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This study presents an integrated multi-attribute decision-making (MADM) and data envelopment analysis (DEA) framework for solving problems with heterogeneous attributes. We classify the heterogeneous attributes into desirable and undesirable classes and provide a model for aggregating the attributes' weights and the alternatives' scores. The proposed model is initially designed as a Multiple Objective Decision Making (MODM) problem with a Data Envelopment Analysis (DEA) policy and then reformulated as a linear programming model tackled through a goal programming approach. We apply the proposed model to a set of European countries based on their fulfillment of the 17 Sustainable Development Goals (SDGs) defined by the United Nations. We show the proposed approach minimizes computational efforts and complexities and maximizes the participation and satisfaction of decision-makers. We compare the rankings derived from our model with those obtained from standard MADM techniques such as Euclid and TOPSIS. We illustrate how the different normalization methods are applied to condition the discrimination power of the models and analyze the reversals triggered by TOPSIS relative to the other techniques. We conclude by noting that our model does not rely on the weights defined by the experts to determine the ranking, which constitutes a significant advantage over the standard MADM techniques in strategic evaluation environments. • We present an integrated MADM and DEA model for problems with heterogeneous attributes. • Heterogeneous attributes are classified into desirable and undesirable categories. • Our model does not rely on the weights defined by the experts to determine the ranking. • The rankings derived remain stable independently of the normalization formula applied. • We study the fulfillment of Sustainable Development Goals among European countries. [ABSTRACT FROM AUTHOR]

Information and Communication Technologies (ICTs) have been extensively adopted by firms worldwide due to the significant positive effect on their performance. This fact contrasts with the uncertainty faced by decision makers when entering a country and selecting local firms with which to interact. Consider selecting Decision Making Units (DMUs) according to their relative efficiency, this efficiency being determined via Data Envelopment Analysis (DEA) based on the potential inputs consumed and outputs produced. The values of these variables are uncertain and defined through interval evaluations. Assume now that the interactions may be interrupted several times and new DMUs selected in place of previous ones. The new DMUs may require higher or lower amounts of inputs to produce variable amounts of outputs. The consequences derived from the potential realizations resolving the uncertainty should be incorporated into the DEA problem when deciding which DMUs to interact with and in which order. We study the combinatorial decision framework arising from the potential interactions with new DMUs. A numerical example is provided to complement the problem statement and outline the drawbacks of the existing approaches. It is shown that the selected DMUs and their order may differ substantially when accounting for the complementarities existing among all the DMUs. Moreover, the selection process and any subsequent decision vary with the number of modifications considered relative to the DMU initially selected. A case study analyzing the productive and environmental efficiency of a group of European countries displaying uncertain interval levels of ICT development is presented. • We study efficiency settings where inputs and outputs are given by interval variables. • Decision makers select the units with which to interact and the order of interaction. • We formalize the combinatorial framework arising across uncertain variables and units. • The set of realizations resolving the uncertainty defines an efficient decision path. • We analyze several European countries described via uncertain interval ICT variables. [ABSTRACT FROM AUTHOR]

In recent years, investment in solar energy has increased substantially across countries. Thus, selecting convenient locations for solar farms has become a fundamental problem when determining the investment required due to differences in climatic factors, the type and availability of land, transportation infrastructures, and the quality of power lines. Multi-Criteria Evaluation approaches based on crisp data are generally used in the selection process of optimal locations. However, despite being crisp, the data available when considering the evaluation criteria of the different alternatives constitute a discrete approximation performed on a spatial grid of potential locations. Thus, we introduce a three-stage fuzzy evaluation framework designed to account for the imprecision inherent to the evaluations when identifying the most convenient location for constructing solar power farms. First, we implement ANFIS (Adaptive Neuro-Fuzzy Inference System) on the set of grid intersection crisp data points and derive a coherent set of approximations per each potential discrete location and evaluation criterion. Then, the fuzzy AHP (Analytic Hierarchy Process) method is used to determine the weights of the different criteria considered from the linguistic evaluations provided by different experts. Finally, we define a set of if-then rules combining the different ANFIS evaluation criteria and their weights within a FIS (Fuzzy Inference System) whose output is used to determine the most convenient location for constructing a solar power farm. The efficacy of the proposed evaluation framework is demonstrated through its application to the Iranian regions of Kerman and Yazd. [ABSTRACT FROM AUTHOR]

The current paper analyzes the formation of social networks determined by the preferences of their users, who are endowed with incomplete information regarding the characteristics of other users from who they receive friendship requests. The acceptance or rejection decision is determined by the limited information available when receiving the requests, the expectations of the users regarding the remaining characteristics of the requesters and the resulting improvement in network capacity derived from accepting the friendship requests. We illustrate how the similarity in preferences among users leads to more concentrated clusters within the incomplete information scenario analyzed. At the same time, the emergence of disutility costs derived from a suboptimal decision when accepting to interact with other users increments the dispersion between clusters. In this regard, the inclusion of requesters endowed with average preferences relative to those of the standard users composing the network acts as a connectivity-enhancing mechanism designed to reduce the dispersion and differences existing between clusters. [ABSTRACT FROM AUTHOR]

Consider the decision faced by the user of a social media site of whether or not to accept a friendship request from another user, given the limited amount of information available before deciding. We formalize the problem by defining the expected utility trade-offs derived from the request and simulate the resulting incentives numerically. These incentives provide the basis on which to build social networks determined by the different expectations and preferences of their users. Social networks are generated using a self-organizing map to cluster the decision makers (DMs) by their friendship acceptance behaviour. This behaviour is determined by the distribution of requesters relative to the preferences of the DMs. [ABSTRACT FROM AUTHOR]

Information retrieval behavior of the patient, satisficing and impatient decision makers (DMs) through the initial nodes of a binary decision tree. [Display omitted] • We study the effect of user impatience and search rankings on click-through rates (CTR). • We design a stochastic information retrieval algorithm to mimic real-life CTR behavior. • Two versions of the algorithm are defined to incorporate the degree of user impatience. • The CTRs of the top three ranked alternatives remain stable as users grow impatient. • CTR differences widen as growingly impatient users proceed halfway through the ranking. This study analyzes the effects that the position of the alternatives ranked by a search engine and the relative impatience of users have on their information retrieval behavior. We design a stochastic information retrieval algorithm calibrated to mimic the click-through rates (CTRs) of users observed in real-life environments. We introduce two versions of the mimicking algorithm designed to demonstrate the importance of impatience as a determinant of CTRs conditioned by the alternatives' ranking position. The first version assumes that users proceed sequentially through the ranking until they find an alternative satisficing their expectations. Once they find a satisficing alternative, they continue retrieving information until they observe an alternative that violates their expectations. The second version increases users' impatience, who stop retrieving information as soon as an alternative does not satisfy their expectations – even if it is the top-ranked one. All three algorithmic structures are sufficiently malleable to incorporate any potential modification to users' beliefs and preferences. We simulate sets of 1,000,000 queries to illustrate how the CTRs of the top three ranked alternatives remain stable as users grow impatient, with differences widening as growingly impatient users proceed halfway through the ranking. [ABSTRACT FROM AUTHOR]

Abstract: We consider a rational utility maximizer decision maker (DM) who must gather two pieces of information from a set of multidimensional products before making a choice. We analyze the resulting sequential information acquisition process where the DM tries to find the best possible product subject to his information acquisition constraint. In addition, we introduce publicly observable signals that allow the DM to update his expected utility functions following a standard Bayesian learning rule. Even though it seems intuitively plausible to assume that the transmission of positive and credible information may lead DMs to accept any product signaled more eagerly, this paper illustrates how transmitting credible positive information is not sufficient to decrease the rejection probability faced by the information sender on its set of products. A significant difference in product rejection probabilities arises depending on the characteristic on which signals are issued, as will be illustrated numerically for both risk-neutral and risk-averse DMs. [Copyright &y& Elsevier]

• A decision maker (DM) observes the numerical value assigned by raters to an alternative. • The DM defines an uncertain interval within which the evaluation observed is contained. • Its width depends on the subjective perception differences between the DM and the raters. • The DM may improve upon an uncertain evaluation by retrieving more uncertain information. • Several scenarios are built based on the capacity of the DM to fully assimilate uncertainty. We formalize the information acquisition and choice structure of a decision maker (DM) when the main characteristics defining the alternatives are not directly observed but numerical evaluations of unknown quality are provided by external raters. The DM observes the overall numerical value assigned by the raters to an alternative and defines an uncertain interval within which the evaluation observed is contained. The width of the interval is determined by the subjective perception and evaluation differences existing between the DM and the raters transmitting the information. We analyze the incentives of the DM to improve upon an evaluation contained within an uncertain interval by retrieving further information from the raters of other alternatives. Different scenarios will be developed based on the ability of the DM to fully assimilate uncertainty and the introduction of heuristic approximations to account for the potential frictions arising from uncertainty. One of the main qualities of the current framework is its capacity to formalize interactions among alternatives determined by interval width differences across their characteristics, providing an analytical advantage over the operational complexity involved in the use of fuzzy and intuitionistic fuzzy sets. The same remark applies to the formalization of the interactions across attributes that must be considered when defining sequential decision processes or dynamical systems while dealing with multiple sources of uncertainty. Numerical simulations are provided to compare the different scenarios developed and describe the main consequences derived from ignoring the uncertainty inherent to the evaluations received. In particular, we illustrate the ranking consequences derived from increasing the spread of the evaluation uncertainty, an effect that can be easily combined with the risk attitude exhibited by DMs. The inclusion of both these features bridges the gap between economics, psychology and multiple criteria decision making, whose techniques do not generally account for these differences among DMs. [ABSTRACT FROM AUTHOR]

• We fuse Data Envelopment Analysis (DEA) and Multi-Attribute Decision-Making (MADM). • We compensate for the weaknesses of DEA with the strengths of MADM and vice versa. • We propose and evaluate several new and promising hybrid MADM-DEA methods. • We develop a new technique to evaluate similarity among multiple rankings in MADM. • This new rank absolute deviation is based on the mean absolute deviation method. Analytic hierarchy process (AHP) and data envelopment analysis (DEA) are two popular decision science methods with many business, science, and engineering applications. AHP is a Multi-Attribute Decision-Making (MADM) method for prioritizing alternatives, and DEA is a non-parametric method for estimating production frontiers. Each method has known strengths and weaknesses, and the strengths in one method can overcome the weaknesses in the other. We study several strategies to diminish the weaknesses of DEA with the strengths of pairwise comparisons in AHP. We tackle the low discrimination power inherent to conventional DEA methods. AHP, with its pairwise comparison capability, has been consistently used to increase the discrimination power and accuracy in DEA. We propose and evaluate several new hybrid MADM-DEA models of different computational complexity and consistency, including combinations of the best-worst method (BWM) and its variants with DEA as well as a novel method composed of Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) and DEA. We further develop a new technique for evaluating the similarity among multiple ranking results in MADM. The new simple but powerful technique is called Rank Absolute Deviation (RAD) and is inspired by the mean absolute deviation method. Several numerical examples and a real-world problem are used to demonstrate the applicability and efficacy of the new BWM-DEA, MACBETH-DEA, and RAD methods proposed in this study. We illustrate how less computationally demanding MADM-DEA techniques provide rankings that are highly correlated with the benchmark DEA-AHP and different consensus ranking models. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is generally used to evaluate past performance and multi objective linear programming (MOLP) is often used to plan for future performance goals. In this study, we establish an equivalence relationship between MOLP problems and combined-oriented DEA models using a direction distance function designed to account for desirable and undesirable inputs and outputs together with uncontrollable variables. This equivalence model can be effectively used to support interactive processes and performance measures designed to establish future performance goals while taking into account the preferences of decision makers (DMs). In particular, it allows DMs to consider different efficiency improvement strategies when subject to budgetary restrictions. The applicability of the proposed method and the efficacy of the procedures and algorithms are demonstrated using a case study where the performance of high schools in the City of Philadelphia is evaluated. [ABSTRACT FROM AUTHOR]

We introduce a novel method called subdividing labeling genetic algorithm (SLGA) to solve optimization problems involving n – dimensional continuous nonlinear functions. SLGA is based on the mutation and crossover operators of genetic algorithms, which are applied on a subdivided search space where an integer label is defined on a polytope built on the n – dimensional space. The SLGA method approaches a global optimal solution by reducing the feasible search region in each iteration. One of its main advantages is that it does not require computing the derivatives of the objective function to guarantee convergence. We apply the SLGA method to solve optimization problems involving complex combinatorial and large-scale systems and illustrate numerically how it outperforms several other competing algorithms such as Differential Evolution even when considering problems with a large number of elements. [ABSTRACT FROM AUTHOR]

Cross-docking is a logistics strategy for direct distribution of products from a supplier or manufacturing plant to a customer or retail outlet with little or no handling and storage time. The classical cross-docking models are used to find the optimal inbound/outbound truck schedule that minimizes the total operational time. We propose a new multi-period cross-docking model with multiple products, due dates, variable truck capacities, and temporary warehouse. The problem is formulated as mixed-integer programming and an evolutionary computation approach based on a genetic algorithm (GA) is designed to solve it. The structure of the chromosomes, the operators, and the constraint handling strategy are specifically designed for multi-period problems. Several test instances have been generated to compare the performance of the proposed GA with that of a branch and bound solution procedure. Moreover, a comprehensive statistical analysis is conducted to illustrate the performance efficacy of the proposed GA relative to the branch and bound algorithm. This analysis reveals that the GA provides a substantial decrease in the computational burden when compared to the branch and bound algorithm. [ABSTRACT FROM AUTHOR]

We propose a new bi-objective multi-product combined cross-docking truck-scheduling model with direct drone shipping and multiple fleets.The proposed model considers two conflicting objective functions (scheduling cost and time) within a multi-objective mixed integer mathematical programming problem. Several constraint sets are also considered for both allocation and scheduling phenomena. An efficient multi-objective epsilon-constraint method is adapted to solve the proposed model. Several numerical examples and metrics are provided to demonstrate the applicability of the proposed model and exhibit the efficacy of the solution procedures and algorithms. The efficient frontiers of the numerical examples are estimated by generating non-dominated solutions. The effects that modifications in the costs associated with the direct shipping of products have on the corresponding Pareto frontiers are analyzed. Finally, sensitivity analysis is used to assess the robustness of the results of the model in the presence of uncertainty. [ABSTRACT FROM AUTHOR]

Project selection and resource allocation are critical issues in project-based organizations. These organizations are required to plan, evaluate, and control their projects in accordance with the organizational mission and objectives. In this study, we propose a three-stage hybrid method for selecting an optimal combination of projects. We obtain the maximum fitness between the final selection and the project initial rankings while considering various organizational objectives. The proposed model is comprised of three stages and each stage is composed of several steps and procedures. We use Data Envelopment Analysis (DEA) for the initial screening, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking the projects, and linear Integer Programming (IP) for selecting the most suitable project portfolio in a fuzzy environment according to organizational objectives. Finally, a case study is used to demonstrate the applicability of the proposed method and exhibit the efficacy of the algorithms and procedures. [ABSTRACT FROM AUTHOR]

Highlights: [•] Information acquisition is essential for sustaining a firm’s competitive advantage. [•] We analyze the strategic side of the information acquisition process. [•] Decision makers (DMs) are allowed to exhibit any type of risk attitude. [•] Managers can estimate our model using linguistic evaluations obtained from DMs. [•] Our model can be implemented within several multicriteria decision making methods. [Copyright &y& Elsevier]

Developing sustainable municipal waste management systems requires an in-depth analysis and synthesis of economic, environmental, and social sustainable development indicators. However, despite its profound impact on organizational performance, social sustainability has received little attention in previous studies compared to economic and environmental sustainability. Although a few studies have been conducted to analyze and measure the impact of social sustainability indicators, most of these endeavors fail to consider many indicators that must be evaluated under uncertain and incomplete information. This study proposes a new decision model that implements Interval-Valued Neutrosophic Sets (IVNS) within a multi-distance measure defined with respect to an ideal reference solution. IVNS allows decision-makers to reliably express their opinions using truth, indeterminacy, and falsity membership functions. A linguistic framework is developed to categorize indicators based on their performance and suggest potential solutions when detecting indicators with relatively weak performances. Social sustainability failures in the municipal waste management system of Istanbul are investigated to show the applicability and efficacy of the proposed approach. We identify salary satisfaction and health insurance as the most significant social indicators determining the success of the system, while freedom of association and citizen participation are categorized as the worst-performing ones. The stability of the proposed methodology is illustrated by performing a comparative analysis with Single-Valued Neutrosophic Sets and Interval-Valued Fuzzy Pythagorean Sets. [Display omitted] • Municipal waste management systems build on economic, environmental, and social pillars. • Social sustainability (SS) indicators often rely on uncertain and incomplete information. • We define a model based on interval-valued neutrosophic sets and multi-distance measures. • A linguistic framework is developed to categorize indicators based on their performance. • We analyze the main SS failures in the municipal waste management system of Istanbul. [ABSTRACT FROM AUTHOR]

Conventional data envelopment analysis (DEA) models treat the decision-making units (DMUs) as black-boxes: inputs enter the system and outputs exit the system, with no consideration for the intermediate steps characterizing the DMUs. As a result, intermediate measures are lost in the process of changing the inputs to outputs and it becomes difficult, if not impossible, to provide individual DMU managers with specific information on what part of a DMU is responsible for the overall inefficiency. This study defines a two-stage DEA model, where each DMU is composed of two sub-DMUs in series, the intermediate products by the sub-DMU in Stage 1 are partly consumed by the sub-DMU in Stage 2, and the initial inputs of the DMU can be freely allocated in both stages. Also, there are additional inputs directly consumed in Stage 2 while part of the outputs of Stage 1 are final outputs. We develop four new linear models to determine the upper and lower bounds of the efficiencies of the two sub-DMUs in a non-cooperative setting and a linear model to calculate the overall efficiency of DMU in a cooperative setting. That is, the overall efficiency of a DMU is modelled in a cooperative setting via upper and lower bounds obtained in the non-cooperative one. The proposed two-stage DEA method allows for important applications to several management areas. A case study in the banking industry is presented to demonstrate the applicability and exhibit the efficacy of the proposed models. [ABSTRACT FROM AUTHOR]

Supplier evaluation and selection constitutes a central issue in supply chain management (SCM). However, the data on which to base the corresponding choices in real life problems are often imprecise or vague, which has led to the introduction of fuzzy approaches. Predictive intelligent-based techniques, such as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS), have been recently applied in different research fields to model fuzzy multi-criteria decision processes where the understanding and learning of the relationships between the input and output data are the key to select suitable solutions. In this paper, a hybrid ANFIS-ANN model is proposed to assist managers in their supplier evaluation process. After aggregating the data set through the Analytical Hierarchy Process (AHP), the most influential criteria on the suppliers’ performance are determined by ANFIS. Then, Multi-Layer Perceptron (MLP) is used to predict and rank the suppliers’ performance based on the most effective criteria. A case study is presented to illustrate the main steps of the model and show its accuracy in prediction. A battery of parametric tests and sensitivity analyses has been implemented to evaluate the overall performance of several models based on different effective criteria combinations. [ABSTRACT FROM AUTHOR]