bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assembly Line Balancing Using Probabilistic Combinations of Heuristics
Authors: Tonge Fred M. 
Published: November 2008

Several recent research efforts have studied the use of probabilistic schemes for generating solutions to particular combinatorial problems. Characteristically, such schemes include both rapid production of many potential solutions, through repeated application of simple choice rules, and dynamic modification of the production process (“learning”). In this study the simple choice rules (heuristics) used are derived from earlier applications of heuristic programming to the assembly line balancing problem. Repeated trials with probabilistic choice of simple rules are here viewed as an alternative method of organizing the appropriate selection and application of these heuristics. The procedure assigns tasks to work stations along the assembly line by randomly selecting a heuristic for choosing the next task to be added to the current work station. While many suitable heuristics for assembly line balancing have been suggested in the literature, our studies have primarily used the following: choose the task with the largest operation time; choose the task with the most immediate followers; choose a task randomly. The results for studies of several problems, and several cycle times of each problem, can be summarized as follows. Random selection of heuristics for choosing the next task does as well as or better than (i.e., results in fewer work stations than) either use of the individual heuristics alone, or random choice of the tasks directly, without intervening choice of heuristics.
Intention and Performance: A Primer of Cybernetics of Planning
Authors: Greniewski Henryk 
Published: November 2008

It is claimed that the object of a plan is always a relatively isolated prospective system: the goal is a certain state of the output; the means are the states of the controlled inputs; and the circumstances under which the goal is to be attained are the states of the uncontrolled inputs. The relationships as between the goal, the means, and the circumstances are analyzed; planning is given a game theoretical interpretation; the implementation of a plan is a two-person game, the planner, and the circumstances being the two players. The concepts of a revision of the plan, the logical structure of the plan, and the various types of the models of planning are also discussed.
Optimal Programming of Lot Sizes, Inventory and Labor Allocations
Authors: Dzielinski Bernard P. , Gomory Ralph E. 
Published: November 2008

The economic lot size programming problem, as studied originally by A. S. Manne and later by B. P. Dzielinski, C. T. Baker and A. S. Manne, is the problem of making economic lot size, inventory and work force decisions in a multi-production process. When several thousand distinct items are involved, the large number of equations that result from the linear programming formulation makes computation infeasible. Also, a large number of variables are involved because of inclusion of alternative set-up sequences for each item. In this paper, the application of the Dantzig and Wolfe decomposition principle and a method for creating alternative set-up sequences as they are needed by means of a computation of the Wagner and Whitin type is described as a method for overcoming the computational difficulty. A digital computer program has been developed using these methods. The results of some experiments where production was planned for a large number of distinct items are described.
A Bayesian Analysis of the Style Goods Inventory Problem
Authors: Murray George R. , Silver Edward A. 
Published: November 2008

A style goods item has a finite selling period during which the sales rate varies in a seasonal and, to some extent, predictable fashion. There are only a limited number of opportunities to purchase or manufacture the style goods item, and the cost, in general, will depend on the time at which the item is obtained. The unit revenue achieved from sales of the item also varies during the selling season, and, in particular, reaches an appreciably lower terminal salvage value. Previous work on this class of problem has assumed one of the following: (a) There is a known deterministic sales rate during the season. (b) The sales are generated by a stochastic process having exactly known probabilities. Both of these formulations of the problem overlook what we believe to be an essential feature—that initially we have a great uncertainty concerning the sales potential of the item but have an opportunity to develop a better forecast as actual sales become known. The model discussed in this paper treats the sales potential of the item as a subjective random variable whose distribution is changed adaptively (through the use of Bayes' Rule) as the sales history unfolds. We examine several methods for encoding the prior knowledge that lead to a computationally feasible dynamic programming solution to the problem. An interesting feature of this approach is that the dynamic programming state represents both the unsold inventory of the item and the current state of knowledge concerning the sales potential. Numerical examples are discussed, showing the importance of formulating the problem, in a way that includes a state of knowledge that adaptively improves as demand becomes known. Finally, computational shortcuts that make problems of a larger scale computationally feasible are discussed.
The Bond Refunding Decision as a Markov Process
Authors: Bierman Harold 
Published: November 2008

Corporations that issue long-term debt are frequently faced with the opportunity to call the bonds and issue new bonds at more advantageous interest rates. When the current interest rate falls below the contractual interest rate on the outstanding debt, then computations must be made to evaluate the desirability of the refunding decision. This paper investigates the possibility of introducing the probabilities of different interest rates in the future, thus considering the desirability of postponing the refunding as well as the desirability of refunding now. There are four basic steps in the analysis of the decision whether or not to refund a currently outstanding bond issue prior to its maturity date.These steps are: a. the computation of the relevant cash flows (or the relevant costs), b. the choice of the rate of discount to be used in making the analysis, c. the treatment of uncertainty, d. the incorporation of qualitative factors.
Is TIMS Talking to Itself?
Authors: Heany Donald F. 
Published: November 2008

The thesis of this article is that a gap currently exists between “managers” on the one hand and many “scientists” doing research in and on business under the banner of Management Science. It is because of this gap that I believe TIMS is talking to itself. As long as this “TIMS gap” endures, it will be difficult for this organization to attain its stated objectives. This TIMS gap is understandable but quite unnecessary. It is less the result of inexorable forces than the unintended by-product of an overly narrow view of management science. This TIMS gap should be an embarrassment to an organization bearing the name of “The Institute of Management Sciences.” With such a view, a stranger might expect to find managers active in our sessions and in our councils. The facts are different. Managers are staying away in droves. They are literally repelled by what they find classified under the label “management science.” Their continual absence deprives our organization of contributions some of the founders of TIMS valued as most important.
On Nonterminating Stochastic Games
Authors: Hoffman A. J. , Karp R. M. 
Published: November 2008

A stochastic game is played in a sequence of steps; at each step the play is said to be in some state i, chosen from a finite collection of states. If the play is in state i, the first player chooses move k and the second player chooses move l, then the first player receives a reward akli, and, with probability pklij, the next state is j. The concept of stochastic games was introduced by Shapley with the proviso that, with probability 1, play terminates. The authors consider the case when play never terminates, and show properties of such games and offer a convergent algorithm for their solution. In the special case when one of the players is a dummy, the nonterminating stochastic game reduces to a Markovian decision process, and the present work can be regarded as the extension to a game theoretic context of known results on Markovian decision processes.
The Ethics of Rational Decision
Authors: Boulding Kenneth E. 
Published: November 2008

In recent years a great deal of attention has been paid by social scientists and others to the problem of the theory of decisions. The decision is a basic concept of social systems, especially in social dynamics. It represents perhaps the most important single class of events, an event being defined as a kind of step-function which separates one position of a social system from the next in point of time. Some events are not the result of human decision at all, such as aging or accidents; some are the results of previous decisions, such as depreciation of a previously created capital structure; but when all these are removed, there remains an important category of events which constitute a deliberate change in state of a social system as a result of human action. Address presented at the 27th National Meeting of the Operations Research Society of America, Boston, Massachusetts, May 7, 1965.
Some Factors Involved in Determining Total Decision Success
Authors: Trull Samuel G. 
Published: November 2008

Currently there is a trend toward attempting to understand the mechanics involved in heuristic decisions, or decisions where the lack of a measure or index of effectiveness, coupled with a surfeit of alternatives and an absence of reliable probabilities associated with decision variables, leads to an open or non-computable decision process. In an attempt to obtain more information about successful decision-making processes, an investigation was made of 100 case examples drawn from five general areas: (1) Industrial (sales, personnel, production, and financial), (2) Military, (3) Medical, (4) Political, and (5) Commercial. Examination of the cases showed that although the decision process was not explicit, a certain clustering of key variables appeared as a common feature of the decision-reaching process. These variables seemed to influence the success of the decision in a causal fashion. This was particularly in evidence when these clusters were analyzed in the broader framework of their impact upon the total system and effect upon decision success. One cluster of variables was found to surround the quality of the decision itself. These included Compatibility with Existing Operating Constraints, Nearness to Optimum Time for Decision related to Proximity to Optimum Amount of Infornaation, and the Problem Solver's Infiuence on the Decision. The second major factor, implementation of the decision, was affected by Avoidance of Confiict of Interests, Reward-Risk Factors, and the Degree of Understanding Achieved. Total decision success was found to be achieved through the effective usage of the variables of decision quality enforced through skillful management of the variables of implementation. E.g., total decision success equals decision quality plus implementation. Within the 100 cases taken under study, there are several areas that are common to the decision-reaching process in general. By identifying what seem to be the critical stages and elements in decision-reaching, the process itself becomes more amenable to a complete and systematic analysis. Decision-reaching, varying from traditional decision-making, is subjective by nature and includes a high degree of uncertainty. It involves a unique program for the decision process with little or no duplication and with interrelated variables of shifting weight functions. The result is obtained through successive interacting steps which, when completed, allow the ultimate determination. The term—decision-reaching rather than word coinage, should be useful in the delineation of a specific process, setting aside a specific area for future research.
A Min-Max Inventory Model
Authors: Agin Norman 
Published: November 2008

An inventory model is considered where every N periods an order is placed for an amount which brings the sum of stock-on-hand plus on-order up to some level S. The model treats a single item for which there is random demand. Demands that arrive when there is no positive inventory on hand are back-ordered. Leadtime is treated as random and the receipts of orders are allowed to cross in time. Values N* and S* which minimize steady state expected costs per unit time cannot be found. In this paper approximations, N0 and S0 are found. These are determined by minimizing an expected cost per unit time which has been maximized over all distributions of stock deficit with a given mean and variance. The method is applicable even when the functional form of the distribution of demand is not known. Computer simulations are used to indicate the values of the input parameters for which the expected costs per unit time associated with N0 and S0 are close to the similar quantities for N* and S*.
Techniques for Removing Nonbinding Constraints and Extraneous Variables from Linear Programming Problems
Authors: Thompson Gerald L. , Tonge Fred M. , Zionts Stanley 
Published: November 2008

In formulating linear programming problems, analysts tend to include constraints that are not binding at the optimal solution for fear of excluding necessary constraints. The inclusion of such constraints does not alter the optimum solutions, but may require many additional iterations to be taken, as well as increase the computational difficulties encountered. Most of the methods proposed to date for identification of redundant and non-binding constraints are not warranted in practice because of the excessive computations required to implement them. These methods are reviewed in the present paper, and some of them are extended. A number of additional methods are also considered. Two of these new methods are not only practical, but have proven to be powerful in solving a number of problems. These methods may be incorporated in a variant of the simplex method. After each simplex iteration is made, constraints and variables are tested and, when identified as non-binding, are eliminated. The procedure is continued, with the problem size dwindling as the algorithm progresses, until the optimum is reached. Then the values of the eliminated variables are computed by substitution into the eliminated constraints. Early evidence shows that a size reduction for small problems (having 25-30 constraints) of 50 percent is not unusual. It is hoped that the results on larger problems will be even more significant.
Decision-Making Allowing for Uncertainty of Future Investment Opportunities
Authors: Kaplan Seymour , Barish Norman N. 
Published: November 2008

All capital investment decisions are made under varying degrees of risk and uncertainty. Evaluations of the desirability of capital investments require estimates of present conditions as well as forecasts of future events which involve these risks and uncertainties. Several recently developed models for capital investment decision-making have involved the random variable treatment of anticipated cash flows. When present worth calculations are made in these cases, a fixed interest rate is usually assumed. When internal rate of return calculations are made, the minimum acceptable rate of return is given a fixed value. This paper treats the minimum attractive rate of return (used with present worth calculations) and the minimum acceptable internal rate of return (used with internal rate of return calculations) as stochastic variables having subjective probability distributions. This procedure takes explicit account of the uncertainty of future investment opportunities. It is shown that, under certain circumstances, significant differences in the values of the mean and the variance of the criteria for making the investment decision are obtained when the results of this procedure are compared with results using the usual deterministic interest rate or minimum acceptable rate of return.
Some Principles of Practical Welfare Economics
Authors: Simon Julian L. 
Published: November 2008

Non-profit institutions must make many kinds of decisions about allocations of resources. The theory is clear: allocate so that the marginal utility is equal in all uses. But this is more easily said than done. This paper suggests a series of heuristic steps by which real-life non-profit allocation situations can be coordinated to the marginal analysis. It discusses the assumptions that must and should be made and sets forth a conceptual framework for the analytic decisions. The process is illustrated with an example from library research—the decision about how many books should be placed into storage libraries to maximize the welfare of a university community.
On the Art of Modeling
Authors: Morris William T. 
Published: November 2008

The problem of teaching or developing creative modeling ability is considered in the light of three basic hypotheses concerning the processes of enrichment (elaboration of very simple models), association (analogy with previously developed structures), and alternating attention to different aspects of the task. Based on these hypotheses, specific steps are presented which have been developed to help individuals acquire modeling skills. The steps are illustrated by means of an example. Other sources of modeling ability are also suggested. The discussion focuses on specific hypotheses concerning the differences between the teaching of models and the teaching of modeling.
Sugar Cane Irrigation: A Case Study in Capital Budgeting
Authors: Champion Robert R. , Glaser R. George 
Published: November 2008

A study of irrigation, economics was conducted in 1963 on a 10,000 acre sugar plantation in Hawaii and repeated in 1964 on three additional plantations covering 34,000 acres. The objective of the study was to recommend, on an agronomic and economic basis, a program for investing large amounts of capital in newly developed automatic and semi-automatic irrigation systems. Although basically a problem in capital budgeting, a number of complicating factors precluded the straightforward application of traditional investment analysis techniques. The more important of these factors were: 1. Many Alternatives. The wide range of physical conditions found on each plantation ruled out the selection of a single “best” irrigation system. As a result, a number of systems had to be evaluated for each of several hundred cane fields. 2. Combinatorial Aspects. Investments in individual cane fields were interdependent since groups of fields shared common water sources; thus, combinations of investments had to be analyzed, increasing the number of investment alternatives astronomically. 3. Opportunity Value of Scarce Resources. New irrigation systems offered substantial water savings. However, the value of these savings arises from the “opportunity” value of water in alternative uses. Alternative uses, in turn, depend on how much water is saved. Thus, no value for potential savings on the plantation could be established until the investment analysis was run, first for individual alternatives and then for combinations of alternatives. 4. Complicated Timing. Because of crop planting schedules, new systems would be installed on small increments of acreage over a period of 8 to 10 years. Thus, “nonconventional” investments (outlays interspersed with benefits such that the stream of cash flows had multiple sign changes) occurred in multiple time periods; furthermore, because of crop harvesting schedules, returns from these investments also were distributed over multiple time periods with varying amounts of delay between investments and returns. The approach used to solve this problem is described in the paper.
Effects of Learning on Optimal Lot Size
Authors: Keachie E. C. , Fontana Robert J. 
Published: November 2008

This paper deals with the effects of learning on the calculation of optimal economic lot sizes in intermittent production. It is assumed that the manufacturing time of the lots is of such length that phenomena usually called “learning” can be encountered within each lot. The three different cases that can occur are incorporated into a model which demonstrates that the traditional lot size formulae may well indicate lots that are smaller than the true optimum. Numerical examples show the significance of these differences. A simple deterministic model is used but results are seen to be extended easily to more complex cases.
Problem-Solving Trends in Management Science
Authors: Prasad S. Benjamin 
Published: November 2008

The purpose of this paper is to present data and some interpretation relating to (a) the classes of problems in general, (b) as they relate to specific business functional areas, and (c) the management science techniques employed to solve the problems. The presentation is based upon a content analysis of 354 articles published in Management Science, January 1955 through September 1964, upon the classes of O. R. problems suggested by Professor Russell Ackoff in his Manager's Guide to Operations Research. Data presented in this analysis suggest that the majority of problems were those concerned with allocation in the production functional area and that linear programming was the most widely used technique. At the end of the paper it is suggested that “management science” is still evolving, and has a long way to go if it should emerge as a science concerned with (top) managerial problems in industrialized and developing countries.
Management Sciences and Management—Some Requirements for Further Development
Authors: Charnes A. , Cooper W. W. 
Published: November 2008

This paper was prepared for presentation on October 15, 1965, as a luncheon address at The Institute of Management Sciences Eastern Meeting held at the Sheraton Hotel, Rochester, New York. Some of our thoughts on this subject were stimulated by a TIMS sponsored methodology symposium on implementation held at Monterey California, during December of 1964 and, which was subsequently followed by our participation in a round table conference organized by Harvey Wagner for the 1965 National Meetings of TIMS in San Francisco on February 3, 1965. Subsequent discussions with G. Kozmetsky of Teledyne, Inc., D. B. Learner of BBDO, Inc., and B. M. Rowles of M&M's Candies, Inc., were also important in helping us to shape the ideas that are presented in this paper.
Strictly Concave Parametric Programming, Part I: Basic Theory
Authors: Geoffrion Arthur M. 
Published: November 2008

This paper, which is presented in two parts, develops a computational approach to strictly concave parametric programs of the form: Maximize α f1(x) + (1 − α)f2(x) subject to concave inequality constraints for each fixed value of α in the unit interval, where f1 and f2 are strictly concave and certain additional regularity conditions are satisfied. This class of problems subsumes a corresponding class of vector maximum problems and also, by means of a simple device, provides a deformation method for ordinary concave programming. The approach is based on exploiting the continuity properties of the parametric program so as to efficiently maintain a solution to the associated Kuhn-Tucker conditions as a traverses the unit interval. The same approach can be adapted to much more general parametric programs than the one above. In Part I, a Basic Parametric Procedure is derived and shown to be finite in a certain sense. It forms the basis of various parametric programming algorithms, depending on what special assumptions are made on the functions. In Part II, additional theory is developed that facilitates computational implementation, and one possible general-purpose algorithm for a digital computer is given. An illustrative graphical example is presented and several extensions are indicated.