Reducing Weight Flexibility in Data Envelopment Analysis

This paper explores the consequences of total weight flexibility in data envelopment analysis (DEA) assessments of the efficiency of decision-making units, and it suggests one possible way of limiting such flexibility. It is suggested that total weight flexibility can lead to some decision-making units being assessed, in effect, on only a small subset of their inputs and outputs, while the rest of their inputs and outputs are all but ignored. Constraining the weights in DEA assessments overcomes this problem. The paper suggests one way in which constraints can be placed for the case where the decision-making units to be assessed use only a single input. The method is illustrated using data on local-authority rates departments. Finally, the paper discusses the interpretation and usefulness of the information obtained from DEA assessments involving weights constraints.     

Stochastic Dynamic Programming Formulation for a Wastewater Treatment Decision-Making Framework

In this paper, a decision-making framework (DMF) based on stochastic dynamic programming (SDP) is presented for a wastewater treatment system, consisting of a liquid treatment line with eleven levels and a solid treatment line with six levels (Chen and Beck, 1997). A continuous-state SDP solution approach based on the OA/MARS method (Chen, Ruppert, and Shoemaker, 1999) is employed, which provides an efficient method for representing a wide range of possible influent conditions. The DMF is used to evaluate current and emerging technologies for the multi-level liquid and solid lines of the wastewater treatment system. At each level, one technology unit is selected out of a set of options which includes the empty unit. The DMF provides a comparison on possible technologies for screening which types of technologies may best be further developed in order for an urban wastewater infrastructure to be judged progressively more sustainable. The results indicate that one or a pair of technologies are dominant in each level. The cheap, lower-technology unit processes receive a mixed review. Some of them are selected as the most promising technology units while the others are not considered as good candidates.     

A Study of Real-Time Human Decision-Making Using a Plant Simulator

This paper describes the use of a novel interface device to display to an operator the behaviour of a computer-simulated plant and to allow him to control the activity of that simulated plant. The human decision-making functions of the operator were studied by analysing the control strategies used and the plans and tactics developed by him in the course of experimental control sessions.The simulated plant used in these experiments was a five-furnace electric-arc steel-melting shop. The operator's task concerned control of electric power to the whole shop to maximize steel output under the restriction of a given energy consumption rate. Undergraduates and steelworks staff familiar with the actual process simulated were used as operators in the experiments.Several notational systems for describing possible and actual control strategies have been studied and the results suggest that one of these provides a feasible method of describing human thought processes during decision-making.     

An extended assignment problem considering multiple inputs and outputs

The existing assignment problems for assigning n jobs to n individuals are limited to the considerations of cost or profit incurred by each possible assignment. However, in real applications, various inputs and outputs are usually concerned in an assignment problem, such as a general decision-making problem. This paper develops a procedure for resolving assignment problems with multiple incommensurate inputs and outputs for each possible assignment. The concept of the relative efficiency in using various resources, instead of cost or profit, is adopted for each possible assignment of the problem. Data envelopment analysis (DEA) is employed in this paper to measure the efficiency of one assignment relative to that of the others according to a set of decision-making units. A composite efficiency index, consisting of two kinds of relative efficiencies under different comparison bases, is defined to serve as the performance measurement of each possible assignment in the problem formulation. A mathematical programming model for the extended assignment problem is proposed, which is then expressed as a classical integer linear programming model to determine the assignments with the maximum efficiency. A numerical example is used to demonstrate the approach.     

Bivariate interval semi-infinite programming with an application to environmental decision-making analysis

This paper proposed a bivariate interval semi-infinite linear programming (BV-ISIP) method to address a type decision-making problem where various uncertainties exist in functional relations and parameter uncertainty. The performance of the method is also demonstrated via an illustrative example and an environmental decision-making problem. As BV-ISIP guarantees that each of the constraints is satisfied under all possible levels of independent variables, the system-failure risk can be reduced. The BV-ISIP solutions can be more robust to the variation of coefficients associated with independent variables than the ILP ones. Other features of BV-ISIP are as follows: (i) flexible decision-making schemes can be developed for decision makers in terms of the BV-ISIP solutions; (ii) BV-ISIP can conveniently be applied to many large-scale optimization problems as no significantly-increased computational costs are required; (iii) the method can easily be improved for addressing functional intervals associated with multiple independent variables.     

An extended compromise ratio model with an application to reservoir flood control operation under an interval-valued intuitionistic fuzzy environment

This paper presents a novel multiple attribute group decision-making (MAGDM) model based on the compromise ratio method under an interval-valued intuitionistic fuzzy (IVIF) environment. The compromise ratio method under uncertainty is introduced by a group of experts based on the concept that the chosen alternative should be as close as possible to the IVIF-positive-ideal point and as far away from the IVIF-negative-ideal point as possible concurrently. First, an IVIF-weighted geometric averaging (IVIFWGA) operator is employed to aggregate all individual IVIF-decision matrices provided by a group of experts into a collective IVIF-decision matrix. Two new basic IVIF-operations are introduced to handle the evaluation process. Then, an extended collective index in an IVIF environment is proposed to discriminate among alternatives for the evaluation process in terms of subjective and objective information. Finally, to demonstrate the suitability and applicability of the proposed IVIF-MAGDM model, an application example of reservoir flood control operation is given from the recent literature.     

一种风险型决策的决策方法

本文阐明了方案益损值和自然状态概率无准确估计的风险型决策问题的决策方法。证明了在期望值决策准则下，用等概率分层随机抽样得到的方案收益期望值的均值，与用简单随机抽样得到的相同。用正交表进行分层随机抽样，得到方案收益期望值服从正态分布的均值和方差。再根据统计推断理论对方案作出选择，并给出决策的风险估计。     

An ordinal sorting method for group decision-making

Several group decision-making methods were proposed with the aim to establish, from individual opinions, a collective one. However, the literature review of such methods show that, until now, few of them accept individual preferences expressed in partial pre-orders or, more generally, in preferences relational systems (p.r.s.). In addition, the majority of these methods produce a ranking on the alternative set, i.e. they concern the ranking decision-making problematic. In fact, the efforts provided to develop methods which treat, for example, the sorting problematic, remain insufficient. For these reasons, we propose in this paper an ordinal sorting method which determines, from individual p.r.s., at least one collective assignment which takes into account the relative importance of the members. If there is more than one collective assignment, an interactive procedure to reach a consensus assignment is proposed.     

Information,Incentives and Decentralized Decision-Making in a Bayesian Framework

This paper examines the problem of choosing an organization structure for decision-making for two-level organizations under various information conditions. Decentralized decision-making (DDM), centralized decision-making with reporting by division (CDRD), and centralized decision-making (CDM) are considered. The information conditions pertain to the observability of states and actions. It is shown that if the party not making the decisions can infer the choice of action from the ex post knowledge of the state, then DDM and CDRD are essentially equivalent in that it is possible to devise optimal incentive schemes in both cases. If, however, the action choice cannot be inferred, then CDRD is at least as preferred as DDM by the centre (regulator). If ex post observation (or inference) of the state can be made only by the party making the decision, we show that the centre (regulator) prefers CDRD to DDM. For this case, we derive an incentive scheme which elicits truthful information from the division (regulated entity). Finally, the incentive schemes are applied to the problem of regulating an industry that pollutes the environment.     

The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making

QUALIFLEX, a generalization of Jacquet-Lagreze’s permutation method, is a useful outranking method in decision analysis because of its flexibility with respect to cardinal and ordinal information. This paper develops an extended QUALIFLEX method for handling multiple criteria decision-making problems in the context of interval type-2 fuzzy sets. Interval type-2 fuzzy sets contain membership values that are crisp intervals, which are the most widely used of the higher order fuzzy sets because of their relative simplicity. Using the linguistic rating system converted into interval type-2 trapezoidal fuzzy numbers, the extended QUALIFLEX method investigates all possible permutations of the alternatives with respect to the level of concordance of the complete preference order. Based on a signed distance-based approach, this paper proposes the concordance/discordance index, the weighted concordance/discordance index, and the comprehensive concordance/discordance index as evaluative criteria of the chosen hypothesis for ranking the alternatives. The feasibility and applicability of the proposed methods are illustrated by a medical decision-making problem concerning acute inflammatory demyelinating disease, and a comparative analysis with another outranking approach is conducted to validate the effectiveness of the proposed methodology.     

Fuzzy random regression based multi-attribute evaluation and its application to oil palm fruit grading

Multi-attribute decision-making is usually concerned with weighting alternatives, thereby requiring weight information for decision attributes from a decision maker. However, the assignment of an attribute’s weight is sometimes difficult, and may vary from one decision maker to another. Additionally, imprecision and vagueness may affect each judgment in the decision-making process. That is, in a real application, various statistical data may be imprecise or linguistically as well as numerically vague. Given this coexistence of random and fuzzy information, the data cannot be adequately treated by simply using the formalism of random variables. To address this problem, fuzzy random variables are introduced as an integral component of regression models. Thus, in this paper, we proposed a fuzzy random multi-attribute evaluation model with confidence intervals using expectations and variances of fuzzy random variables. The proposed model is applied to oil palm fruit grading, as the quality inspection process for fruits requires a method to ensure product quality. We include simulation results and highlight the advantage of the proposed method in handling the existence of fuzzy random information.     

A generalized activity network model

In recent years activity networks for projects with both random and deterministic alternative outcomes in key nodes have been considered. The developed control algorithm chooses an optimal outcome direction at every deterministic alternative node which is reached in the course of the project's realization. At each routine decision-making node, the algorithm singles out all the subnetworks (the so-called joint variants) which correspond to all possible outcomes from that node. Decision-making results in determining the optimal joint variant and following the optimal direction up to the next decision-making node. However, such models cover a limited class of alternative networks, namely, only fully-divisible networks which can be subdivided into nonintersecting fragments. In this paper, a more generalized activity network is considered. The model can be applied to a broader spectrum of R&D projects and can be used for all types of alternative networks, for example, for non-divisible networks comprising nodes with simultaneously ‘must follow’, random ‘exclusive OR’ and deterministic ‘exclusive or’ emitters. The branching activities of the third type refer to decision-making outcomes; choosing the optimal outcome is the sole prerogative of the project's management. Such a model is a more universal activity network; we will call it GAAN—Generalized Alternative Activity Network. The problem is to determine the joint variant optimizing the mean value of the objective function subject to restricted mean values of several other criteria. We will prove that such a problem is a NP-complete one. Thus, in general, the exact solution of the problem may be obtained only by looking through all the joint variants on the basis of their proper enumeration. To enumerate the joint variants we will use the lexicographical method in combination with some techniques of discrete optimization. A numerical example will be presented. Various application areas are considered.     

Multicriteria fuzzy decision-making method using entropy weights-based correlation coefficients of interval-valued intuitionistic fuzzy sets

A multicriteria fuzzy decision-making method based on weighted correlation coefficients using entropy weights is proposed under interval-valued intuitionistic fuzzy environment for the some situations where the information about criteria weights for alternatives is completely unknown. To determine the entropy weights with respect to a decision matrix provided as interval-valued intuitionistic fuzzy sets (IVIFSs), we propose two entropy measures for IVIFSs and establish an entropy weight model, which can be used to determine the criteria weights on alternatives, and then propose an evaluation formula of weighted correlation coefficient between an alternative and the ideal alternative. The alternatives can be ranked and the most desirable one(s) can be selected according to the values of the weighted correlation coefficients. Finally, two applied examples demonstrate the applicability and benefit of the proposed method: it is capable for handling the multicriteria fuzzy decision-making problems with completely unknown weights for criteria.     

An identification algorithm in fuzzy relational systems

The paper presents an effective identification method in fuzzy relational systems. We propose an algorithm for constructing models on the basis of fuzzy and nonfuzzy data with the aid of fuzzy discretization and clustering techniques. The usefulness of the method provided is demonstrated by means of two numerical examples. Also a possible way of generating a linguistic decision-making algorithm is discussed.     

TOPSIS for bi-level MODM problems

TOPSIS (technique for order preference by similarity to ideal solution) is a multiple criteria method to identify solutions from a finite set of alternatives based upon simultaneous minimization of distance from an ideal point and maximization of distance from a nadir point. This paper proposes a fuzzy TOPSIS algorithm to solve bi-level multi-objective decision-making (BL-MODM) problems, and in which the objective function at each level are non-linear functions which are to be maximized. The proposed model for getting the satisfactory solution of the BL-MODM problems includes the membership functions for the upper level decision variables vector with possible tolerances, the membership function of the distance function from the positive ideal solution (PIS) and the membership function of the distance function from the negative ideal solution (NIS). A numerical illustrative example is given to clarify the proposed TOPSIS approach of this paper.     

应急不完全信息环境下的混合多属性协同决策方法

科学的应急救援协同决策理论方法,不但能使应急管理系统更好地发挥作用,而且能使政府及公众的应急救援行为更加规范和有序.为此,针对应急环境下决策信息不完全的背景,研究构建了一类综合集成网络层次分析法(ANP)、证据理论(D-SEvidence Theory)以及改进的理想点法(TOPSIS)的混合多属性应急协同决策方法.其中ANP用于处理应急救援方案非独立和相互联系的评价指标权重的确立,D-S Theory用于处理不完全信息条件下多个部门对应急救援候选方案的不同评价信息融合,改进的理想点法(TOPSIS)则用于最终候选应急救援方案的排序.研究结果表明,所提出的混合多属性协同决策方法不仅在理论上有所集成创新,而且在实际应用中可以有效解决应急环境下多部门或多环节协同决策问题.     

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.     

Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology

Fuzzy analytic hierarchy process (AHP) proves to be a very useful methodology for multiple criteria decision-making in fuzzy environments, which has found substantial applications in recent years. The vast majority of the applications use a crisp point estimate method such as the extent analysis or the fuzzy preference programming (FPP) based nonlinear method for fuzzy AHP priority derivation. The extent analysis has been revealed to be invalid and the weights derived by this method do not represent the relative importance of decision criteria or alternatives. The FPP-based nonlinear priority method also turns out to be subject to significant drawbacks, one of which is that it may produce multiple, even conflict priority vectors for a fuzzy pairwise comparison matrix, leading to entirely different conclusions. To address these drawbacks and provide a valid yet practical priority method for fuzzy AHP, this paper proposes a logarithmic fuzzy preference programming (LFPP) based methodology for fuzzy AHP priority derivation, which formulates the priorities of a fuzzy pairwise comparison matrix as a logarithmic nonlinear programming and derives crisp priorities from fuzzy pairwise comparison matrices. Numerical examples are tested to show the advantages of the proposed methodology and its potential applications in fuzzy AHP decision-making.     

Operational Research for Strategic Decision-Making: The Role of World-View

Standard Operational Research analysis of the problem is of limited value to those making complex strategic decisions for which adequate modelling is rarely possible. It may be more helpful to provide models which make explicit the decision-making process. These could enable the decision-maker to be more aware of possible dangers leading to less preferred outcomes. As a demonstration, a dynamic systems model is developed and used to investigate the decision-making of the Kennedy Administration during the Cuban Missile Crisis. Conclusions from this, and several case studies by other authors, emphasize the great importance of the world-view of the decision-making group in determining their actions.     

Reconsideration of the present value method

In investment decision-making, the net present value method is widely used as one of the best decision rules (techniques or criteria). At the same time, it is also used to evaluate decision alternatives for a long range period of time, in economics or even in control theory. Its theoretical validation as the best method for investment decision-making has been based on a basis such that the best technique (investment decision rule) will maximize shareholders' wealth which is measured by the present value of cash flows discounted at the opportunity cost of capital. Such a theoretical requirement as maximizing shareholders' wealth is very important for investment decision-makings. This requirement implies that an ordering relation of projects determined by the best investment rule must be order-isomorphic to that determined by the measure of shareholders' wealth. This order-isomorphism can be represented by necessary and sufficient conditions (or separate criteria). However, they are not suitable for comparing investment decision rules, because they are designed for selecting the best investment decision rule. At the same time, the other dominance of the net present value method over other investment rules is also found in its decision-theoretical aspects. Formulating the net present value method, internal rate of return method and simple sum method in an axiomatic fashion, the net present value method is compared with the other rules, and is shown to have enough clarity and simplicity in theory and practice.