bluuebunny/crossref_metadata_2025_split huggingface dataset:
Planning Models
Authors: Starr Martin K. 
Published: November 2008

At present, the term planning has many meanings and little substance. It is an omnibus word or a dressing gown under which a variety of management matters masquerade. So many management activities are described as planning that the significance of the term may have succumbed to generality. The planning function appears to be a status generator, presumably because planning is closely associated with top managements' interests. It is surrounded with emotional issues furthermore, that help to obscure the difference between what we say we are doing (i.e., planning) and what we are doing (?). Although planning models are the subject of this paper, from the viewpoint of consistent terminology and unambiguous definition they don't exist. On the other hand, as judged by common usage, most management science models qualify as planning models. At the uppermost entrepreneurial levels planning is almost exclusively an intuitional affair. But characteristically these domains are dramatically unstable. No maps exist for traveling in such regions. Usually, even the fundamental dimensions are unknown. What planning models have been built to cope with these real and vital top-management problems? Search and survey answers, none. Consequently, a paper about planning models can either continue as in present practice to ignore critical issues, or can begin with simple classification—more or less as Linnaeus started back in the 1700's. We have chosen the latter course and begin this paper by attempting to develop a workable taxonomy.
Resistance Against Analysis and Planning in Research and Development
Authors: Krauch Helmut 
Published: November 2008

Many current reflections on science and technological development seem to be concentrated upon the notion that these processes are not subject to man's conscious control. But, instead of demanding an analysis of research itself, from which conscious control might ultimately be expected, they focus their attention on the consequences of scientific activity. They recommend the scientific comprehension of the results of science, on the ground that only thus can we save ourselves from calamitous consequences. A feeling of helplessness and resignation in the face of the apparent autonomy of technological development is quite common. Its origins may reach far back. With Heraclitus, we feel that war is “the father of all things,” and reach the height of our pessimism in Spengler's Kulturkritik. “The master of the world becomes the slave of the machine.” This attitude of resignation prevails among large numbers of educated Germans, who persist in closing their eyes to the connections between scientific development and social factors.
Minimum-Cost Checking Using Imperfect Information
Authors: Pollock Stephen M. 
Published: November 2008

An event takes place at time t, a discrete random variable with known probability function. At unit intervals of time, a measurement x is observed which yields information about the event; x is a random variable, with a known probability density function being dependent upon whether or not the event has yet occurred. After each observation, a decision is made that the event has or has not yet occurred. The latter decision implies waiting for the next measurement. The former decision, if correct, ends the procedure. If incorrect, this fact is incorporated, and the procedure continues. A decision cost structure is assumed that assigns: (1) a fixed (false alarm) cost to deciding the event has occurred when, in fact, it has not; (2) a (time late) cost proportional to the time between the occurrence of the event and the decision that it has occurred. The minimum-expected-cost decision strategy and the minimum cost thus obtained are derived by means of dynamic programming.
The Analysis of Simulation-Generated Time Series
Authors: Fishman George S. , Kiviat Philip J. 
Published: November 2008

This study applies spectral analysis to the study of time series data generated by simulated stochastic models. Because these data are autocorrelated, analysis by methods applicable to independent observations is not possible. Mathematical models known as covariance stationary stochastic processes are useful representations of autocorrelated time series. The increased publication of literature describing stochastic processes and spectral analysis, in particular, is making these ideas available to an increasing audience. Section I presents a rationale for our interest in time series models and spectral analysis. Section II describes the basic notions of covariance stationary processes. It emphasizes the equivalence of these processes in both the time and frequency domains; the compactness of frequency domain analysis seemingly recommends it over correlation analysis. Section III provides a heuristic background for understanding statistical spectral analysis. Simple frequency-domain statistical properties are emphasized and compared with the rather involved sampling properties of estimated correlograms. Several relevant statistical tests are described. Three simulated experiments are used as examples of how to apply spectral analysis. These are described in Section IV. They are (1) a single-server, first-come-first-served (FCFS) queueing problem with Poisson arrivals and exponentially distributed service times; (2) a similar model with the FCFS assignment rule replaced by one that chooses the job with the shortest service time; and (3) yet another similar model, but with constant service time. In this third example both assignment rules are equivalent. The state of the queue was observed and recorded at unit intervals for all three examples and forms the time series on which Section V is based; theoretical and statistical correlograms and spectra are compared for example 1; examples 1 and 2 are compared using estimated correlograms and spectra; example 3 serves to show some unusual properties of spectra. A particular conclusion of this paper is that differences in the statistical properties of queue length that result under various assumptions and operating rules are easily identified using spectral analysis. More generally, however, this estimation procedure provides a tool for (1) comparing simulated time series with real-world data, and (2) for understanding the implications that various alternative assumptions have on the output of simulated stochastic models.
Implementation of Management Science in Latin America: A Case Study and Generalization
Authors: Loomba N. Paul , Turban Efraim , Cohen Alvin 
Published: November 2008

Private and public programs designed for rapid industrialization and for raising living standards have caused a growing demand in Latin America for persons trained in management science. Current sources for such training include special “productivity centers,” departments or institutes of management science at various Latin American universities, technical personnel of North American firms, and professional academicians of North American educational institutions. If the level of private business between North and South America increases, it would be quite reasonable for North American firms to assume a greater responsibility for the transfer of management science techniques to Latin America. This paper describes an actual case study of the application of management science to the business and operational problems of a Latin American meat processing firm. Located in Paraguay, the firm is a subsidiary of a large multi-management company—Ogden Corporation. In conjunction with the local management, the study was conducted by Ogden's department of Operations Research. The following aspects of the Latin American firm were analyzed for possible application of management science techniques: (1) organization structure, (2) ranch operations, (3) product mix problems, (4) investment analysis, and (5) inventory control. Based on Ogden's experiences in a typical Latin American business setting, we attempt here to describe some of the present problems and the future potential for management science applications in Latin America. Major problems of implementation identified in this case and considered typical to Latin America are: (1) lack of qualified personnel, (2) lack of EDP facilities, and (3) existence of traditional cultural and political barriers to scientific approaches. However, because of a rising technological function, management science application offers a fertile territory in Latin America.
Regional Rain Models with Space- and Time-Correlated Structure
Authors: Sorensen Eric E. 
Published: November 2008

Many activities combining the interactions between man and machine are influenced by a third factor of weather. The sensitivity analysis required to explore the complicated effects of weather is important in evaluating situations when man-machine strategies are constructed to arrive at optimal operations. The influence of rain on the harvesting of crops is one such situation. A generalized model has been constructed which will simulate rainfall over regions containing gradient extremes of annual rainfall of 5 inches to 50 inches within the region for any given year. The specific application covered a region 5 miles by 14 miles. We believe that much larger regions can be represented in the same manner. The model will generate daily rain patterns of a spatial and temporal sequence correlated from over a large number of sub areas (fields or farms) within the region. Daily isohyets (equal rainfall contour lines) can be constructed from simulated computer periods of an unlimited time horizon. The model can interact with other models (for example, harvesting and irrigation models) in order to analyze the sensitivity of these models to rainfall incidence, rainfall severity and duration.
Information Structures in Stochastic Programming Problems
Authors: Miyasawa Koichi 
Published: November 2008

Problems of multi-stage decision under uncertainty are usually classified into “stochastic” and “adaptive” ones, depending on whether the decision maker does or does not know the relevant probability distribution. If the Bayesian approach is taken, then, in the adaptive case, the decision maker is assumed to know the prior distribution of certain parameters. It is shown in the paper that the adaptive case is then reducible to the stochastic one. The problems can also be classified according to the kind of information (memory) available to the decision maker. In the paper, optimal policies and the expected gains they yield, are determined for some important classes of “information structures.” In cases in which added information does not increase the expected gain, a sufficient information structure is specified.
Optimization and Suboptimization: The Method of Extended Models in the Non-Linear Case
Authors: Bessiere F. , Sautter E. A. 
Published: November 2008

Let us consider some particular optimization problem. Most of ita data is determined by the economic “environment”. However, this environment is evolutive, and may be the subject of an optimization research. The particular problem first considered then appears to be a suboptimization, and its results will be valid only if it is separable from its environment. This point of view can be expressed in mathematical terms by considering a “principal” programming problem from which one tries to separate a “reduced” subproblem. The concept of “separability” is defined and studied in the first part of this paper. In the second part, it is used to justify the “Method of Extended Models” as a tool for building optimization models.
Discrete Stochastic Programming
Authors: Cocks K. D. 
Published: November 2008

A method is presented for solving linear programming problems where (any number of) the functional, restraint, and input-output coefficients are subject to discrete; probability distributions. The objective function is formulated in terms of variance and/or expectation. The procedure involves the simultaneous generation of all (mutually exclusive) possible outcomes and hence the transference of all variability into the objective function of a very much enlarged linear program.
Operational Gaming Simulation with Application to a Stock Market
Authors: Barish Norman N. , Siff Frederick H. 
Published: November 2008

This paper presents a concept of operational gaming with important new features and describes its implementation. One of these new features is the incorporation in the model of computer generated play whose character is determined by the action of the players as well as computer programmed stochastic characteristics. The paper 1. Presents the concept of operational gaming simulation and its potential usefulness. 2. Describes what has been done to implement this concept in one area of application for economic research and instruction. 3. Pictures this application to a stock market in sufficient detail so that persons who might be interested in using this model for research and/or instructional purposes are aware of its nature and availability. The development of the model into a usable instrument is described and the procedures being used to give it realism and validity are discussed during the course of this presentation.
Investment Analysis Under Uncertainty
Authors: Wilson Robert 
Published: November 2008

Investment projects are described in this paper as a pattern of uncertain cash flows over time; i.e., as a cash flow pattern over an event tree. Whereas the portfolio problem of selecting projects generally requires simultaneous consideration of all available projects, here we seek sufficient conditions for accepting and rejecting individual projects. These conditions are formulated as mathematical programming problems which are amenable to routine application at subordinate levels of an organization. “Investment is, in essence, present sacrifice for future benefit. But the present is relatively well known, whereas the future is always an enigma. Investment is also, therefore, certain sacrifice for uncertain benefit.”1
A Financial Simulation for Risk Analysis of a Proposed Subsidiary
Authors: Economos A. M. 
Published: November 2008

This paper describes a model for a computer stimulation designed to analyze and forecast the financial posture of a computer leasing company as a subsidiary to a large corporation. The simulation considers the rate of market growth, the year of the introduction of fourth generation hardware and the year that the computer manufacturers act to impede the growth of the leasing industry, as random variables. Other assumptions are input as equations or constants. Financial analyses are performed during a five year simulation period. The means and the 10% and 90% quantile range for several key financial indicators are determined and plotted.
The Consensus of Subjective Probability Distributions
Authors: Winkler Robert L. 
Published: November 2008

“‘But we can't agree whether A or B is correct,' he concluded, ‘and so we're collecting expert opinions, weighting them appropriately, and programming WESCAC to arbitrate the whole question.’” (John Barth, Giles Goat-Boy, p. 664.) In the Bayesian framework, quantified judgments about uncertainty are an indispensable input to methods of statistical inference and decision. If a decision maker has little knowledge with regard to the parameters of interest, he may decide to consult a number of experts and obtain their quantified judgments in the form of subjective probability distributions. If this is the case, the decision maker must somehow combine the distributions assessed by the experts and form a single distribution to be used as an input to a formal Bayesian analysis. Several methods for combining the distributions are suggested, some involving mathematical formulae and some involving feedback and/or group discussion. These methods are compared under certain assumptions regarding the form of the distributions and also under experimental conditions.
A Lock-Box Model
Authors: Stancill James McN. 
Published: November 2008

Faced with the problem of trying to reduce investment in accounts receivable, a number of corporate finance managers have directed attention to the problem of undesired “float,” i.e., the dollar amount of checks payable to the subject firm but unavailable for cashing because they are in process of transmittal via the mail system. One of the most direct ways to reduce such undesirable “float” is through the use of what is called a “lock-box” arrangement. “Lock-box” is the popular term applied to an arrangement whereby a firm in one area directs their customers in another (distant) area to send their checks to a post-office box in the customer's area (city). Arrangements are made with a bank in the selected city to withdraw the checks, cash them and wire-transfer the funds to the firm's principal bank. This article presents a decision rule model for the establishment of a lock-box. The form of the model is an inequality and delineates the relevant variables. While specific lock-box arrangements with banks vary, the model presents the variables involved in the two most popular arrangements. Use of this model would permit a finance manager to appraise the profitability of possible lock-box installations.
Multi-Product Scheduling in a Chemical Plant
Authors: Eilon Samuel 
Published: November 2008

Details are given of a chemical plant consisting of four machines, each capable of producing one of several products at a time. Five products are to be produced, each subject to a normal demand distribution with known parameters. The rates of production, costs, profit margins, setup times and machine capacities are given. The study compares two production scheduling methods, the first (Method I) is based on linear programming (two variants of this method are considered) and the second (Method II) on a multi-product batch scheduling approach. A simulation of the plant for a variety of demand conditions and for several safety-stock policies was carried out. The results suggest that Method I yields somewhat better plant performance for the case when average demand is below plant capacity, but neither method showed superiority under all conditions, and in many cases the difference between the results for the two methods was only marginal.
Lease-Buy Planning Decisions
Authors: Weekes Paul A. , Chambers John C. , Mullick Satinder K. 
Published: November 2008

A company with a long standing practice of leasing equipment may amass, over a period of time, a complicated structure of leasing arrangements, both long-term and short-term. Due to changes in costs of leasing, purchase prices, etc., there should be frequent evaluations of whether the existing strategy is optimal. The usual approach is to perform a “macro-steady-state” analysis, where cash flows and/or return-on-investment calculations are performed. However, some macro-analyses indicate only which choice is preferable and do not take into account either the possibility of a mixed strategy (own some and lease some) or implementation problems and the resulting costs that occur when converting from one system to another. Furthermore, if there is to be a conversion from leased to owned equipment, additional burdens will be placed on the maintenance department. An overall planning model has been developed which yields an optimum conversion schedule and the cost of conversion. The optimal solution can be updated over time if there are changes in leasing and purchasing costs, taxes, interest rate and maintenance costs. Various assumptions are discussed, along with the inputs, the model structure, and the use of a matrix generator. The specific example considered is the problem relating to leasing or purchasing railroad equipment for transporting finished products to the customer.
Optimal Inventory Policy with Multiple Set-Up Costs
Authors: Lippman Steven A. 
Published: November 2008

We consider a deterministic, single product, discrete review, finite time horizon inventory problem, called the multiple set-up cost problem. The holding cost in each period is a nondecreasing (and sometimes concave) function. The distinguishing feature of our model is the ordering cost function which is neither concave nor convex. The ordering cost is such that a natural interpretation of it consists in assuming that the order in period i is delivered in trucks with capacity Mi and that the cost of delivery for each truck is a nondecreasing concave function of the amount delivered by that truck. We establish the existence of an optimal production schedule such that for each period (1) there are no partially filled trucks in period i if the inventory entering period i is positive and (2) the inventory at the end of period i is less than Mi. Exploiting this information, an efficient algorithm is developed. In part II, we study the stationary, infinite horizon version of the multiple set-up cost problem. We single out a countable set S of schedules, each of which possess a periodic property in addition to properties (1) and (2) above, and we show that S contains a schedule with minimal cost per unit time. Moreover, if the ratio of demand per period to Mi is rational, then S contains a schedule with minimal discounted cost.
Artifacts in the Siegel-Fouraker Study of Bargaining and Group Decision Making
Authors: Swinth Robert L. 
Published: November 2008

Siegel and Fouraker (1960) hypothesize and purport to show experimentally that players in bilateral monopoly will tend toward contracts at the Paretian optimum quantity (P.O.) under conditions of incomplete information (players know only their own payoff functions), even though the theory implicitly assumes complete information (both players know both functions). Their results contain artifacts which severely limit the generality of any inferences that might be drawn from them. The theory prediction that players will tend toward P.O. must be based on the explicit, not implicit, assumption of complete information. In this paper it is demonstrated that under conditions of incomplete information it is very likely that players will tend toward contracts at the P.O. only when it can be assumed that they meet the very restrictive condition of behaving like perfect competitors. Also, in an experiment measuring the degree to which players did in fact bid along their marginal functions, some 48% of the variance in behavior was explained by this model.
Elements of Large-Scale Mathematical Programming Part I: Concepts
Authors: Geoffrion Arthur M. 
Published: November 2008

A framework of concepts is developed which helps to unify a substantial portion of the literature on large-scale mathematical programming. These concepts fall into two categories. The first category consists of problem manipulations that can be used to derive what are often referred to as “master” problems; the principal manipulations discussed are Projection, Inner Linearization, and Outer Linearization. The second category consists of solution strategies that can be used to solve the master problems, often with the result that “subproblems” arise which can then be solved by specialized algorithms. The Piecewise, Restriction, and Relaxation strategies are the principal ones discussed. Numerous algorithms found in the literature are classified according to the manipulation/strategy pattern they can be viewed as using, and the usefulness of the framework is demonstrated by using it (see Part II of this paper) to rederive a representative selection of algorithms. The material presented is listed in the following order: The first section is introductory in nature, and discusses types of large-scale problems, the scope of discussion and the literature, and the notation used. The second section, entitled “Problem Manipulations: Source of ‘Master’ Problems” covers the subjects of projection, inner linearization and outer linearization. The third section, “Solution Strategies: Source of ‘Subproblems’,” discusses piecewise strategy, restriction and relaxation. The fourth section is entitled “Synthesizing Known Algorithms from Manipulations and Strategies,” and is followed by a concluding section and an extensive bibliography.