How to give away £100 million: information for senior decision makers

Every year the National Coal Board divides about £100 million capital for purchasing mining machinery between 12 mining regions. This paper discusses how the allocation should be made, and in particular what information should be provided to the senior management staff who take the final decision.     

Developing a straightforward approach for group decision making based on determining weights of decision makers

Group decision making is an active area of research within multiple attribute decision making. This paper assumes that all the decision makers (DMs) are not equally qualified to contribute equitably to the decision process. The aim of this paper is to develop an approach to determine weights of DMs, in which the decision information on alternatives with respect to attributes, provided by each DM, is represented in the form of interval data. We define the average of all individual decisions as the positive ideal decision (PID), and the maximum separation from PID as the negative ideal decision, which are characterized by a matrix, respectively. The weight of each DM is determined according to the Euclidean distances between the individual decision and ideal decisions. By using the obtained weights of DMs, all individual decisions are aggregated into a collective decision. Then the alternatives is ranked based on the collective decision. Meanwhile, this paper also gives a humanized decision method by using an optimistic coefficient, which is used in adjusting the relative importance between profit and risk. Finally, we give an example to illustrate the developed approach.     

The interface with decision makers and some experimental results in interactive multiple objective programming methods

We discuss the basic concepts of interactive methods in multiple objective linear programming. The underlying mathematical formulation is investigated. It turns out that these methods are all based on three different types of scalarization. This explains the exchange of information—the interface—between the decision makers and the model. Problems in designing this interface will be investigated using results from psychology concerning the ability of a human being to oversee a large number of stimuli. We present the results of some practical experiments with a model of the energy system in the Netherlands, and finally we draw some conclusions concerning the possible use of interactive methods in long-term planning.     

An avoiding information loss approach to group decision making

Owing to the increasing complexity in various management, aggregating experts’ knowledge and experiences to make an appropriate decision is an important research area. However, with aggregation of information in decision process, some information may be lost. The aim of this paper is to present a systematic methodology avoiding information loss for group decision making. An extended TOPSIS method is twice used to the current method, which is first used to determine the weights of decision makers, and second used to rank the preference order of alternatives. The proposed approach is straightforward and has no aggregation of information. A comparison of proposed method with other methods is also done. Finally, a numerical example for supplier selection is given to illustrate the application of the introduced method.     

The fuzzy decision problem: An approach to the point estimation problem with fuzzy information

This paper is devoted to the extension of the Bayesian method for the point estimation, when the available information is ‘vague’.In the nonfuzzy case, the parametric estimation can be approached as a particularization in the statistical decision problem. This motivates us to accomplish the mentioned extension by looking at the parametric estimation in the fuzzy case as a special situation in the fuzzy decision problem (defined by Tanaka, Okuda and Asia).In this way, concepts in the fuzzy decision problem are first ‘expressed’ in the estimation terminology. Then, on the basis of these concepts, we shall introduce some notions and state some interesting results. Finally, several illustrative examples will be exposed.     

Making the information systems outsourcing decision: A transaction cost approach to analyzing outsourcing decision problems

During the last several years outsourcing has emerged as a major issue in information systems management. As competitive forces impinge on business firms, senior managers are re-structuring their organizations with an eye on attaining or maintaining competitive advantage. Various strategies to IS outsourcing have emerged, for example, some outsourcers contract with a sole vendor while others contract with several. To date no studies have been done to determine which strategies are appropriate under what conditions. And while some firms have achieved varying degrees of success with any of these strategies, many have encountered significant difficulties. How then are managers to choose from a set of options that which is most appropriate for their firm? Outsourcing problems are complex and entail considerable implications for the strategy of the firm. A wrong decision can result in loss of core competencies and capabilities, and exposure to unexpected risks. Although many articles have appeared on outsourcing, few have extended the discussion beyond simple cost–benefit analysis. In this paper we discuss a transaction cost theory approach to the analysis of outsourcing decision making. Our approach provides managers with a strategy and techniques for analyzing some of the more subtle issues they may face when dealing with complex outsourcing decisions problems.     

The fuzzy decision problem: An approach to the problem of testing statistical hypotheses with fuzzy information

This paper is devoted to the problems of testing statistical hypotheses about an experiment, when the available information from its sampling is `vague'. When the information supplied by the experimental sampling is exact, the problems of testing statistical hypotheses about the experiment can be regarded as a particular statistical decision problem. In addition, decision procedures may be used in problems of testing hypotheses.In a similar manner, the problem of testing statistical hypotheses about an experiment when the available sample information is vague, is approached in this paper as a particular fuzzy decision problem (as defined by Tanaka, Okuda and Asai). This approach assumes that the previous information about the experiment can be expressed by means of certain conditional probabilistic information, whereas the present information about it can be expressed by means of fuzzy information. The preceding framework allows us to extend the notion of risk function and some nonfuzzy decision procedures to the fuzzy case, and particularize them to the problem of testing.Finally, several illustrative examples are presented.     

An approach to aggregation of ordinal information in multi-criteria multi-person decision making using Choquet integral of Fubini type

An algorithm for the selection among n alternatives based on the evaluation of n (distinct) groups of persons according to the same m criteria is described. The evaluation of each person for each criterion is represented by a proportional ordinal 2-tuple and the overall opinion is aggregated by a pair of quantifier-guided ordered weighted averaging (OWA) aggregation and (floating) anchoring value-based ordered weighted averaging (AV-OWA) aggregation operators. An example is provided to illustrate the algorithm. The decision function of the algorithm is shown to be a Choquet integral of the associated function of two variables (corresponding to the two aggregation processes in the algorithm) which can be accomplished alternatively by a Choquet integral of Fubini type.     

On the application of TRIMAP to problems with multiple decision makers

In this paper the potentialities of TRIMAP to provide decision support in multiobjective problems with multiple decision makers are exploited. TRIMAP is an interactive three-objective linear programming package which enables a progressive and selective learning of the nondominated solution set. The aim is to aid the opposing parties in exploring their own preferences and to explore the dynamic nature of the negotiation process.     

Modelling of human decision-making in simulation models of conflict using experimental gaming

Over the past few years we have developed a number of key components which allow us to capture the effects of command decision-making in simulation models of conflict. One of these key components is the Rapid Planning process, which is based on the psychological construct of naturalistic decision-making. Validating such theoretical approaches and assumptions is an on-going activity which helps build confidence over time that our models and theories are valid within our domain of application, and can be used as the basis of sound advice to our customers in the UK Ministry of Defence. Here we present one piece of that validation; a series of command decision-making games which were analysed using a probit-based statistical approach. In the paper we show that the results of these games gives strong support to the way in which rapid planning captures such decision-making in algorithmic form.     

A method for group decision-making based on determining weights of decision makers using TOPSIS

In general, weights of decision makers (DMs) play a very important role in multiple attribute group decision-making (MAGDM), how to measure the weights of DMs is an interesting research topic. This paper presents a new approach for determining weights of DMs in group decision environment based on an extended TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. We define the positive ideal solution as the average of group decision. The negative ideal solution includes two parts: left and right negative ideal solution, which are the minimum and maximum matrixes of group decision, respectively. We give an example to illustrate the developed approach. Finally, the advantages and disadvantages of this study are also compared.     

The GOWA operator based approach to multiattribute decision making using intuitionistic fuzzy sets

The aim of this paper is to develop a methodology for solving multiattribute decision making (MADM) problems in which weights of attributes and ratings of alternatives on qualitative and quantitative attributes are expressed with intuitionistic fuzzy sets (IFSs). In this methodology, relative membership/satisfaction and non-membership/non-satisfaction degrees are formulated to construct IFSs for numerical values of alternatives on quantitative attributes. Alternatives on qualitative attributes are evaluated using linguistic variables and semantics which are parameterized by IFSs. Hereby, weights and ratings of alternatives on both qualitative attributes and quantitative attributes may be expressed with IFSs in a unified way. The generalized ordered weighted averaging (GOWA) operator is further extended to the situations in which the argument values are IFSs and thus a methodology is developed to solve MADM problems using IFSs. Validity and applicability of the proposed methodology in this paper are illustrated with a real numerical example.     

An interactive sorting approach based on the assignment examples of multiple decision makers with different priorities

Although group decision-making is often adopted by many organizations in today??s highly complicated business environment, the multiple criteria sorting (MCS) problem in the context of group decision-making has not been studied sufficiently. To this end, we propose a new interactive method to assist a group of decision makers (DMs) with different priorities. With the goal of relieving the cognitive effort exerted by DMs, this method uses the assignment examples provided by the DMs to draw the parameters for the group preference model. In the iterative MCS process that we employ, the DMs are supported from two perspectives. When the assignment examples provided by the DMs are inconsistent, a RINCON algorithm is developed to identify all the possible solutions that the DMs can use to resolve the conflicts. When the examples are consistent, the potential and the fittest assignments of each alternative are deduced using linear programming techniques. These are then presented to the DMs to help them provide more information for the decision-making process. Furthermore, the priority of each DM is objectively and subjectively evaluated, and then progressively updated to reflect the decision-making performance of a DM at each iteration. Meanwhile, the priorities are integrated into the linear programming model to deduce the fittest assignment, as well as into the RINCON algorithm. Hence, the assignment examples of the DMs with higher priorities are emphasized in the fittest assignment, and are less likely to be revised for inconsistency. A practical example featuring MBA programs is also presented to demonstrate the proposed method.     

An approach to a consistent qualification of decision procedures in probabilistic information processing

Pattern recognition seems to be a rather unique field of interwoven logical inference and decision theory applications. The existence of hundreds of theoretical and real pattern recognition devices forms an ideal basis for research on the structures of various approaches and their comparison. The task of pattern recognition is to select a hypotheses out of a set (e.g.: figures 0, …, 9) on the basis of given data (e.g. the black and white points of a digitized picture). There exists an ideal classifier to solve this problem as the theorem of Bayes provides a logically perfect connection between the input data and the result. But as the so called Bayes-machine proves completely unpractical for real purposesit is “approximated” by more or less complex “real” decision procedures.Thus the theorem of Bayes provides a starting point for the application of statistical considerations and information theory to the analysis of the structures of real decision procedures. The results allow a rather consistent and simple comparison of most decision procedures and provide a tool to estimate the performance of a given procedure in a given environment. The results apply not only to pattern recognition but also to many other fields such as imminence analysis and medical diagnosis.     

Bounds on the value of information in uncertain decision problems

The cost of obtaining good information regarding the various probability distributions needed for the solution of most stochastic decision problems is considerable. It is important to consider questions such as: (1) what minimal amounts of information are sufficient to determine optimal decision rules; (2) what is the value of obtaining knowledge of the actual realization of the random vectors; and (3) what is the value of obtaining some partial information regarding the actual realization of the random vectors. This paper is primarily concerned with questions two and three when the decision maker has an a priori knowledge of the joint distribution function of the random variables. Some remarks are made regarding results along the lines of question one. Mention is made of assumptions sufficient so that knowledge of means, or of means, variances, co-variances and n-moments are sufficient for the calculation of optimal decision rules. The analysis of the second question leads to the development of bounds on the value of perfect information. For multiperiod problems it is important to consider when the perfect information is available. Jensen's inequality is the key tool of the analysis. The calculation of the bounds requires the solution of nonlinear programs and the numerical evaluation of certain functions. Generally speaking, tighter bounds may be obtained only at the expense of additional information and computational complexity. Hence, one may wish to compute some simple bounds to decide upon the advisability of obtaining more information. For the analysis of the value of partial information it is convenient to introduce the notion of a signal. Each signal represents the receipt of certain information, and these signals are drawn from a given probability distribution. When a signal is received, it alters the decision maker's perception of the probability distributions inherent in his decision problem. The choice between different information structures must then take into account these probability distributions as well as the decision maker's preference function. A hierarchy of bounds may be determined for partial information evaluation utilizing the tools of the multiperiod perfect information case. However, the calculation of these bounds is generally considerably more dicult than the calculation of similar boulids in the perfect information case. Most of the analysis is directed towards problems in which the decision maker has a linear utility function over profits, costs or some other numerical variable. However, some of the bounds generalize to the case when the utility function is strictly increasing and concave.     

Existence to singular boundary value problems with sign changing nonlinearities using an approximation method approach

This paper studies the existence of solutions to the singular boundary value problem

The decision rule approach to optimization under uncertainty: methodology and applications

Computational Management Science - Dynamic decision-making under uncertainty has a long and distinguished history in operations research. Due to the curse of dimensionality, solution schemes that...     

Repair strategies in an uncertain environment: Markov decision process approach

This paper deals with repair strategies that maximize the time until a catastrophic event, that is, when there is a vital need for equipment, and the equipment fails to function. We examine the case where the need for the equipment varies over time according to a Markov chain. This means that the environment can be in different states, each with their own probability of the initiating event occurring. We model the form of the optimal policy for repair under this uncertain environment by Markov decision processes.