A DYNAMIC PROGRAMMING MODEL FOR OPTIMAL ALLOCATION OF DECISION TIME

Recurrent decision making by a lower-level manager can be viewed as a sequential decision process in which time and uncertainty are limiting factors. Under these conditions, the manager must determine how to best utilize his decision making time consistent with his own particular set of decision values. A dynamic programming model was devised to determine the optimal (consistent) allocation of decision time among five different types of problems for a sequence of simulated recurrent decision situations. Fifty-one lower-level managers were interviewed about their use of decision time and decision procedures. The model was validated by comparing model assumptions and results with the findings from the interviews. The model was used to determine the effects of variations in the levels of time available and uncertainty upon the optimal allocation of decision time.     

ON THE COST OF NEW ISSUES OF COMMON STOCK: AN AMENDMENT TO THE CONSTANT GROWTH MODEL

Expansion decisions by corporate managements are importantly influenced by their perceptions of the firm's cost of capital and especially the cost of common equity. Their decision making is then influenced by the common perception that new issues of common stock are more costly than retained earnings because of the flotation costs inherent in new issues. Thus a rate of expansion that requires new issues is downgraded in decision making. In this paper the authors show that in actuality new issues of common equity may be less costly than retained earnings. Thus different decision-making implications are clearly involved.     

INCORPORATING SALES FORCE PREFERENCES IN A GOAL PROGRAMMING MODEL FOR THE SALES RESOURCE ALLOCATION PROBLEM

This note presents a model for the sales territory assignment and resource allocation problem. The integer-goal-programming model includes input from the sales representatives in the form of preference values along with organizational goal values from management. The approach integrates the multiple objectiive inputs both for individual sales reprresentatives and for the organization into a single model by employing the approaches of multiattribute utility theory and multicriteria decision making. The purpose of the model is to provide a vehicle for testing various strategies and assessing the impact of those strategies on the sales representatives’utilities and the organization's goals.     

DELIVERY GUARANTEES AND THE INTERDEPENDENCE OF MARKETING AND OPERATIONS

Delivery guarantees are an important element in a customer satisfaction program. When setting delivery guarantees, a firm must consider customer expectations as well as operational constraints. We develop a profit‐maximization model in which a firm's sales organization, with incomplete information on operations' status, solicits orders and quotes delivery dates. If obtained, orders are processed in a make‐to‐order facility, after which revenue is received, minus tardiness penalty if the delivery was later than quoted. We specify conditions for an optimal log‐linear decision rule and provide exact expressions for its effect on arrival rate, mean processing time, and mean cycle time.     

LOGISTICS MANAGEMENT SYSTEM: AN ADVANCED DECISION SUPPORT SYSTEM FOR THE FOURTH DECISION TIER DISPATCH OR SHORT-INTERVAL SCHEDULING

The Logistics Management System (LMS) is a real-time transaction-based system combining decision technologies from AI, MS/OR, and decision support system that serves very successfully as a dispatcher or short-interval scheduler by monitoring and controlling the manufacturing flow of IBM's semiconductor facility near Burlington, Vermont. LMS coordinates the actions and decisions of several logically isolated participants in a serially dependent system of activities. Therefore, it balances the requirements of several goals (cycle time, output, serviceability, and inventory management) that compete for the same resource, exploits emerging opportunities on the manufacturing floor, and reduces the distortion from unplanned events. This paper provides an overview of the LMS application, the concept of interrelated decision tiers in manufacturing decision making, and the need for the dispatch decision tier to successfully reduce apparent randomness. Historically, production and operations management has ignored this decision tier. This has significantly limited our ability to make an impact on the performance of the manufacturing operation.     

A STUDY OF THE PERFORMANCE OF SUBJECTIVE PROBABILITY ASSESSORS

Despite the key role that subjective probabilities play in decisions made under conditions of uncertainty, little is known about the ability of probability assessors in developing these estimates. A literature survey is followed by a review of results from a continuing series of experiments designed to investigate the external accuracy of subjectively assessed probability distributions. Initial findings confirm that probability assessments provided by untrained assessors are of questionable value in predicting the distribution of actual outcomes of uncertain events. Particular difficulty is encountered when subjects attempt to quantify the extremes of their subjective distributions. The impact of extended assessor training and hypotheses regarding the effects of variation in the assessor's information level and the complexity of the assessment task are explored. Implications for applied decision making are drawn, and directions for future investigations are suggested.     

INFLUENCE OF MISSING ATTRIBUTES ON SELECTING A BEST MULTIATTRIBUTED ALTERNATIVE

Alternatives involving many factors arc difficult to evaluate because of multiple underlying competing objectives. If evaluation is based on an incomplete set of factors, and if the purpose of the evaluation is to select a single overall best alternative, inferior alternatives may be selected with surprising frequency and/or severe negative impact. At the same time, sensitivity analysis of evaluation scores based on statistical criteria can easily mask the impact and the frequency of selection of inferior alternatives. In this paper, criteria appropriate to reflect the decision impact are developed and both the frequency and impact of the selection of inferior alternatives are demonstrated empirically. Previous studies based on statistical criteria [1] [9] indicated minimal impact on overall evaluation and selection. This paper demonstrates that high statistical criterion values coexist with frequent and/or serious errors of selection.     

ON THE EQUIVALENCE OF TWO VERSIONS OF THE PRESENT VALUE PROCEDURE

The central issue in equipment investment analysis is the selection of the best of two or more alternatives. A common procedure is to first assume that for each alternative there is an infinite stream of identical pieces of equipment. The present value of the associated infinite stream of revenues and costs is calculated. Then the alternative having the highest present value (where revenues are positive and costs are negative) is selected. The assumption of the infinite stream, though computationally desirable, is not very palatable to the decision maker. In this paper we show that the above procedure is equivalent to using a criterion which is definitely more appealing. The author has found this “equivalence” result to be helpful in teaching the basic concepts of the present value procedure. Also the result should be of interest to practitioners involved in equipment investment decision making.     

A Learning Real Options Framework with Application to Process Design and Capacity Planning

This paper studies the impact of learning on a multi‐staged investment scenario. In contrast to other models in the real options literature in which learning is viewed as a passive consequence of the delay period, this paper quantifies information acquisition by merging statistical decision theory with the real options framework. In this context, real option attributes are discussed from a Bayesian perspective, thresholds are identified for improved decision‐making, and information's impact on downstream decision‐making is discussed. Using real data provided by a firm in the aerospace maintenance, repair, and overhaul industry, the methodology is used to guide a multi‐phased irreversible investment decision involving process design and capacity planning.     

THE IMPACT OF ALTERNATE DECISION TECHNIQUES ON USER BEHAVIOR

The quantitative disciplines have focused their efforts primarily upon more complete and efficient methods for analyzing decision problems. Almost totally neglected in these efforts is the interplay between a decision technique and its ultimate user. This interface is of crucial importance to the continued success of the decision sciences in organizations. Data are presented to support the contention that decision techniques and their resultant impact on job content interact with psychological variables to cause variation in user satisfaction and performance. These results are similar to the effects hypothesized to occur at blue collar levels in organizations.     

THE EFFECTS OF TWO NORMATIVE STRUCTURAL INTERVENTIONS ON ESTABLISHED AND AD HOC GROUPS: IMPLICATIONS FOR IMPROVING DECISION MAKING EFFECTIVENESS1

This paper focuses on a direct comparison of consensual, nominal, and conventional decision making techniques in established and ad hoc groups. The impact of the structural interventions on group decision quality and group attitudes is examined, and the appropriateness of the techniques in various situations is discussed.     

An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application

This paper studies the group decision making problem with linguistic preference relations. We first study the consensus measure between the individual preference relations and the collective (group) preference relation by defining the concept of degree of similarity between two linguistic values and two linguistic preference relations. Then we propose a concept of the acceptance consensus threshold value for group decision making with linguistic preference information. We show that the consensus between individual preference relations and the collective (group) preference relation is greater than the weighted similarity degree of a given individual preference relation with respect to other individual preference relations in group decision making with linguistic preference relations. The results will help in the analysis of crucial issues of conflict and agreement among preferences of decision-makers, which affect the consensus of group decision making with linguistic preference relations. Theoretical foundations are then established for the proposed method. Finally, the proposed method is applied to evaluate the degree of consensus of individual overall preference values with respect to the collective overall preference values for multi-attribute group decision making with linguistic information. The main contribution of this paper is twofold. One is to present a new way to measure the consensus between the individual preference relations and the collective (group) preference relation in group decision making with linguistic preference information. Another is to provide an effective approach to evaluating individual overall preference values with respect to the collective overall preference values in multi-attribute group decision making with linguistic information.     

A SIMULATION MODEL OF THE HIGHER EDUCATION SYSTEM OF A STATE1

This paper describes a simulation model to estimate the enrollments and expenditures associated with various policy alternatives for the post-secondary education system of a state, with Indiana as an example. Enrollments, operating expenditures, and construction costs are predicted for every public and private school for each year until 1985. The basic model-building technique employed is regression analysis on historical data, but it is supplemented by other quantitative techniques and by experts' predictions. The model may be used to predict “most likely” future enrollments and costs, to perform sensitivity analyses to better understand the impact of various exogenous variables upon the system, and to evaluate possible policy alternatives. Including all of the schools in a state makes possible the evaluation of policy alternatives such as possible configurations of new two-year public schools, the effect of tuition changes, and the effect of enrollment ceilings for larger state universities.     

Eliciting and Classifying Concerns: A Methodological Critique

Two methodological steps in the study of peoples'concerns are elicitation and classification. Elicitation of concerns through analytical methods such as surveys can be supplemented with techniques that perform more diversively. We present two examples of how this can be accomplished: one in the expert community and one in the lay community. A classification taxonomy is a subjective choice of the researcher and it can only be evaluated against the stated objectives of the research. We present a classification schema that is explicitly oriented toward diagnosing the substantive needs of public discourses about risk decision making. To illustrate how concerns can be elicited in a social setting and how this classification tool can be applied, we report on a public participation exercise in New Jersey where citizens discussed the impacts of land application of sewage sludge at an experimental farm.     

THE EFFECTS OF CHANGING SAMPLE SIZE ON THE ALPHA AND BETA ERRORS: A PEDAGOGIC NOTE

This note discusses a theoretical peculiarity about the interrelationships between the sample size and the two types of decision errors in hypothesis testing theory. It is shown that for a given decision rule, while the alpha error invariably diminishes as the sample size becomes larger, the beta error could increase for certain values of the parameter under the alternative hypothesis.     

DUAL-RESOURCE PARALLEL QUEUES WITH SERVER TRANSFER AND INFORMATION ACCESS DELAYS*

Dual-resource constrained queuing systems contain fewer servers than service facilities. This study uses computer simulation to evaluate several server assignment procedures in a dual-resource system. A field study serves as the basis for developing a model with two service facilities in parallel, a single server, and deterministic information access and transfer delays that can be applied to job shops, computer operating systems, and elevators. Several findings, useful in server assignment decision making, resulted from the study. If first-come, first-served sequencing is used, delaying server assignment at a facility until all jobs are completed reduces both the mean and the variance of job flow time. If shortest-process-time-first sequencing is used, an assignment rule is tested that delays a server at a facility until a sufficiently short job is estimated to have arrived elsewhere. This rule performs best overall in terms of both the mean and variance of flow time. Methods to implement this decision rule easily are discussed.     

Facility location decisions with environmental considerations: a case study from the petrochemical industry

The recently growing concerns of customers and governments about environmental protection and greenhouse gas reduction have forced companies to integrate the topic of environmental sustainability into their decision making. Facility location decisions are of special relevance in this respect because of their strategic nature. Furthermore, many different trade-offs must be considered, for example between operational costs and customer service. But as soon as environmental issues are concerned, other critical issues must be considered as well. Based on a case study from the petrochemical industry, this paper extends two basic facility location models and shows the impact of distribution network-design decisions on the economic and environmental performance of the company. The results show a trade-off between total (distribution) costs and transport carbon emissions.     

A Public Transportation Decision‐Making Model within a Metropolitan Area

Understanding the decision‐making factors associated with public transportation is essential in strategic development of public transportation to improve acceptance and utilization of mass transit systems. This research analyzes factors affecting attitudes toward public transportation and the choice of transportation mode by investigating the public transportation decision‐making process of working professionals using a survey methodology. The objectives of this research are to model the transportation decision‐making process of public transportation users in a metropolitan area and to determine key factors that affect the public transportation choices made by potential public transportation users. This study contributes to the literature by developing and testing an integrated theoretical framework for modeling an individual's public transportation decision‐making process using four independent variables: Perceived Public Transportation Security, Knowledge, Price, and Convenience. We develop the proposed theoretical framework based upon the extant literature and tested it using partial least squares structural equation modeling (PLS‐SEM). Based on the Theory of Reasoned Action, the Theory of Planned Behavior, and utility theory, we develop the factors and refine associated items using confirmatory factor analysis.     

基于公众偏好大数据分析的大群体应急决策质量动态演化研究

针对突发事件大群体应急决策质量及其影响因素的问题，探讨了公众参与视角下突发事件演化的全生命周期，以及各阶段下公众风险感知对应急决策质量的影响，并以“8·12天津港爆炸”为背景案例进行分析。首先，阐述了公众偏好大数据的获取及处理方式，并以此对“8·12天津港爆炸”案例的公众偏好大数据进行预处理和可视化；其次，通过TF-IDF算法提取并量化公众风险感知，建立大群体应急决策质量打分函数，衡量不同阶段的应急决策质量；最后，基于“8·12天津港爆炸”案例的微博数据进行大群体应急决策质量的动态演化分析，得出研究结论和启示。实证表明，所提的模型与方法具有较新颖的研究视角，能够为大数据背景下的应急响应和政府决策提供理论支持和方法辅助。