bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Note on “An Efficient Zero‐One Formulation of the Multilevel Lot‐Sizing Problem”
Authors: Rajagopalan S. 
Published: June 2007

ABSTRACTIn a recent paper, McKnew, Saydam, and Coleman [3] presented a novel zero‐one integer programming formulation of the multilevel dynamic, deterministic lot‐sizing problem in assembly systems. They stated that “the relaxed linear programming solution to this formulation will always be integer’ [3, p. 280] since the constraint matrix is totally unimodular. In this note, we point out that the constraint matrix is not totally unimodular and therefore the authors’claim that a linear relaxation of the zero‐one integer formulation always yields an integer solution is not true.
On the Sequential Selection Problem*
Authors: Chun Young Hak , Platt Marjorie 
Published: June 2007

ABSTRACTKwan and Yuan [13] considered the sequential selection problem in which an employer should arrange the sequence of interviews with job applicants to fill a position. In this note, it is shown that their selection problem would be alternately interpreted as an optimal search problem or, more specifically, a discrete search problem with a stationary target. A more generalized version of the ordering problem is proposed which explicitly considers the time value of money. Also, the optimality of the ordering strategy in the generalized problem is proven by the pair‐wise exchange method, which is simpler than the induction hypothesis‐based proof. The sequention selection problem is shown to be a special case of the general ordering problem where the discount rate is zero.
The Emergence of Artificial Intelligence as a Reference Discipline for Decision Support Systems Research
Authors: Goul Michael , Henderson John C. , Tonge Fred M. 
Published: June 2007

ABSTRACTIt is timely and appropriate to examine both philosophical and pragmatical issues associated with formalizing the adoption of artificial intelligence as a reference discipline for decision support systems research. This paper reflects on where we were when the first special issue of Decision Sciences on expert systems and decision support systems was published, addresses the dynamics of what has taken place subsequent to the publication of that first special issue, sets forth a proposition to stimulate ongoing dialog with respect to synergies between the decision support system research agenda and the research agenda of the artificial intelligence discipline, and demonstrates how the papers appearing in this follow‐up special issue of Decision Sciences are representative of an emerging, challenging, and exciting new decision support systems era.
A Prototype Expert Decision Support System for the Market Appraisal of the Single Family Residence
Authors: Moore James S. 
Published: June 2007

ABSTRACTThe assessment of the current market value of residential property is a potential source of anxiety for the typical homeowner. Common criticisms of the existing appraisal process include excessive subjectivity and inconsistency across properties and through time. This paper examines the nature of decision support required for valuation decisions and the appropriateness of applying expert system technologies to evaluate the recoverable value of the single‐family residence. The expert system approach allows for the integration of the qualitative and quantitative aspects of appraisal, while introducing the beneficial dimensions of increased objectivity, comprehensiveness, and consistency. A prototype expert system is offered that enables the end user to conduct a market analysis of a subject property. Unlike most expert system applications that seek a diagnostic or classification response, this study explores a numerical intent for the system, in a decision‐making environment that is traditionally viewed as highly judgmental. The system's early validation results show promise of proving effective as such an evaluation aid.
An Interactive Framework for Multi‐Person, Multiobjective Decisions
Authors: Lewis Holly S. , Butler Timothy W. 
Published: June 2007

ABSTRACTGroup decision making in the presence of multiple conflicting objectives is complex and difficult. This paper describes and evaluates an iterative technique to facilitate multiple objective decision making by multiple decision makers. The proposed method augments an interactive multiobjective optimization procedure with a preference ranking tool and a consensus ranking heuristic. Two multiple objective linear programming (MOLP) solution approaches, the SIMOLP method of Reeves and Franz [39] and the interactive weighted Tchebycheff procedure of Steuer and Choo [49], are recommended optimization strategies to be used independently or in concert. Computational experience suggests that the proposed framework is an effective decision‐making tool. The procedure quickly located excellent compromise solutions in a series of test problems with hypothetical decision makers. In addition, human decision makers gave positive evaluations of the procedure and the production plans the procedure provided for a resource allocation case problem.
Toward an Understanding of the Factors Affecting the Quality of the Audit Process*
Authors: Sutton Steve G. 
Published: June 2007

ABSTRACTThe accounting profession has faced increasing pressure from external parties to monitor and improve the quality of the audit process. Similar to other service oriented industries, the accounting profession's ability to meet these pressures has been hampered by the lack of an objective means by which to evaluate process quality. In this research, groups of experienced auditors from two international accounting firms were used to develop and validate a set of key factors influencing the quality of the audit process and a corresponding set of measures for evaluating audit quality. The results support the belief that there exists a consensus among experienced auditors on a set of key audit quality factors which have a significant impact on overall audit quality. The results obtained go beyond those in prior audit quality research, in that the factors identified cover a broader scope and recognize the significant effect of the audit environment on process quality. The consistency between groups in the profession provides support for further use of the nominal group techniques in developing quality measures for processes in other service sector areas.
Determining Differences in Expert Judgment: Implications for Knowledge Acquisition and Validation*
Authors: O'Leary Daniel E. 
Published: June 2007

ABSTRACTIn knowledge acquisition, it is often desirable to aggregate the judgments of multiple experts into a single system. In some cases this takes the form of averaging the judgments of those experts. In these situations it is desirable to determine if the experts have different views of the world before their individual judgments are aggregated. In validation, multiple experts often are employed to compare the performance of expert systems and other human actors. Often those judgments are then averaged to establish performance quality of the expert system. An important part of the comparison process should be determining if the experts have a similar view of the world. If the experts do not have similar views, their evaluations of performance may differ, resulting in a meaningless average performance measure. Alternatively, if all the validating experts do have similar views of the world then the validation process may result in paradigm myopia.
Fuzzy vs. Minmax Weighted Multiobjective Linear Programming Illustrative Comparisons
Authors: Martinson Frederick K. 
Published: June 2007

ABSTRACTThis paper compares two approaches to the solution of weighted multiobjective linear programming problems: the fuzzy linear programming method and the minmax distance metric. The two models produce an identical solution for equally weighted objectives, but the solutions differ when the objectives are unequally weighted. This is due to the underlying meaning of the weights attached to each solution method. The paper illustrates the graphical meaning of the weights and the implications to the decision maker.
Two‐Group Classification Using Neural Networks*
Authors: Patuwo Eddy , Hu Michael Y. , Hung Ming S. 
Published: June 2007

ABSTRACTArtificial neural networks are new methods for classification. We investigate two important issues in building neural network models; network architecture and size of training samples. Experiments were designed and carried out on two‐group classification problems to find answers to these model building questions. The first experiment deals with selection of architecture and sample size for different classification problems. Results show that choice of architecture and choice of sample size depend on the objective: to maximize the classification rate of training samples, or to maximize the generalizability of neural networks. The second experiment compares neural network models with classical models such as linear discriminant analysis and quadratic discriminant analysis, and nonparametric methods such as k‐nearest‐neighbor and linear programming. Results show that neural networks are comparable to, if not better than, these other methods in terms of classification rates in the training samples but not in the test samples.
Heuristic Procedures for Practical‐Sized Uncapacitated Location‐Capacitated Routing Problems*
Authors: Chien T. William 
Published: June 2007

ABSTRACTFacility location and vehicle routing are two important logistical problems closely interrelated in many real‐world applications where locating facilities and determining the associated multi‐stop vehicle routes are required simultaneously. Previous research has found that using the classical facility location models on these location‐routing problems (LRPs) may lead to suboptimal solutions. We propose an approximate approach for the LRPs, which first generates and improves feasible location/allocation schemes with the associated multi‐stop routing costs approximated using some route length estimators. We then design the minimum‐cost routes based on the location/allocation results. We review two estimators that can provide accurate approximations to the multi‐stop route distances; define the uncapacitated location‐capacitated routing problem; and evaluate several heuristic procedures for approximately solving the problem. Computational results show that when vehicle capacities are not too restrictive, the sequential procedures that incorporate the two robust route length estimators can produce good solutions to practical‐sized problems with a reasonable amount of computational efforts.
Analyzing and Modeling the Maximum Diversity Problem by Zero‐One Programming*
Authors: Kuo Ching‐Chung , Glover Fred , Dhir Krishna S. 
Published: June 2007

ABSTRACTThe problem of maximizing diversity deals with selecting a set of elements from some larger collection such that the selected elements exhibit the greatest variety of characteristics. A new model is proposed in which the concept of diversity is quantifiable and measurable. A quadratic zero‐one model is formulated for diversity maximization. Based upon the formulation, it is shown that the maximum diversity problem is NP‐hard. Two equivalent linear integer programs are then presented that offer progressively greater computational efficiency. Another formulation is also introduced which involves a different diversity objective. An example is given to illustrate how additional considerations can be incorporated into the maximum diversity model.
The Contingent‐Claims Approach to Investment Decisions*
Authors: Chung Kee H. 
Published: June 2007

ABSTRACTThis paper presents a contingent‐claims approach to project valuation when capital expenditures are made sequentially over time. It focuses on an important facet of sequential investment projects that the firm can undertake—or pass up projects—as more information becomes available. The contingent‐claims approach takes account of this important feature of firms' investment decision process, whereas the traditional capital budgeting procedure does not. Since the traditional method does not reflect the options nature of investment opportunities, it underestimates the value of sequential investment projects. As a result, a naive implementation of the traditional capital budgeting procedure could result in rejecting profitable projects. The extent of undervaluation associated with the traditional capital budgeting procedure is greater when the correlation between the random component of the future asset value and that of the required capital expenditure is smaller and/or when the growth rate of the required capital expenditure is higher.
Necessary and Sufficient Conditions of Unacceptable Solutions in LP Discriminant Analysis*
Authors: Xiao Baichun 
Published: June 2007

ABSTRACTCharacterization of unacceptable solutions for linear programming (LP) discriminant models have been discussed in the literature and the results presented so far are not satisfactory. This paper establishes necessary and sufficient conditions of unacceptable solutions for a number of LP models, addresses the practical implications of these conditions, and discusses the relationship between unacceptable solutions and multiple solutions.
Assessing Service Level Targets in Production and Inventory Planning*
Authors: Greis Noel P. 
Published: June 2007

ABSTRACTIn production and stock planning, the relationship between customer service, defined as the ability to meet demand for finished goods from in‐stock inventory, and expected profits or expected costs can be represented by a simple reliability curve. The shape of this curve depends upon the parameters of the demand process, specifically the expected level of demand, standard deviation and correlation structure, as well as upon the capacities and initial state of the production and inventory system. A model is presented which explicitly determines this trade‐off curve for a firm. The model is intended both as an operational model to aid managers in setting revenue and service targets which are compatible with the capacities and resources of the firm, and as a tool for exploring relationships between the parameters of the demand process and the constraints of the physical production and inventory system. The results illustrate that the level of risk depends strongly on the variability of the demand process, the cost structure, the capacities and initial state of the system and, to a lesser extent, the correlation in demand between succeeding periods. Results suggest that establishing service level targets consistent with the firm's strategic orientation must be done in consideration of both the characteristics of the demand process and the capacities of the production and inventory system. The model provides a tool for estimating the premium above unit cost which must be paid to provide a designated service level.
User Perceptions of Decision Support Effectiveness: Two Production Planning Experiments*
Authors: Davis Fred D. , Kottemann Jeffrey E. 
Published: June 2007

ABSTRACTDecision support systems continue to be very popular in business, despite mixed research evidence as to their effectiveness. We hypothesize that what‐if analysis, a prominent feature of most decision support systems, creates an “illusion of control” causing users to overestimate its effectiveness. Two experiments involving a production planning task are reported which examine decision makers' perceptions of the effectiveness of what‐if analysis relative to the alternatives of unaided decision making, and quantitative decision rules. Experiment 1 found that almost all subjects believed what‐if analysis was superior to unaided decision making, although using what‐if analysis had no significant effect on performance. Experiment 2 found that decision makers were indifferent between what‐if analysis and a quantitative decision rule which, if used, would have led to significant cost savings. Thus, what‐if analysis did create an illusion of control: decision makers perceived performance differences where none existed, and did not detect large differences when they were present. In both experiments, decision makers exhibited difficulty realizing that their positive beliefs about what‐if analysis were exaggerated. Such misjudgments could lead people to continue using what‐if analysis even when it is not beneficial and to avoid potentially superior decision support technologies.
The Impact of Quality Management Practices on Performance and Competitive Advantage
Authors: Flynn Barbara B. , Schroeder Roger G. , Sakakibara Sadao 
Published: June 2007

As decision makers become more involved in implementing Total Quality Management, questions are raised about which management practices should be emphasized. In this exploratory investigation of the relationship of specific quality management practices to quality performance, a framework was constructed. It focuses on both core quality management practices and on the infrastructure that creates an environment supportive of their use. In addition, it incorporates two measures of quality performance and their role in establishing and sustaining a competitive advantage.Path analysis was used to test the proposed model, with multiple regression analysis determining the path coefficients, which were decomposed into their various effects. Weak linkages were eliminated. The trimmed model indicated that perceived quality market outcomes were primarily related to statistical control/feedback and the product design process, while the internal measure of percent that passed final inspection without requiring rework was strongly related to process flow management and to statistical control/feedback, to a lesser extent. Both measures of quality performance were related to competitive advantage. Important infrastructure components included top management support and workforce management. Supplier relationships and work attitudes were also related to some of the core quality practices and quality performance measures.The results were interpreted in light of Hill's concept of order winners and order qualifiers and Garvin's eight dimensions of quality. They indicate that different core quality management practices lead to success in different dimensions of quality, and that those dimensions function differently as order winners and order qualifiers.
Specifying Critical Inputs in a Genetic Algorithm‐driven Decision Support System: An Automated Facility*
Authors: Pakath Ramakrishnan , Zaveri Jigish S. 
Published: June 2007

ABSTRACTWe present a simple scheme for the automated, iterative specification of the genetic mutation, crossover, and reproduction (usage) probabilities during run time for a specific genetic algorithm‐driven tool. The tool is intended for supporting static scheduling decisions in flexible manufacturing systems. Using a randomly generated (base) test problem instance, we first assess the method by using it to determine the appropriate levels for specific types of mutation and crossover operators. The level for the third operator, reproduction, may then be inferred. We next report on its ability to choose one or more appropriate crossovers from a set of many such operators. Finally, we compare the method's performance with that of approaches suggested in prior research for the base problem and a number of other test problems. Our experimental findings within the specific scheduling domain studied suggest that the simple method could potentially be a valuable addition to any genetic algorithmbased decision support tool. It is, therefore, worthy of additional investigations.
Identification of the Critical Factors of TQM*
Authors: Black Simon A. , Porter Leslie J. 
Published: June 2007

Current concepts in the field of Total Quality Management (TQM) are largely based upon case studies, anecdotal evidence and the prescriptions of leading “gurus.” Consequently, there is little consensus on which factors are critical to the success of the approach. Few attempts have been made to scientifically synthesise frameworks for measuring quality management practices, and a methodology for examining the issue has yet to be established. This has meant that current TQM models, such as the Malcolm Baldrige Award, have not been constructed or validated by empirical means. This paper presents a research methodology that can be used to improve self‐assessment frameworks, such as the Baldrige Award, to better inform organisations in the development of Total Quality Systems.The research extracts a series of items from the Baldrige model and established literature. These items formed the basis of a questionnaire sent to over 200 managers. Data was examined using several well‐established analytical techniques that identified 10 critical factors of TQM. These factors were shown to be reliable and valid, and offer new insights into our understanding of TQM.
Risk, Ambiguity, and Decision Choice: Some Additional Evidence*
Authors: Ghosh Dipankar , Ray Manash R. 
Published: June 2007

ABSTRACTThis paper extends prior research by jointly assessing the roles of risk attitude and tolerance for ambiguity in predicting choice. An experiment examined the effects of these variables on decisions made in four different scenarios. The four scenarios (treatment combinations) were generated by manipulating risk and ambiguity into two levels (high and low). The context was defined in terms of a sample size selection problem. The second issue explored was the effect of attitudes toward risk and ambiguity on decision confidence.The results indicate that (1) both risk attitude and ambiguity intolerance determined choice behavior, (2) the roles of these individual attitudes depend on the levels of the two treatment variables of risk and ambiguity, (3) the presence of ambiguity accentuates the perception of risk in individual subjects, and (4) decision makers who are less risk averse, and have more tolerance for ambiguity, display greater confidence in their choice. The paper discusses some of the managerial implications of the results.
What to Learn from Near Misses: An Inductive Learning Approach to Credit Risk Assessment
Authors: Tessmer Antoinette Canart 
Published: June 2007

ABSTRACTThis paper presents a new dimension of inductive learning for credit risk analysis based on the specific impact of Type I and Type II credit errors on the accuracy of the learning process. A Dynamic Updating Process is proposed to refine the credit granting decision over time and therefore improve the accuracy of the learning process. The new dimension is tested on credit files of small Belgian businesses. Results indicate an improvement of the learning process in terms of predictive accuracy, stability, and conceptual validity of the final decision tree.