bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of Attribute Weights from Preference Comparisons
Authors: Horsky Dan , Rao M. R.
Published: November 2008

The multi-attribute utility model serves as a basis for many marketing decisions such as new product planning and advertising message selection. The estimation of individuals' attribute weights can be performed using several data types and estimation techniques. There is evidence to suggest that the estimates derived from ordinal preference data through linear programming show greater stability and predictive validity. In this paper we address two fundamental issues which have not been addressed in the context of this latter type estimation: the theoretical foundations for estimating cardinal utility functions from ordinal preference data and the properties of the linear programming estimators. First, we establish the theoretical foundations from economics, mathematical psychology, and decision analysis of obtaining a cardinal (interval scaled) multi-attribute function from ordinal data. This leads us to recommend that in addition to the collection of paired preference comparisons, also comparisons of pairs of pairs be collected. We then describe the type of errors which are likely to arise in the measurement stage, and their relationship to the phenomenon of intransitivities. We formulate a linear program, LINPAC, for the estimation of attribute weights from the above preference data. The previously proposed LINMAP procedure is a special case of this formulation when only the information on the paired preferences is utilized. Next, the statistical properties of the estimators, such as uniqueness, unbiasedness, consistency and efficiency, are examined. Then, through a simulation study we examine the rate of convergence of the estimated weights to the true weights as a function of the number of brands. In the simulation study we also examine the conditions under which the estimators outperform equal weights and compare the estimates derived from LINPAC with those derived from LINMAP. Finally, the estimation procedures are examined with actual data while the simulation results, an equal weights model, and a stated weights model serve as benchmarks.
A Comparison of Exact Approaches for Solving the Multiple Constrained Resource, Project Scheduling Problem
Authors: Patterson James H.
Published: November 2008

A recurring problem in managing project activity involves the allocation of scarce resources to the individual activities comprising the project Resource conflict resolution decisions must be made whenever the concurrent demand for resources by the competing activities of a project exceeds resource availability. When these resource conflict resolution decisions arise, project managers seek direction on which activities to schedule and which to delay in order that the resulting increase in project duration is the minimum that can be achieved with the given resource availabilities. The procedures examined in this paper are all designed to provide for this type of decision support. Each procedure examined is enumerative based, methodically searching the set of possible solutions in such a way that not all possibilities need be considered individually. The methods differ in the manner in which the tree representing partial schedules is generated and is saved, and differ in the methods which are used to identify and discard inferior partial schedules. Each procedure was found to be generally superior on a specific class of problems, and these classes are identified.
Two-Level Linear Programming
Authors: Bialas Wayne F. , Karwan Mark H.
Published: November 2008

Decentralized planning has long been recognized as an important decision making problem. Many approaches based on the concepts of large-scale system decomposition have generally lacked the ability to model the type of truly independent subsystems which often exist in practice. Multilevel programming models partition control over decision variables among ordered levels within a hierarchical planning structure. A planner at one level of the hierarchy may have his objective function and set of feasible decisions determined, in part, by other levels. However, his control instruments may allow him to influence the policies at other levels and thereby improve his own objective function. This paper examines the special case of the two-level linear programming problem. Geometric characterizations and algorithms are presented with some examples. The goal is to demonstrate the tractability of such problems and motivate a wider interest in their study.
The Factored Transportation Problem
Authors: Evans James R.
Published: November 2008

The factored transportation problem is one having cost coefficients of the form cij = uivj. The main result proven in this paper is that if rows are arranged according to nonincreasing ui and columns by nondecreasing vj, then the northwest corner rule provides an optimal solution. Applications to stock location in a warehouse and information storage and retrieval policies are presented. For the stock location problem, this verifies that a previously advocated heuristic is optimal.
The Feasibility of One-Officer Patrol in New York City
Authors: Green Linda , Kolesar Peter
Published: November 2008

How many patrol cars staffed with a single police officer are needed to provide equivalent police service to an existing system with n two-officer patrol cars? This question is explored for New York City using a multiple patrol car per call priority queueing model. It is shown that a one-officer patrol program is feasible, yet pitfalls exist which could adversely affect its performance. The paper details the process of data analysis and model building and emphasizes the subjective elements that remain in a highly technical OR study. Speed of response to emergency calls from the public was the key performance characteristic considered. The analysis also raised issues related to the safety of police officers in one-officer cars.
Estimating Learning Curves from Aggregate Monthly Data
Authors: Womer Norman Keith
Published: November 2008

In this paper the problems of using aggregate monthly data to estimate learning curves are investigated. Here, aggregate monthly data on labor hours are assumed to contain some of both fixed and variable labor hours. They are also assumed to be influenced by fluctuating quantities of work in process. A distributed lag model is developed to deal with these two characteristics of aggregate monthly data. The model is generalized to permit production rate to influence labor productivity. This generalized model is then estimated and compared to a cumulative average learning curve in analyzing the impact of a production break. A set of production data which arose from a government contract claim is used for this purpose.
An Empirical Test of Value-Based Planning Models and Implications
Authors: Woo Carolyn Y.
Published: November 2008

The value of the firm is clearly the central purpose of most entrepreneurial activities. Considering the scope, nature and impact of Strategic Management decisions, one would expect firm value to be an integral concern of this area. Yet seldom do we observe value being explicitly managed or systematically linked to the strategies and direction of the firm.

Recent economic pressures are escalating the need to achieve a more complete and concrete portrayal of this relationship. In this search, a small number of frameworks have sprung up from traditional finance models to describe the value creation/destruction process from the strategic management perspective.

Though these models have generated strong interest and have provided useful insights, they have not been subjected to large-scale empirical evaluation. This research examined two frameworks and assessed the degree of empirical support for their implications.

Based on 4,000 observations in 40 industries over a ten-year period, the current effort provided only partial support for the models and their implications. This paper supported the qualitative contributions of these models to better decision-making processes. Their statistical explanatory power, however, was not demonstrated.
A Model of Venture Capitalist Investment Activity
Authors: Tyebjee Tyzoon T. , Bruno Albert V.
Published: November 2008

The paper describes the activities of venture capitalists as an orderly process involving five sequential steps. These are (1) Deal Origination: The processes by which deals enter into consideration as investment prospects, (2) Deal Screening: A delineation of key policy variables which delimit investment prospects to a manageable few for in-depth evaluation, (3) Deal Evaluation: The assessment of perceived risk and expected return on the basis of a weighting of several characteristics of the prospective venture and the decision whether or not to invest as determined by the relative levels of perceived risk and expected return, (4) Deal Structuring: The negotiation of the price of the deal, namely the equity relinquished to the investor, and the covenants which limit the risk of the investor, (5) Post-Investment Activities: The assistance to the venture in the areas of recruiting key executives, strategic planning, locating expansion financing, and orchestrating a merger, acquisition or public offering. 41 venture capitalists provided data on a total of 90 deals which had received serious consideration in their firms. The questionnaire measured the mechanism of initial contact between venture capitalist and entrepreneur, the venture's industry, the stage of financing and product development, ratings of the venture on 23 characteristics, an assessment of the potential return and perceived risk, and the decision vis-à-vis whether to invest. The modal venture represented in the database was a start-up in the electronics industry with a production capability in place and seeking $1 million (median) in outside financing. There is a high degree of cross-referrals between venture capitalists, particularly for the purposes of locating co-investors. Factor analysis reduced the 23 characteristics of the deal to five underlying dimensions namely (1) Market Attractiveness (size, growth, and access to customers), (2) Product Differentiation (uniqueness, patents, technical edge, profit margin), (3) Managerial Capabilities (skills in marketing, management, finance and the references of the entrepreneur), (4) Environmental Threat Resistance (technology life cycle, barriers to competitive entry, insensitivity to business cycles and down-side risk protection), (5) Cash-Out Potential (future opportunities to realize capital gains by merger, acquisition or public offering). The results of regression analyses showed expected return to be determined by Market Attractiveness and Product Differentiation (R2 = 0.22). Perceived risk is determined by Managerial Capabilities and Environmental Threat Resistance (R2 = 0.33). Finally, a discriminant analysis correctly predicted, in 89.4% of the cases, whether or not a venture capitalist was willing to commit funds to the deal on the basis of the expected return and perceived risk. The reactions of seven venture capitalists who reviewed the model's specification were used to test its validity.
Inferring Preferences in Multiple Criteria Decision Analysis Using a Logistic Regression Model
Authors: Stewart Theodor J.
Published: November 2008

A method is proposed for the analysis of multiple criteria decision making problems in an interactive environment, when decision-maker preferences are inconsistent with a simple utility model and/or are self-inconsistent (e.g., showing intransitivities). A maximum likelihood estimation procedure is invoked which is based on a logistic regression model relating the probability of selecting one decision option over another to a linear function of attribute values. The method is illustrated by application to multi-objective linear programming, where it serves as an alternative to the method of Zionts and Wallenius (Zionts, S., J. Wallenius. 1976. An interactive programming method for solving the multiple criteria problem. Management Sci. 22 652–663.), and allows for inconsistencies which are not satisfactorily handled in the Zionts-Wallenius approach.
The p-Median Problem for Cluster Analysis: A Comparative Test Using the Mixture Model Approach
Authors: Klastorin T. D.
Published: November 2008

Recently, Mulvey and Crowder (Mulvey, J., H. Crowder. 1979. Cluster analysis: an application of Lagrangian relaxation. Management Sci. 25 329–340.) suggested that the p-median problem might be useful for cluster analysis problems (where the goal is to group objects described by a vector of characteristics in such a way that objects in the same group are somehow more alike than objects in different groups). The intent of this paper is to test Mulvey and Crowder's proposal using the mixture model approach; i.e., by applying a number of algorithms (including one for the p-median problem) to a set of objects randomly sampled from a number of known multivariate populations and comparing the ability of each algorithm to detect the original populations. In order to evaluate the results, a generalized partition comparison measure and its distribution are developed. Using this measure, results from various algorithms are compared.
Decision Analysis Models for Social Attitudes Toward Inequity
Authors: Harvey Charles M.
Published: November 2008

Social preferences for equity in the distribution of net benefits are not represented in a cost-benefit study by the sum of the individuals’ net present values. This paper presents two different decision analysts models for representing such an equity issue. For each model, conditions on the tradeoffs between different individuals are shown to imply that preferences can be represented by a special type of group value function. Procedures are presented by which such a group value function can be determined and used as part of a public policy evaluation.
Forecasting Trends in Time Series
Authors: Gardner Everette S. , Mckenzie Ed.
Published: November 2008

Most time series methods assume that any trend will continue unabated, regardless of the forecast lead time. But recent empirical findings suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence. This paper develops an exponential smoothing model designed to damp erratic trends. The model is tested using the sample of 1,001 time series first analyzed by Makridakis et al. Compared to smoothing models based on a linear trend, the model improves forecast accuracy, particularly at long leadtimes. The model also compares favorably to sophisticated time series models noted for good long-range performance, such as those of Lewandowski and Parzen.
A Multi-Echelon Inventory Model for a Repairable Item with One-for-One Replenishment
Authors: Graves Stephen C.
Published: November 2008

The problem that we address is to determine the inventory stockage levels in a multi-echelon inventory system for a repairable item. In its simplest form the multi-echelon system consists of a set of operating sites supported by a centrally-located repair depot. Each operating site requires a set of working items and maintains an inventory of spare items. The repair depot also holds an inventory of spare items. Item failures are infrequent and are replaced on a one-for-one basis. In this paper we present an exact model for finding the steady-state distribution of the net inventory level at each site. This model assumes that the failures are generated by a compound Poisson process and that the shipment time from the repair depot to each site is deterministic. No assumptions are made with regard to the repair cycle at the depot. We contrast this model with existing models for these systems. Based on the exact model we present an approximation for the steady-state distribution for the case with ample servers at the repair depot. We show that this approximation is very accurate on a set of test problems.
An Interactive Decomposable Heuristic for Project Selection
Authors: Mandakovic Tomislav , Souder William E.
Published: November 2008

Decomposable systems, decomposition and 0-1 integer programming techniques are reviewed relative to project selection problems. It is concluded that a heuristic procedure is a suitable way to handle such problems in organizational settings. A hierarchical multiperiod multidimensional model is proposed. Good results are obtained based on an existing heuristic algorithm by Toyoda, by means of a decomposable interactive formulation. Examples and discussions are included that show how the use of this model can improve management decisions. A major advantage of this formulation is its flexibility to handle many different R&D situations, a feature which should increase its usefulness over many other R&D Project Selection Models. The model can be used in a multihierarchy ambience, can use parametric budgeting, and can be used as a control tool. The interactive feature provides the means for improving organizational communications and integrating divergent viewpoints.
Project Selection Decision Making Linked to a Dynamic Environment
Authors: Fox G. Edward , Baker Norman R.
Published: November 2008

A simulation model that attempts to include simplified market and production characteristics of a hypothetical firm and a specific project selection decision mechanism is constructed. Three types of projects are assumed to exist—projects whose impact is to increase market share, decrease production cost and increase production capacity. An experiment aimed at two questions is conducted. The questions are (1) Do different market conditions affect project selection decisions and (2) over time, what patterns of project selection emerge. In the model and experiment, an attempt is made to capture the essence of Abernathy and Utterback's model of the innovation process so that it may be applied to more specific settings. Observed project selection patterns over time indicate an apparent relationship between market share increasing and production cost decreasing projects and Abernathy and Utterback's concept of product innovation, and similarly between capacity increasing projects and process innovation.

The model can be viewed as an attempt at developing a linkage between the environment of a firm and an “iterative decision mechanism” as proposed by Baker and Sweeney.
An Improved Successive Linear Programming Algorithm
Authors: Zhang Jianzhong , Kim Nae-Heon , Lasdon L.
Published: November 2008

Successive Linear Programming (SLP) algorithms solve nonlinear optimization problems via a sequence of linear programs. They have been widely used, particularly in the oil and chemical industries, beginning with their introduction by Griffith and Stewart of Shell Development Company in 1961. Since then, several applications and variants of SLP have appeared, the most recent being the SLPR algorithm described in this journal in 1982 (Palacios-Gomez et al.). SLP procedures are attractive because they are fairly easy to implement if an efficient, flexible LP code is available, can solve nonseparable as well as separable problems, can be applied to as large a problem as the LP code can handle (often thousands of constraints and variables), and have been successful in many practical applications.

This paper describes a new SLP algorithm called PSLP (Penalty SLP). PSLP represents a significant strengthening and refinement of the SLPR procedure described in Palacios-Gomez et al. (Palacios-Gomez, F., L. Lasdon, M. Engquist. 1982. Nonlinear optimization by successive linear programming. Management Sci. 28 1106–1120.). We give a convergence proof for PSLP—the first SLP convergence proof for nonlinearly constrained problems of general form. This theory is supported by computational performance—in our tests, PSLP is significantly more robust than SLPR, and at least as efficient. A Fortran computer implementation is described.

A simplified version of PSLP has already solved several “real world” NLP problems at Exxon (Baker and Lasdon [Baker, T. E., L. S. Lasdon. 1985. Successive linear programming at Exxon. Management Sci. 31 (March) 264–274.), including nonlinear refinery models of up to 1000 rows. As with other SLP algorithms, PSLP is especially efficient on problems which are highly constrained, i.e., which have nearly as many active constraints as there are variables. For problems with vertex optima (at least as many active constraints as variables), it is quadratically convergent. Nonlinear refinery models often have vertex optima, since they are large and mostly linear, and on line process unit optimization problems are likely to possess highly constrained solutions as well. PSLP has great potential for accurate, efficient solution of such problems.
An Experimental Evaluation of Graphical and Color-Enhanced Information Presentation
Authors: Benbasat Izak , Dexter Albert S.
Published: November 2008

A laboratory experiment was conducted to assess the influence of graphical and color-enhanced information presentation on decision quality, decision-making time and user perceptions of information systems attributes. The experimental design examined the main and interaction effects of report format, color, and individual differences (field dependence/independence) among the subjects. The findings indicate that the claims made about the benefits of color-enhanced reports are subject to qualification. Even though color influenced decision-making quality in general, and to some extent color was more beneficial for graphical than tabular reports, its most significant impact was on the performance of field-dependents. The decision-making quality of field-dependents with color-enhanced reports was 73% better than field-dependents who did not have such reports. There were no performance differences between subjects who used tabular and graphical reports. This outcome is explained by taking into consideration the underlying nature of the task given to the subjects and how the reports were organized to support this task. This finding suggests that proponents of graphical presentation must qualify their claims to environments where there is a clearly defined rationale for the potential benefits of graphical report usage.
A Method of Multiattribute Decision Making with Incomplete Information
Authors: Weber M.
Published: November 2008

A precise determination of a multiattribute utility function of a decision maker or a group of decision makers requires considerable information that may not be available in many decision situations. In this paper the method HOPIE, which allows one to determine a set of utility functions consistent with the incomplete information received from the decision maker, is proposed. The method is based on holistic judgments of hypothetical alternatives defined by a certain factorial design. It requires that the evaluation of the alternatives be given on an interval scale. The method can also accommodate other types of additional information such as pairwise comparisons.
Information Acquisition and the Adoption of New Technology
Authors: McCardle Kevin F.
Published: November 2008

The profitability of a new technology is rarely known with certainty at its announcement date. Consequently, prior to making an adoption decision it behooves the firm considering the adoption of this innovation to reduce the level of uncertainty associated with its profitability. The firm accomplishes this by sequentially gathering information, updating its prior estimate of profitability in a Bayesian manner. Quantifying the uncertainty regarding the innovation permits application of dynamic programming techniques: criteria are derived which tell the firm when to stop collecting information and make the adoption decision. It will be shown that it is optimal for the firm to continue to collect information until its estimate of profitability crosses one of two thresholds: upon crossing the upper threshold the firm adopts the technology, whereas the firm rejects the technology if the lower threshold is crossed. The model predicts that even the manager who behaves optimally will occasionally adopt unprofitable technologies and reject profitable ones.
Estimating Market Model Betas: A Comparison of Random Coefficient Methods and Their Ability to Correctly Identify Random Variation
Authors: McDonald Bill
Published: November 2008

When estimating market model betas using random coefficient methods, the rather fine distinction between significance or insignificance, as argued in recent studies, overlooks two important factors. First, the maximum likelihood method has not been tested in comparison to the generalized least squares approximation. Second, and more importantly, the ability of these methodologies to correctly identify a known random coefficient process has not been examined in the context of the market model. Using both simulations and subsequent empirical tests, this study shows that, for reasonable levels of variation in beta, neither method can consistently identify a random coefficient process. These results suggest that the nominal level of significant random coefficients previously observed could be indicative of a much more predominant phenomenon.
98%-Effective Integer-Ratio Lot-Sizing for One-Warehouse Multi-Retailer Systems
Authors: Roundy Robin
Published: November 2008

A warehouse supplies N retailers. Constant external demand occurs at each retailer, and shortages are not allowed. There are linear holding costs and fixed costs for ordering at the warehouse and at each retailer. The goal is to minimize the long-run average cost over an infinite time horizon.

We define a new class of policies for this problem whose simple structure facilitates both computation and implementation. The cost of a policy that is optimal within this class is shown to be within 2% of the cost of an optimal policy for the original problem, in the worst case. Such a policy can be computed in O(N log N) time.
A State Space Modeling Approach for Time Series Forecasting
Authors: Sastri Tep
Published: November 2008

A stochastic filtering method is presented for on-line recursive estimation and forecasting of autocorrelated time series. Several state space models for nonseasonal and seasonal time series, which belong to the autoregressive integrated-moving average class, are presented. The Kalman filter is introduced as the recursive data processor for on-line time series forecasting. The estimation problem and initial values determination are discussed, and numerical examples are given. An extension of Brown's adaptive smoothing method for autocorrelated time series through the proposed filtering approach is also presented.
Branch and Bound Experiments in Convex Nonlinear Integer Programming
Authors: Gupta Omprakash K. , Ravindran A.
Published: November 2008

The branch and bound principle has long been established as an effective computational tool for solving mixed integer linear programming problems. This paper investigates the computational feasibility of branch and bound methods in solving convex nonlinear integer programming problems. The efficiency of a branch and bound method often depends on the rules used for selecting the branching variables and branching nodes. Among others, the concepts of pseudo-costs and estimations are implemented in selecting these parameters. Since the efficiency of the algorithm also depends on how fast an upper bound on the objective minimum is attained, heuristic rules are developed to locate an integer feasible solution to provide an upper bound. The different criteria for selecting branching variables, branching nodes, and heuristics form a total of 27 branch and bound strategies. These strategies are computationally tested and the empirical results are presented.
The Impact of Decision Models on Federal Coal Leasing
Authors: Gulley D. A. , Mei D. M.
Published: November 2008

A three-year case study was conducted of the effects of computer-based decision technology on policy formation, policy implementation, and routine operations in the Federal Coal Management Program. Several methodologies were used, including semistructured interviewing and content analysis. Midway through the interview schedule, program regulations were changed to reflect the different philosophies of the Carter and Reagan administrations, affording the observers an unusual opportunity to study the reactions of the organization to a de-emphasis on analyses and computer applications. Also noteworthy in the case was the emergence of a major public policy dispute which was closely associated with the coal leasing program’s changes in use of computer-based economic analysis. We found that computer models had significant but not dominant effects on the workplace environment of most persons working in the program. Nevertheless some applications of computer models had very high visibility and large impacts.
A New Product Adoption Model with Price, Advertising, and Uncertainty
Authors: Kalish Shlomo
Published: November 2008

This paper introduces a framework for modeling innovation diffusion that includes price and advertising. The adoption of a new product is characterized by two steps: awareness and adoption. Awareness is the stage of being informed about the product search attributes. The process of becoming aware is modeled as a simple “epidemic” type model, where the information is spread by advertising and word of mouth. The second step, adoption, is conditional on awareness, and it occurs if the perceived risk adjusted value of the product exceeds its selling price. The population is heterogeneous with respect to valuation of the product. Individuals are risk averse, and therefore are willing to pay more for the product, on the average, as information from early adopters reduces uncertainty about the product. Optimal control of the diffusion process by pricing and advertising over time is analyzed, and a comparative estimation of the model in one application is reported.
A Robust Credit Screening Model Using Categorical Data
Authors: Kolesar Peter , Showers Janet L.
Published: November 2008

Motivated by an application in a public utility, the credit screening problem is re-examined from a decision theoretic viewpoint. The relationships between several alternative problem formulations are explored, and compared to the classical linear discriminant analysis (LDA) approach. Several mathematical programming based solution methods are proposed when the data are binary, and an efficient algorithm is developed for the case when the screening function must also have binary weights. Actual results of both the mathematical programming and LDA methods are presented and compared. The resulting mathematical programming rules are effective, robust, and flexible to administer. Practical advantages of the resulting “n out of N” type rules are discussed. These screening rules have been widely implemented by a major public utility and have resulted in substantial benefits to the utility and to the public.
Capital Rationing and Organizational Slack in Capital Budgeting
Authors: Antle Rick , Eppen Gary D.
Published: November 2008

This paper reconciles three stylized facts about capital budgeting in firms and shows that they are tied to the presence of asymmetric information among the several members of the firm, each with his or her own objectives and decisions. The facts of interest are:

1. The existence of organizational slack.

2. The “rationing” of resources within organizations.

3. The stated “cut off rate” for accepting capital projects in firms is often greater than the market rate of interest.

Organizational slack is defined as the excess of resources allocated over the minimum necessary to accomplish the tasks assigned. Resource rationing is defined as the under-allocation of resources; i.e., an increase in the amount allocated would generate revenues in excess of its costs. Rationing and slack are both manifestations of ex post inefficiencies. An LP model is used to show that these inefficiencies can occur in ex ante efficient organizational designs when asymmetric information is present. The optimal allocation policy involves a hurdle rate criterion in which the hurdle rate is strictly in excess of the cost of capital, thus inducing rationing in some states of the world. Typically, resources are optimally allocated such that slack exists in other states. The optimal allocation policy trades off these two inefficiencies.
Capacity Expansion of Power Generation Systems with Uncertainty in the Prices of Primary Energy Resources
Authors: Levin Nissan , Tishler Asher , Zahavi Jacob
Published: November 2008

In capacity expansion studies of power generation systems, the uncertainty in the prices of the primary energy resources is a major concern. The approach presented in this paper accounts for this uncertainty by mapping the probability distribution of the installed capacity for any given distribution of the fuel prices. Given the probability density function and the cumulative distribution of the installed capacity, a variety of performance measures is derived, including the expected value, the mode, and the variance of the installed capacity, and the expected total costs. Results are obtained analytically for a 2-unit system using a breakeven point analysis approach to find the optimal mix for any given set of fuel prices under the assumption of normally distributed primary energy prices.
The Role of Linear Recursive Estimators in Time Series Forecasting
Authors: Pack D. J. , Pike D. H. , Downing D. J.
Published: November 2008

This paper presents a descriptive synthesis of a number of a linear recursive estimator (LRE) procedures for time series forecasting, i.e., procedures which involve parameter updates proportional to the last period forecast error. It is stressed that both constant and variable parameter procedures exist among LRE's. General requirements for stability of parameter estimates are given, as are general forms for parameter estimate covariance matrices that appear in forecast variance determinations. Procedures explicitly considered are the Kalman filter, dynamic autoregression, the Carbone-Longini adaptive estimation procedure, generalized least squares, Widrow's least mean square, and the Makridakis-Wheelwright generalized adaptive filtering.
Uncertainty and Technical Communication Patterns
Authors: Brown James W. , Utterback James M.
Published: November 2008

This paper examines the relationship between research and development peoples' perceptions of uncertainty in their firm's competitive environment and their patterns of technical communication. Measures of both these attributes of six R&D groups, two in each of three industries, are reported and analyzed here. Technical people who saw the world (competitors, suppliers, customers, technology and regulations) outside their firm as more uncertain also were found to seek greater contact with sources of information outside their firms. The gatekeeper phenomenon was found to be more pronounced, but less formal and less well defined in these firms. Gatekeepers in general were found to perceive a higher level of uncertainty than others in all six firms.
M-Labeled Digraphs: An Aid to the Use of Structural and Simulation Models
Authors: Burns James R. , Winstead Wayland H.
Published: November 2008

M-labeled digraphs are defined and a technique for determination of the sign of sequences in such digraphs is presented. Conditions under which the sign of a sequence can be determined are derived. This work is motivated by the need to assess the structural effect of an “input” upon the endogenous quantities of an M-labeled digraph. A technique for comparing and combining the effects of structurally redundant inputs is presented. The theoretical development is followed by application to a prototypical Forrester model involving a population sector interactant with a housing sector.
Note—A Dynamic Programming Algorithm for Joint Replenishment Under General Order Cost Functions
Authors: Rosenblatt Meir J. , Kaspi Moshe
Published: November 2008

The multi-item joint replenishment problem is generalized to allow ordering costs to be dependent on the specific items jointly supplied. A fixed cycle approach is examined in which all the items of a group are always jointly replenished. A dynamic programming algorithm is developed for partitioning the items into groups, each with its own fixed cycle time, resulting in an optimal fixed cycle replenishment policy.
Transactions Costs, Extent of Inefficiencies, Entries and Multiple Wagers in a Racetrack Betting Model
Authors: Hausch Donald B. , Ziemba William T.
Published: November 2008

In a previous paper (Management Science, December 1981) Hausch, Ziemba and Rubinstein (HZR) developed a system that demonstrated the existence of a weak market inefficiency in racetrack place and show betting pools. The system appeared to make possible substantial positive profits. To make the system operational, given the limited time available for placing bets, an approximate regression scheme was developed for the Exhibition Park Racetrack in Vancouver for initial betting wealth between $2500 and $7500 and a track take of 17.1%. This paper: (1) extends this scheme to virtually any track and initial wealth level; (2) develops a modified system for multiple horse entries; (3) allows for multiple bets; (4) analyzes the effects of the track take and breakage on profits; (5) presents recent results using this system; and (6) considers the extent of the inefficiency, i.e., how much can be bet before the market becomes efficient?
Effort and Accuracy in Choice
Authors: Johnson Eric J. , Payne John W.
Published: November 2008

Individuals often use several different strategies such as the expected value rule, conjunctive rule, and elimination-by-aspects, to make decisions. It has been hypothesized that strategy selection is, in part, a function of (1) the ability of a strategy to produce an accurate response and (2) the strategy's demand for mental resources or effort. We examine effort and accuracy and their role in strategy selection. Several strategies that may be used to make choices under risk are simulated using a production system framework. This framework allows the estimation of the effort required to use the strategy in a choice environment, while simultaneously measuring its accuracy relative to a normative model. A series of Monte-Carlo studies varied several aspects of the choice environments, including the complexity of the task and the presence or absence of dominated alternatives. These simulations identify strategies which approximate the accuracy of normative procedures while requiring substantially less effort. These results, however, are highly contingent upon characteristics of the task environment. The potential of production system models in understanding task effects in decisions is stressed.
A Linear Dynamic Programming Approach to Irrigation System Management with Depleting Groundwater
Authors: Stoecker A. L. , Seidmann A. , Lloyd G. S.
Published: November 2008

A model for measuring the economic benefits of irrigation system development over a depleting aquifer is presented, along with related methodology for detailed long-range farm planning. The paper considers management issues, such as distribution system configuration, drilling policy, area developed for irrigation, and crop production.

A Linear Dynamic Programming (LDP) method is developed and applied to derive optimal temporal investments in the use of stock resources and long-term cropping plans. First, parametric linear programming (PLP) is used to maximize periodic profits subject to specified values of state variables related to annual water use and irrigation system capacity. The PLP results are then used in a dynamic programming model to determine the optimal allocation of water and irrigation resources over time. The impact of aquifer depletion on the profitability of furrow and pivot irrigation systems is illustrated for a typical farm situation in the Texas High Plains. Results indicate that the economic benefits of modern water and energy efficient irrigation systems may come from the expansion of current irrigation intensity rather than from an extended period of irrigation when water is initially scarce relative to land. Several conditions are identified where economic depletion may occur before the point of physical exhaustion is reached.
Multiple Objective Linear Programming with Parametric Criteria Coefficients
Authors: Benson Harold P.
Published: November 2008

In this paper we study the multiple objective linear programming problem with parametric criteria coefficients. This problem is of interest since in many situations the coefficients of the objective functions of a multiple objective linear program either represent estimates of the true data or are subject to systematic variations. Properties of this problem are developed, and an algorithm for generating the set of all weakly-efficient extreme points of this problem is described. To implement this algorithm, a nonconvex subproblem must be solved for each candidate extreme point encountered. This is accomplished by applying the Generalized Benders Decomposition method. Computational results concerning the solution of these subproblems are presented.
Fill-Rate Optimization in a One-Warehouse N-Identical Retailer Distribution System
Authors: Schwarz Leroy B. , Deuermeyer Bryan L. , Badinelli Ralph D.
Published: November 2008

This paper examines the system fill-rate of a one-warehouse N-identical retailer distribution system as a function of warehouse and retailer safety stock. Using the approximation model of Deuermeyer and Schwarz (Deuermeyer, B. L., L. B. Schwarz. 1981. A model for the analysis of system service level in warehouse/retailer distribution systems: the identical retailer case. Chapter 8 in Multi-Level Production/Inventory Control Systems: Theory and Practice. North-Holland, Amsterdam.) we examine the problem of maximizing system fill-rate subject to a constraint on system safety stock. Optimal safety stock policy is characterized to be the intersection of a fill-rate policy line and the safety stock budget line. Properties of fill-rate policy lines are given. These properties may be used to provide managerial insight into system optimization and as the basis for heuristics. One heuristic, the vertical heuristic, based on the corresponding deterministic version of the problem, yields trivially simple and near-optimal policies.
Should Scoring Rules be “Effective”?
Authors: Nau Robert F.
Published: November 2008

A scoring rule is a reward function for eliciting or evaluating forecasts expressed as discrete or continuous probability distributions. A rule is strictly proper if it encourages the forecaster to state his true subjective probabilities, and effective if it is associated with a metric on the set of probability distributions. Recently, the property of effectiveness (which is stronger than strict properness) has been proposed as a desideratum for scoring rules for continuous forecasts, for reasons of “monotonicity” in keeping the forecaster close to his true probabilities, since in practice the forecast must be chosen from a low-dimensional set of “admissible” distributions. It is shown in this paper that what effectiveness implies, beyond strict properness, is not a monotonicity property but a transitivity property, which is difficult to justify behaviorally. The logarithmic scoring rule is shown to violate the transitivity property, and hence is not effective. The L1 and L∞ metrics are shown to allow no effective scoring rules. Some potential difficulties in interpreting admissible forecasts are also discussed.
Strategic Implications of Market Share Attraction Models
Authors: Karnani Aneel
Published: November 2008

Market share attraction models, which specify that a firm's market share is equal to the ratio of its “attraction” to the total attraction for all firms, have received increasing attention in recent years. However, there has been little research investigating the practical implications of such models. This paper presents a game-theoretic analysis of such a model and deduces the strategic implications of a Nash equilibrium solution to the model. It is shown that these implications are consistent with previous empirical research in marketing and business policy.
The Tolerance Approach to Sensitivity Analysis in Linear Programming
Authors: Wendell Richard E.
Published: November 2008

In constrast to “ordinary” sensitivity analysis in linear programming, the tolerance approach considers simultaneous and independent changes in the objective function coefficients and in the right-hand side terms. This approach yields a maximum tolerance percentage such that, as long as selected coefficients or terms are accurate to within that percentage of their estimated values, the same basis is optimal. In particular, if the objective function coefficients are accurate to within the maximum tolerance percentage of their specified values, then the same solution is optimal.
Estimating Network Characteristics in Stochastic Activity Networks
Authors: Fishman George S.
Published: November 2008

This paper describes a Monte Carlo method based on the theory of quasirandom points for estimating the distribution functions and means of network completion time and shortest path time in a stochastic activity network. In particular, the method leads to estimators whose absolute errors converge as (log K)N/K, where K denotes the number of replications collected in the experiment and N is the number of dimensions for sampling. This rate compares favorably with the standard error of estimate O(1/K1/2) which obtains for experiments that use random sampling. Moreover, since quasirandom points are nonrandom the upper bound (log K)N/K is deterministic in contrast to the random sampling rate O(1/K1/2) which is probabilistic. The paper demonstrates how the use of a cutset of the network reduces N in the bound when estimating the distribution functions. Two examples illustrate the benefits and costs of using quasirandom points.
Work Force Size and Single Shift Schedules with Variable Demands
Authors: Burns R. N. , Carter M. W.
Published: November 2008

In recent years, labour negotiations for seven-day-week organizations such as hospitals, mining companies and chemical industries have seen an increased emphasis on improving shift schedules. This paper gives an exact lower bound on the number of workers required to satisfy such primary contractual commitments as ensuring that each employee receives at least A out of every B weekends off, everyone works exactly five days per week, and no one works more than six consecutive days. The model addressed is general in that it allows the number of workers required each day to vary. The proof that the bound is the best possible is constructive. A linear time algorithm is presented that generates schedules satisfying all the primary objectives. Extensions discuss means of incorporating secondary objectives such as giving pairs of adjacent days off.
Note—A Note on the Optimality of the Modified Generalized Goal Decomposition Model
Authors: Davis Wayne J. , Whitford David T.
Published: November 2008

This note demonstrates that Freeland's (Freeland, J. R. 1976. A note on goal decomposition in a decentralized organization. Management Sci. 23 100–102.) modification of Ruefli's (Ruefli, T. W. 1971. A generalized goal decomposition model. Management Sci. 17 505–518.) Generalized Goal Decomposition model can generate nonoptimal solutions and shows how these difficulties can be corrected. In several papers on multi-level planning, it has been suggested that extending the hierarchical levels of the models from two to three or more levels is a straightforward process. This paper shows that this assertion is generally not true. The note points out that in Freeland's (Freeland, J. R. 1976. A note on goal decomposition in a decentralized organization. Management Sci. 23 100–102.) three-level model, the interactions between the two lowest level of the hierarchy must be strictly controlled before information is passed to the highest level of the organization.