International Journal of Information Technology & Decision Making

Decision makers often face complicated decision problems with intangible and conflicting criteria. Numerous multi-criteria decision-making (MCDM) methods have been proposed to handle the measurement of the priorities of conflicting tangible/intangible criteria and in turn use them to choose the best alternative for a decision. However, the presence of many MCDM methods bewilders users. The existence of these methods becomes a decision problem in itself, and decision makers may be uncertain about which one to use. Thus the comparative analysis and evaluation of various MCDM methods has come under scrutiny by both researchers and practitioners in order to discover if there are logical, mathematical, social or practical reasons why one method is better than another. Criteria for their evaluation are the first important issue that needs to be resolved. In this paper, 16 criteria are introduced that may be used to judge and evaluate various MCDM methods. The criteria proposed and some guidelines for their evaluation are given to help readers evaluate these MCDM methods.

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming methodology for performance measurement of organizational units that can be utilized normatively and proactively in resource allocation and target setting. While previous studies along this line have commonly utilized exact (crisp) data, the prospective and proactive use of DEA in the activity planning frequently involves uncertainty or impreciseness as to the feasible ranges for resources to be allocated and output targets to be established. The current paper proposes an imprecise DEA-based linear programming method with interval inputs and outputs by addressing the gap of missing the imprecise data settings. For this aim, we present common set of weights models to obtain the interval efficiency of Decision-Making Units (DMUs) with interval inputs and outputs. We then propose DEA-based models to allocate imprecise resources and setting imprecise targets to DMUs such that the interval efficiency of all the DMUs improves or at least remains. The proposed model provides reasonable managerial objectives with respect to the efficiency of the subordinate units when the centralized planner implements resource allocation and target setting. We exemplify the applicability and efficacy of the proposed method using a numerical example in the frame of two distinct scenarios.

Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.

This paper deals with the optimal designing of step-stress accelerated life test (SSALT) for two stress variables. The lifetime of the items follows the Gompertz distribution and the test is subject to termination at a predetermined number of failures of test items (Type II censoring). Furthermore, we model the effects of changing stress as a cumulative exposure (CE) function. This test is presented to obtain the optimal hold times for each combination of stress levels. The optimal test plan with the minimum asymptotic variance (AV) of the maximum likelihood estimator (MLE) of reliability at time $\xi$ is determined. Due to nonlinearity and complexity of the objective function, the particle swarm optimization (PSO) algorithm is developed to calculate the optimal hold times. In this method, the research speed is very fast and optimization ability is more. Finally, simulation results are discussed to illustrate the proposed criteria. For some selected values of the parameters, the effect of initial estimates on optimal values has been studied.

The analytic hierarchy/network process (analytic hierarchy process (AHP)/analytic network process (ANP)) became the most popular tool for weighting criteria in the field of multiple criteria analysis during the 1980s. However, these models often suffer from criticisms because of their theoretical and practical problems. In this paper, the diminishing utility decision model (DUDM) is proposed in order to retain the pros and avoid the cons of the AHP and ANP for weighting criteria. The DUDM integrates the AHP and the concept of diminishing marginal utility in order to model the main and interaction weights of criteria, respectively. From the results of the numerical examples, it can be seen that the proposed method can solve two major limitations of the ANP. First, the proposed method can significantly reduce the number of questions that are asked in the ANP. Second, the proposed method can ensure convergence in many situations and avoid the problem of the ANP with regard to the absorbing state.

DEA and ratio analysis are commonly viewed as alternatively effective tools in assessing the performance of different DMUs. Many researches have been developed to reveal their inherent relationship and to explore the integration of both methods as well. In this paper, we first question Theorem 1 by Wu et al. (International Journal of Information Technology & Decision Making 4(3) (2005) 369–384) and illustrate a counter example to show the nonequivalence between the aggregated ratio model they proposed and CCR model. The errors which existed in its proof are also indicated. Further, an improved aggregated ratio model is established to better comprehend the inner relationship between the two methods. We demonstrate its equivalence to traditional CCR model, and propose an inference that the observed DMU is ratio efficient if and only if it is CCR efficient. Numerical analysis carefully describes the nontrivial difference between the proposed model and that by Wu et al. (International Journal of Information Technology & Decision Making 4(3) (2005) 369–384), and validates the feasibility of our model.

Weight elicitation methods in multi-criteria decision analysis (MCDA) are often cognitively demanding, require too much precision, time and effort. Some of the issues may be remedied by connecting elicitation methods to an inference engine facilitating a quick and easy method for decision-makers to use weaker input statements, yet being able to utilize these statements in a method for decision evaluation. In this paper, we propose a fast and practically useful weight elicitation method, answering to many of the requirements. The method builds on the ideas of rank-order methods, but can also take imprecise cardinal information into account. The method is subsequently employed in two real-life case studies and compared to a case where a simple ratio weight procedure using exact input statements was employed.

Nature-inspired optimization algorithms have become useful in solving difficult optimization problems in different disciplines. Since the introduction of evolutionary algorithms several studies have been conducted on the development of metaheuristic optimization algorithms. Most of these algorithms are inspired by biological phenomenon. In this paper, we introduce a new algorithm inspired by prey-predator interaction of animals. In the algorithm randomly generated solutions are assigned as a predator and preys depending on their performance on the objective function. Their performance can be expressed numerically and is called the survival value. A prey will run towards the pack of preys with better surviving values and away from the predator. The predator chases the prey with the smallest survival value. However, the best prey or the prey with the best survival value performs a local search. Hence the best prey focuses fully on exploitation while the other solution members focus on the exploration of the solution space. The algorithm is tested on selected well-known test problems and a comparison is also done between our algorithm, genetic algorithm and particle swarm optimization. From the simulation result, it is shown that on the selected test problems prey-predator algorithm performs better in achieving the optimal value.

Z Utility Theory refers to a class of nonlinear utility functions for solving Risk and Multiple Criteria Decision-Making problems. Z utility functions are hybrids of additive and nonadditive (nonlinear) functions. This paper addresses the concepts and assessment methods for the additive part of Z-utility functions for multiple criteria problems that satisfy the efficiency (nondominancy) principle. We provide a decision paradigm and guidelines on how to approach, formulate, and solve decision-making problems. We, also, overview the modeling of decision process based on four types of decision-making styles. For multi-criteria problems, a new definition of convex efficiency is introduced. Also polyhedral efficiency is developed for presenting multi-criteria efficiency (nondominancy) graphically. New double helix quasi-linear value functions for multi-criteria are developed. Two types of double helix value functions for solving bi-criteria (Advantages versus Disadvantages) and also risk problems are introduced: Food–Fun curves for expected values and Fight-Flight curves for expected risk values. Ordinal/Cardinal Approach (OCA) for assessment of additive utility functions is developed. Simple consistency tests to determine whether the assessed utility function satisfies ordinal and/or cardinal properties are provided. We show that OCA can also be used to solve outranking problems. We provide a critique of Analytic Hierarchy Process (AHP) for assessing additive value functions and show that the developed Ordinal/Cardinal Approach overcomes the shortcomings of AHP. We also develop a unified/integrated approach for simultaneous assessment of nonlinear value and additive (multi-criteria) utility functions. These results in an additive utility function that can be concave, convex, or hybrid concave/convex based on the nonlinear value function. Finally, we show an interactive paired comparisons approach for solving nonadditive and nonlinear utility functions for bi-criteria decision-making problems. Several illustrative examples are provided. The paper provides reliable and robust approaches for modeling the utility preferences of heterogeneous economic agents in macro and micro-economics.