Published: November 2008

In this paper, we consider a “satisficing” criterion to solve two-person zero-sum games with random payoffs. In particular, a player wants to maximize the payoff level he can achieve with a specified confidence. The problem reduces to solving a nonconvex mathematical programming problem. The main result shows that solving this problem is equivalent to finding the root of an equation whose values are determined by solving a linear problem. This linear problem results from maximizing the confidence with fixed payoff level.
The Determination of Partial Moments
Authors: Winkler Robert L. , Roodman Gary M. , Britney Robert R.
Published: November 2008

In statistical decision theory, computations often involve the partial moments of a random variable. Several methods for determining partial moments are discussed, including direct calculation, the use of general formulas which apply to entire families of distributions, and the use of partial moment generating functions. Using these methods, formulas (some involving recursive relationships) are developed for the partial moments of a number of frequently encountered distributions and compared with the corresponding formulas for complete moments.
Rejoinder: Direct Search Method for Finding Negative Cycles
Authors: Florian M. , Robert P.
Published: November 2008

We would like first to stress that we have not “claimed” wide applicability for this method except for the results that we have given. Our motivation for carrying out this work was to find a method of locating and tracing negative cycles to use as a subroutine in Klein's [Klein, M. 1967. A primal method for minimal cost flows with applications to the assignment and transportation problems. Management Sci. 4 (3) 205–220.] algorithm.
Chance-Constrained Linear Programming with Chi-Square Type Deviates
Authors: Sengupta Jati K.
Published: November 2008

The implications of replacing the assumption of normal distribution of the parameters (A, b, c) by chi-square and other related nonnegative distributions are discussed here in the framework of chance-constrained linear programming.
An Interactive Approach for Multi-Criterion Optimization, with an Application to the Operation of an Academic Department
Authors: Geoffrion A. M. , Dyer J. S. , Feinberg A.
Published: November 2008

An interactive mathematical programming approach to multi-criterion optimization is developed, and then illustrated by an application to the aggregated operating problem of an academic department.
The Use of the Delphi Procedures in Manpower Forecasting
Authors: Milkovich George T. , Annoni Anthony J. , Mahoney Thomas A.
Published: November 2008

This paper is a case study in the development, implementation and evaluation of the delphi technique, which systematically makes use of expert judgment in generating manpower forecasts. The study was conducted in a large national retail organization on professional manpower. The results of the delphi technique are compared with results generated by conventional regression based models and the actual experience of the organization, which serves as the criterion. The study also analyzes the Informational elements used by experts during the delphi procedures and develops a model based on these elements. The usefulness of the delphi in generating manpower forecasting models is also discussed.
Some Empirical Tests of the Criss-Cross Method
Authors: Zionts Stanley
Published: November 2008

Randomly-generated linear programming problems of three different types and five different sizes were solved by the criss-cross method and by the simplex method. One hundred problems of each type and size were solved, and the results are overwhelmingly favorable to the criss-cross method. An improvement to the criss-cross method used in these tests is given, and the extension of the results of the paper to variations of the criss-cross and simplex methods is discussed.
Methods for Allocating Urban Emergency Units: A Survey
Authors: Chaiken Jan M. , Larson Richard C.
Published: November 2008

An urban emergency service system provides mobile units (vehicles) to respond to requests for service which can occur at any time and any place throughout a city. This paper describes the common characteristics and operational problems of these systems and surveys the various methods, both traditional and recently developed, which may be used for allocating their units. Aspects of allocation policy discussed include (1) determining the number of units to have on duty, (2) locating the units, (3) designing their response areas or patrol areas, (4) relocating units, and (5) planning preventive-patrol patterns for police cars.

Typical policy changes which may be suggested by the use of quantitative allocation models include selective queuing of low priority calls, varying the number of units on duty (and their locations) by time of day, dispatching units other than the closest ones to certain incidents, relocating units as unavailabilities begin to develop, and assigning police cars to overlapping patrol sectors. As a result of making such changes, it is often possible to reduce queuing and travel time delays, improve the balance of workload among units, and enhance the amount of preventive patrol where needed.
A Program for Research on Management Information Systems
Authors: Mason Richard O. , Mitroff Ian I.
Published: November 2008

An information system consists of, at least, a PERSON of a certain PSYCHOLOGICAL TYPE who faces a PROBLEM within some ORGANIZATIONAL CONTEXT for which he needs EVIDENCE to arrive at a solution, where the evidence is made available through some MODE OF PRESENTATION. This defines the key variables comprising a Management Information System (MIS). It is argued that most research and development to date on MIS has assumed only one underlying psychological type, one class of problem types, one or two methods of generating evidence, and, finally, one mode of presentation. Other states are suggested for all these key variables. The result is the outline of a systematic research program on MIS.
Economic Lot Size Determination in Multi-Stage Assembly Systems
Authors: Crowston Wallace B. , Wagner Michael , Williams Jack F.
Published: November 2008

We consider the optimal lot size problem for multi-stage assembly systems where each facility may have many predecessors but only a single successor. Assumptions include constant continuous final product demand, instantaneous production, and an infinite planning horizon. Costs at each facility consist of a fixed charge per lot and a linear holding cost. Under the constraint that lot sizes remain time invariant, it is proven that the optimal lot size at each facility is an integer multiple of the lot size at the successor facility. This fact is used in the construction of a dynamic programming algorithm for the computation of optimal lot sizes. The algorithm exploits the concept of echelon stock [Clark, A. J., H. Scarf. 1960. Optimal policies for a multi-echelon inventory problem. Management Sci. 6 (4, July) 475–490; Clark, A. J., H. Scarf. 1962. Approximate solution to a simple multi-echelon inventory problem. Chapter 5. K. J. Arrow et al., eds. Studies in Applied Probability and Management Science. Stanford University Press, Stanford, California.].
Optimal Sequencing of a Single Machine Subject to Precedence Constraints
Authors: Lawler E. L.
Published: November 2008

Suppose n jobs are each to be processed by a single machine, subject to arbitrary given precedence constraints. Associated with each job j is a known processing time aj, and a monotone nondecreasing cost function cj(t), giving the cost that is incurred by the completion of that job at time t. The problem is to find a sequence which will minimize the maximum of the incurred costs. An efficient computational procedure is given for this problem, generalizing and simplifying previous results of the present author and J. M. Moore.
A Markovian Analysis of a Geriatric Ward
Authors: Meredith Jack
Published: November 2008

This paper evaluates the cost-effectiveness of a specialized geriatric program in a California mental hospital by analyzing the movement of patients into, through, and out of the program. The procedure for modelling the patients' movements between states with a Markov chain is detailed and the results analyzed. First passage times, long-term trends, and stay times until death are derived as well as the associated costs the hospital must endure. Lastly, ways to simulate the effect of possible program modifications are illustrated.
A Methodology for Emergency Ambulance Deployment
Authors: Fitzsimmons James A.
Published: November 2008

An important feature of emergency ambulance systems is that the probability of any given unit being busy is dependent upon which of the other units are busy. An analytical model incorporating this feature is developed and found to be reliable in predicting the entire response time distribution for an actual operating system. A computer version of the model coupled with a pattern search routine constitutes the ambulance deployment methodology referred to as CALL (Computerized Ambulance Location Logic). This methodology may be used to find the deployment of ambulances that minimizes mean response time, as illustrated by an application for the City of Los Angeles. CALL was used to select from a possible set of 34 firehouses 14 for the location of emergency ambulances. Although the objective was the minimization of mean response time, the final deployment substantially reduced the probability of excessive response times as well. Another ancillary benefit from the use of CALL was a substantial smoothing of workload among the 14 ambulance crews.
Allocation of Importance in a Hierarchial Goal Structure
Authors: Sayeki Yutaka , Vesper Karl H.
Published: November 2008

A weighted additive utility model was used to investigate people's consistency in their allocations of importance and subsequent utility judgments, when a complex goal-subgoal hierarchy was given. In the experiment subjects allocated importance to four policies given each of three objectives and to the objectives under an overall goal. Subjects' task was to evaluate several proposals for a company's future courses of action on the basis of their contribution to the policies, objectives, and overall goal. The results showed that the model described fairly well subjects' consistency in their judgments for importance and utilities, except when they were asked to make judgments for remote, indirect goals.
Semi-Markov Decision Processes with Unbounded Rewards
Authors: Lippman Steven A.
Published: November 2008

We consider a semi-Markov decision process with arbitrary action space; the state space is the nonnegative integers. As in queueing systems, we assume that {0, 1, 2, …, n + N} is the set of states accessible from state n in one transition, where N is finite and independent of n. The novel feature of this model is that the one-period reward is not required to be uniformly bounded; instead, we merely assume it to be bounded by a polynomial in n.

Our main concern is with the average cost problem. A set of conditions sufficient for there to be an optimal stationary policy which can be obtained from the usual functional equation is developed. These conditions are quite weak and, as illustrated in several queueing examples, are easily verified.
Bounds on the Wait in a GI/M/k Queue
Authors: Brumelle Shelby L.
Published: November 2008

The wait in a GI/M/k queue is bounded above (stochastically) by the wait in an associated GI/M/1 queue. The analysis of the single server queue is considerably easier than that of the k-server queue, and also the bounds developed for single server queues by J. F. C. Kingman and Kneale T. Marshall can be applied.
Duality Theory for Infinite Horizon Convex Models
Authors: Weitzman Martin L.
Published: November 2008

Often it is desirable to formulate certain decision problems without specifying a cut-off date and terminal conditions (which are sometimes felt to be arbitrary). This paper examines the duality theory that goes along with the kind of open-ended convex programming models frequently encountered in mathematical economics and operations research. Under a set of general axioms, duality conditions necessary and sufficient for infinite horizon optimality are derived. The proof emphasizes the close connection between duality theory for infinite horizon convex models and dynamic programming. Dual prices with the required properties are inductively constructed in each period as supports to the state evaluation function.
From Statistical Decision Theory to Practice: Some Problems with the Transition
Authors: Conrath David W.
Published: November 2008

While considerable progress has been made in the development of sophisticated statistical decision models, little attention has been paid to the problem of applying them in the “real world.” This paper approaches one aspect of the necessary conversion process, namely the conceptualization and use of probabilistic data for decision making. Three points are demonstrated on the basis of clinical experimental evidence: (1) decision makers have difficulty conceiving of probability distributions, rather they are more comfortable with point estimates; (2) the concept of “risk of failure,” the probability of failing to meet a perceived target, plays a major role in choice; and (3) the format in which probabilistic data are presented can affect choice behavior. A simple descriptive decision model is then developed, based on both the clinical evidence and two years of observation of a committee empowered to invest in new product development and product expansion activities.
Cognitive Style and the Acceptance of Management Science Recommendations
Authors: Doktor Robert H. , Hamilton William F.
Published: November 2008

The effective implementation of management science results depends upon a wide variety of technical and organizational factors. This paper examines the effects of cognitive style in the implementation process. Previous research in cognitive theory and implementation indicates that the nature of the researcher-manager interface may be related to measurable cognitive factors. An experimental study was conducted to investigate the influence of cognitive style and the style of written reports on the acceptance of management science recommendations. Graduate students and practicing managers participated in separate but identical experimental trials. The findings indicate that differences in acceptance rates are due not only to differences in cognitive style, but also to differences in the subject populations.
Multistage Production for Stochastic Seasonal Demand
Authors: Crowston Wallace B. , Hausman Warren H. , Kampe William R.
Published: November 2008

We consider the problem of production planning for a seasonal good which is produced in a multistage manner (e.g., when one or more components must be produced or purchased with a lead time that is long compared to the sales season). During the selling season, lost Bales occur if demand cannot be satisfied; at the end of the season, leftover inventory incurs the usual overage cost. As the season progresses, the forecast of total demand is revised in light of current sales. The problem is to determine production quantities of the various components and assemblies at each period to minimize expected costs of underage and overage. If delivery is not required until the end of the selling (or “order-taking”) season, then a dynamic programming formulation can produce the optimal decision rule. However, for the case in which delivery is required during the season, the associated dynamic programming formulation is computationally infeasible. The paper explores four heuristics for the latter problem and compares their cost performance in a numerical example. The most sophisticated heuristic produces expected profits which range from 3.2% to 5.5% of an upper bound on expected profit.
A Class of Solutions for Group Decision Problems
Authors: Yu P. L.
Published: November 2008

Given a set of utility functions defined on a decision space for a group of individuals, we introduce the concept of utopia point for the group as well as the group regret of a feasible decision. The group regret of a decision varies with a class of distance functions. The class of solutions under study are those which minimize the group regret according to the class of distance functions. We investigate its properties from!he viewpoint of decision rationale. The bounds and monotonicity of the solutions together with their computation are also explored.
Majority Rule Under Transitivity Constraints
Authors: Bowman V. J. , Colantoni C. S.
Published: November 2008

In this paper we are concerned with imposing constraints directly on the admissible majority decisions so as to insure transitivity without restricting individual preference orderings. We demonstrate that this corresponds to requiring that majority decisions be confined to the extreme points of a convex polyhedron. Thus, transitive majority decisions can be characterized as basic solutions of a set of linear inequalities. Through the use of a majority decision function (which is not restricted to be linear) it is shown that constrained majority rule is equivalent to an integer programming problem. Some special forms of majority decision functions are studied including the generalized lp norm and an indicator function. Implications of an integer programming solution, including alternate optima and post optimality analysis, are also discussed.
An Efficient Minimal Cost Flow Algorithm
Authors: Bennington G. E.
Published: November 2008

One of the methods of solving the minimal cost flow problem is to find cycles of negative length in a marginal cost network. These negative cycles indicate a change in the flows around the cycle, which will result in a reduction in the total cost. The proposed method finds negative cycles by attempting to find the shortest chains from a fixed node to all other nodes. Results of computational experiments comparing this algorithm with the out-of-kilter algorithm are presented. These results indicate that the proposed method of repetitively detecting negative cycles yields an efficient minimal cost flow algorithm.
A Linear Decision Rule for Production and Employment Scheduling
Authors: Holt Charles C. , Modigliani Franco , Simon Herbert A.
Published: November 2008

The decision problems involved in setting the aggregate production rate of a factory and setting the size of its work force are frequently both complex and difficult. The quality of these decisions can be of great importance to the profitability of an individual company, and when viewed on a national scale these decisions have a significant influence on the efficiency of the economy as a whole. This paper reports some of the findings of a research team that has been developing new methods to enable production executives to make better decisions and to make them more easily than they can with prevailing procedures. With the cooperation of a manufacturing concern, the new methods have been developed in the context of a set of concrete production scheduling problems that were found in a factory operated by the company.

The new method which is presented in this paper, involves: (1) formalizing and quantifying the decision problem (using a quadratic approximation to the criterion function) and (2), calculating a generalized optimal solution of the problem in the form of a (linear) decision rule. Like a rule of thumb, an optimal decision rule prescribes a course of action when it is applied to a particular set of circumstances; but, unlike most rules of thumb, an optimal decision rule prescribes courses of action for which the claim can be made that the decisions are “the best possible,” the meaning of “best” being clearly specified. The ultimate test, of course, must be whether the new decision methods do or do not outperform prevailing decision methods when full allowance is made for the cost of obtaining the optimal decisions.
Computers as Tools for Management
Authors: Coleman John S.
Published: November 2008

This year the development of electronic digital computing machines has reached the point where American industry can turn from analysis and speculation to actual trial of the effectiveness of these machines in general business use. In these circumstances, it would seem necessary to look to the fundamentals right now, early in the growth period. Most of the business applications of computers, which we have planned up to this point, have led in one direction: greater speed in the processing of business data. To achieve this goal, we have aimed at more and more complete office automation. Probably that was a sound choice of a starting point. For business became interested in the electronic computer when it was pointed out that the machine can effect great savings in the processing of data. Hence this application of the computer was the foot in the door needed to get this industry off to a sound start financially.
An Inventory Policy for a Case of Lagged Delivery
Authors: Beckman M. , Muth R.
Published: November 2008

In this paper we study the question which inventory policy is optimal in the presence of long delivery lags under otherwise simple conditions. The data on which the solution depends are the average demand per day (say), the carrying cost per unit and day, the fixed and per unit costs of ordering, the penalty per day of shortage, and the delivery lag. It will be shown that under certain conditions, mentioned presently, the optimal inventory policy is of the following simple type: order a fixed amount whenever the level of stock plus outstanding orders reaches a certain level. Formulas for the determination of the order size and of the reordering point will be derived.
Derivation of a Linear Decision Rule for Production and Employment
Authors: Holt Charles C. , Modigliani Franco , Muth John F.
Published: November 2008

An application of linear decision rules to production and employment scheduling was described in the last issue of this journal [Holt, C. C., F. Modigliani, H. A. Simon. 1955. A linear decision rule for production and employment scheduling. Management Sci. (October).]. The hypothetical performance of these rules represented a significant improvement over the actual company performance as measured by independent cost estimates and other managerial measures of efficiency. The quadratic cost function which was used should be applicable to production and employment scheduling decisions in many other situations. Also the general approach of approximating decision criteria with quadratic functions and obtaining linear decision rules can usefully be extended to many decision problems.

In the present paper we will demonstrate (a) how optimal (i.e., minimum expected cost) decision rules may be derived for a quadratic cost function involving inventory, overtime, and employment costs, and (b) how the numerical coefficients of the rules may be computed for any set of cost parameters.
General Systems Theory—The Skeleton of Science
Authors: Boulding Kenneth E.
Published: November 2008

In recent years increasing need has been felt for a body of systematic theoretical constructs which will discuss the general relationships of the empirical world. This is the quest of General Systems Theory. It does not seek, of course, to establish a single, self-contained “general theory of practically everything” which will replace all the special theories of particular disciplines. Such a theory would be almost without content, for we always pay for generality by sacrificing content, and all we can say about practically everything is almost nothing. Somewhere however between the specific that has no meaning and the general that has no content there must be, for each purpose and at each level of abstraction, an optimum degree of generality. It is the contention of the General Systems Theorists that this optimum degree of generality in theory is not always reached by the particular sciences.
Management Science in Marketing: Status and Prospects
Authors: Anshen Melvin
Published: November 2008

The practice of administration in marketing has been handicapped by a type of cultural lag. In every phase of marketing operations the application of systematic methodology to the management task has trailed by approximately one generation the experience in the field of production. Recognition of the causes of this lag helps to explain the current state of management science in marketing and will contribute to an understanding of future prospects and opportunities. This understanding has relevance for advances in the science and its application, on one hand, and in management's willingness and ability to use a scientific approach to making marketing decisions, on the other.
On the Theory of Dynamic Programming—A Warehousing Problem
Authors: Bellman Richard
Published: November 2008

In a recent report, [Charnes, A., W. W. Cooper. Generalizations of the warehousing model. O. N. R. Research Memorandum, No. 34, 1955, Graduate School of Industrial Administration, Carnegie Institute of Technology.], Charnes and Cooper present a solution by means of linear programming techniques of one version of what is called the “warehouse problem”. The purpose of this note is to indicate how problems of this general nature may be approached by means of the functional equation technique of the theory of dynamic programming, and thereby reduced to a very simple and straight-forward computational problem.
The Transhipment Problem
Authors: Orden Alex
Published: November 2008

The transportation problem has offered two mathematical facets: (1) as a specialized type of linear programming problem, (2) as a method of representation of some combinatorial problems. In this paper a third aspect of the mathematical properties of the transportation problem is developed. It is shown that the same mathematical framework can be extended beyond pair-wise connections, to the determination of optimum linked paths over a series of points. This extension although viewed here as a linear programming problem, takes advantage of the combinatorial aspect of the transportation problem, and applications may arise which, like the assignment problem, appear to be combinatorial problems, but which can be solved by linear programming.
A Fruitful Application of Static Marginal Analysis
Authors: Karr H. W. , Geisler M. A.
Published: November 2008

This paper describes the application of the technique of simple “marginal analysis” to an Air Force problem and suggests that a similar application may be useful in many industrial situations. Examples of such industrial problems are also presented.
Geographical Distribution of Production in Multiple Plant Operations
Authors: Dillon John D.
Published: November 2008

The primary purpose of this report is to point up the interrelations found among selected conflicting company objectives, the criteria by which one may evaluate alternate course of action indicated by each of the stated objectives, and some methods for reducing similar problems to a reasonable size, susceptible of analysis by available mathematical techniques. The problem discussed herein is a real problem, but in order to protect the interests of the company which sponsored this particular study, the entire report is paraphrased, and numbers such as quantities and dollars are deliberately changed.
Planning Horizons for Production Smoothing with Deterministic Demands
Authors: Kunreuther Howard C. , Morton Thomas E.
Published: November 2008

In Part I of this paper, we develop an algorithm for finding planning horizons for the deterministic production smoothing problem when all demand must be met from regular production, under rather general assumptions for the production, production smoothing, and holding cost functions. (In Part II, planning horizons will be developed when the model is extended to include backlogging and overtime.)

The techniques developed here are essentially forward-looking and marginal-cost-balancing in nature, rather than total-cost-minimizing and backward-looking such as dynamic programming, or total-cost-minimizing and omni-looking in nature such as linear programming. The fact that the planning horizon theorems do not depend on having discount rates less than one illustrates that the approach developed here is fundamentally different from ordinary infinite horizon dynamic programming techniques.

The algorithm is “user oriented” in the sense that only a small amount of forecasting work and computation ordinarily must be done to determine the horizon; the nature of the algorithm also makes the exact dependence of the horizon on the forecast clear for sensitivity analysis. Firms facing a seasonal demand pattern will often find that the horizon occurs within the first several periods after the peak period if there is a sufficient enough drop in demand afterwards. This result complements the findings of Modigliani and Hohn and provides insight into the nature of the optimal policy for stochastic planning problems.
Dynamic Lot Size Models for Multi-Stage Assembly Systems
Authors: Crowston Wallace B. , Wagner Michael H.
Published: November 2008

A multi-stage assembly system is a special case of Veinott's general multi-facility system in that each facility may have any number of predecessors but at most a single successor. This paper presents two algorithms for computing optimal lot sizes in such systems with known time-varying demand. The first is a dynamic programming algorithm for which solution time increases exponentially with the number of time periods, but only linearly with the number of stages, irrespective of assembly structure. The second is a branch and bound algorithm intended for cases where the number of time periods is large but the structure is close to serial. Computational results are given and extensions considered.
An Iterative Procedure for the Single-Machine Multi-Product Lot Scheduling Problem
Authors: Doll C. Loren , Whybark D. Clay
Published: November 2008

Most of the procedures that have been developed to find solutions to the single-machine, multi-product lot scheduling problem depend on judgment to define the desirable frequencies of production for the products. In this paper we describe an iterative procedure for directly determining near optimal frequencies of production for the products and the associated fundamental cycle time which, in many cases, can be used directly for constructing production schedules. In cases where feasible schedules cannot be constructed using the values from the iterative procedure, the procedure provides a basis for changing the production frequencies and the fundamental cycle time to obtain feasible schedules.
An Integer Programming Algorithm for Portfolio Selection
Authors: Faaland Bruce
Published: November 2008

A mean-variance portfolio selection model suitable for the small investor is formulated as a sequence of quadratic integer programming problems. The special structure of these quadratic problems is exploited in a partial enumeration algorithm which uses cutting planes to accelerate convergence. Computational experience is reported on problems ranging in size from fifteen to fifty variables.
Multi-Attribute Utility Models: A Review of Field and Field-Like Studies
Authors: Huber George P.
Published: November 2008

This article contains a review of published field and field-like research studies concerned with the development and use of multi-attribute utility models. Since the clients or experimental subjects who participated in these studies were all occupationally experienced in making the utility judgments required of them, it is felt that these particular findings would be useful in the practice of management science. Multi-attribute utility models are models designed to obtain the utility of items or alternatives that must be evaluated on more than one criterion. The additive and multiplicative model forms are examined, and methods for estimating the model parameters are presented.
Surveillance Schedules for Medical Examinations
Authors: Kirch R. L. A. , Klein M.
Published: November 2008

A model is developed to aid in determining examination schedules for the detection of an age-dependent disease. It is shown that in an optimal schedule the interval between examinations is proportional to the square root of the age-specific incidence probability of the disease. To illustrate the methodology, optimal schedules are developed for breast cancer examinations.
Exceptional Paper—Lexicographic Orders, Utilities and Decision Rules: A Survey
Authors: Fishburn Peter C.
Published: November 2008

Proceeding from a unifying framework, this paper reviews recent developments in lexicographic orders, preferences, utilities, probabilities and decision rules. Several new results are included.
Organizational Structure and Technology: A Computer Model Approach
Authors: Gerwin Donald , Christoffel Wade
Published: November 2008

The explanation of organizational structure has always been an important issue to administrative theorists. This paper investigates the structure of manufacturing operations and therefore starts from the premise that technological factors are most crucial. One current approach to structure and technology, comparative analysis, has studied the overall context but at a highly aggregative level. The “constituent” approach, on the other hand, has investigated intraorganizational relationships but at the expense of empirically verifiable theory. We develop a heuristic computer model capable of testing existing constituent theories. The model uses work flow data as technological inputs and produces a structural configuration as output. It employs administrative rules (based on Thompson's propositions for structuring the technical core) to convert inputs into outputs. The inputs, outputs, and rules are formally represented using list processing concepts. Operationalizing the formal concepts has been satisfactorily explored in a pilot study of a small manufacturing firm. Testing existing constituent theory is just the initial phase of this project. It is possible to develop new structural theory for the technical core and the rest of the organization, and to engage in a form of comparative analysis to judge the effects of varying contexts. Moreover, the fact that a structural configuration is produced implies utility in the area of design.
A Goal Programming Approach to Aggregate Planning of Production and Work Force
Authors: Goodman David A.
Published: November 2008

This paper presents a goal programming approach to the problem of scheduling aggregate production and work force. The approach is illustrated via two case applications. Numeric results are derived and compared to those of two other approaches. The results suggest that goal programming may be an effective approach for solving certain types of planning models.
The Political Properties of Crystalline H2O: Planning for Snow Emergencies in New York
Authors: Savas E. S.
Published: November 2008

A major snowstorm produced a serious snow emergency and a political crisis in New York City. Mayor Lindsay asked for an analysis of the problem to prevent its recurrence. His management science unit studied four basic questions: (1) How much snow falls on New York City? (2) How much work has to be done to clean it up? (3) What is the capacity for performing this work? (4) What improvements are needed to correct any imbalance between work load and work capacity?

The surprising findings eliminated most of the remedies that had been offered earlier. A low-cost snow emergency plan was prepared, calling for some additional equipment, better deployment, and a strategy for more rapid mobilization. The recommendations were implemented promptly, and with due regard for political impact, in the midst of a mayoralty election campaign.
Airline Seat Share: A Study in False Optimization
Authors: Barcun Seymour D. , Jeming Peter
Published: November 2008

An interactive model is developed which correctly predicts the problems faced by the airline industry today. The function of the model is to simulate the decision making process of determining the number of seats (and therefore planes) a competitor will fly in a given “market.” (A market is a city pair, e.g., the Washington-Chicago market.) This model of competition in a regulated industry considers the interaction of seat share, market share and profitability as major determinants of policy.

Simulated data is presented for three cases in a two-competitor market. Case 1 supposes this model is used to determine policy only by one airline. Case 2 assumes the model is used by both competitors, but neither will fly a market if losses are encountered. Case 3 assumes that the model is used by both competitors, and losses will be taken to insure a favorable market share for future profitability.
Mail-Order Demands for Style Goods: Theory and Data Analysis
Authors: Hausman Warren H. , Sides Richard St.G.
Published: November 2008

A number of alternative data-generating processes are explored for mail-order demands for seasonal style-goods. Weekly demands for 126 items over an 18-week selling season provide the empirical data. Arguments are presented which result in the following candidates for data-generating processes: (1) ratios of successive forecasts are distributed lognormally; (2) ratios of successive forecasts are distributed as t (Student); (3) actual demands during unequal time periods are distributed as negative binomial. Analysis of the data suggests the negative binomial data-generating process as both most closely representing the underlying process and being simple to adapt to a decision model. The paper concludes with an example of the use of the chosen data-generating process in a decision framework, and deals briefly with some issues of implementation.
A New Algorithm for Locating Sources Among Destinations
Authors: El-Shaieb A. M.
Published: November 2008

The problem considered is the location of m sources among n locations (m < n) and the assignment of the location requirements to the sources in order that the total transportation cost is minimized. A branch and bound algorithm is developed to achieve an optimal solution. Two lower bounds are developed for the algorithm; one is efficient for small problems while the other is more efficient for larger problems. An illustrative example is given, and a detailed application of the algorithm to the thirty largest metropolitan centers in the United States is presented for various numbers of destination (10, 20 and 30) and sources (2, 4 and 6). The branch and bound algorithm was coded in Fortran IV and tested on a number of problems. Some of the example problems that were tested are included in this paper.
Nested Decomposition and Multi-Stage Linear Programs
Authors: Glassey C. Roger
Published: November 2008

A multi-stage linear program is defined with linking variables that connect consecutive stages. Optimality conditions for the composite problem are partitioned into local and linking conditions. When the Dantzig-Wolfe decomposition scheme is applied with the first stage as the master, the subproblem is also a MLP with one' less stage. The same decomposition is then applied to the subproblem, giving rise to a nested decomposition scheme, in which each stage acts as a master for the following stage and a subproblem for the preceding. Optimizing a single stage problem results in satisfying the “local” optimality conditions. A very general rule is given for selecting the next subproblem to optimize, and finite convergence to a solution satisfying all linking conditions is demonstrated. Procedures for extracting the optimal primal solution at the end of the main algorithm and for initialization are given. Particular rules for selecting the next subproblem and for generating additional proposal vectors are discussed. Finally, it is shown how a variety of problems may be restructured as multi-stage linear programs to which this algorithm may be applied, and some computational experience is reported.
Sequential Policies for Bank Money Management
Authors: Pye Gordon
Published: November 2008

Banks must hold an average deposit balance in the Federal Reserve Bank each week. Surplus or deficit balances may be traded on the Federal funds market. The rate on Fed funds behaves as a random variable over the week as does the size of the surplus or deficit to be offset. Incorporating both these sources of uncertainty, sequential minimax and expected utility maximizing strategies are derived for offsetting the surplus or deficit over the week.
Management Science in the Developing Countries: A Comparative Approach to Irrigation Feasibility
Authors: Rose J.
Published: November 2008

The paper describes an Irrigation Feasibility Study carried out for a Developing Country. The area considered comprised some 1,000,000 acres of land in Burma and the study was undertaken for the Burmese Government under the auspices of the United Nations. The solution technique used was Mixed Integer Programming, there being approximately 750 continuous variables, 50 integer variables and 250 constraints. The study is of particular interest in that the problem was solved both by conventional methods and by Operational Research methods independently. Therefore, a complete comparison of the relative merits of both approaches exists and is discussed here.
Reduction Algorithm for Zero-One Single Knapsack Problems
Authors: Ingargiola Giorgio P. , Korsh James F.
Published: November 2008

A simple algorithm is described for the reduction of 0-1 single knapsack problems to significantly smaller problems. The time required by the reduction algorithm is proportional to the number of variables. When used in conjunction with available algorithms for knapsack problems it substantially reduces total time and space requirements.
Estimation of Rural Demand for Natural Gas
Authors: Tinic S. M. , Harnden B. M. , Janssen C. T. L.
Published: November 2008

The Government of Alberta has undertaken a study to determine the feasibility of supplying natural gas to rural areas of the Province not currently served. The study covers various aspects relevant to the development of an overall plan for rural gasification, but the emphasis is on major factors affecting the economic viability of the venture. We discuss in this paper one segment of the study—the problem of determining the demand for natural gas in rural areas. Three separate demand schedules, each based on a different methodology but using common data obtained from a survey of rural residents, are estimated. The three demand schedules are analysed and compared in an attempt to establish an overall demand curve for natural gas in rural Alberta. The price elasticity of demand for natural gas is determined from the demand function obtained.
A Dynamic Programming Model for the Expansion of Electric Power Systems
Authors: Petersen E. R.
Published: November 2008

This paper describes a dynamic programming model that has been developed to determine an optimal expansion plan for the generating capacity of an electric power system. The optimization model determines the least-cost mix of capacity between hydro, nuclear, thermal and peaking turbine plants, the size of the plants to add to the system, and the timing of these additions. We show how the computational requirements of this four-state-variable, four-decision-variable problem can be substantially reduced, resulting in a computationally feasible model. The techniques developed are applicable to a large class of capital budgeting problems under uncertainty. Reference is also made to the actual application of the model and an example is presented.