We illustrate the capacity of Artificial Intelligence (AI) and Machine Learning (ML) techniques to preserve consistent categorization abilities whenever the quality of the data decreases, displaying mistakes or mismatches across matrix entries, while standard statistical methods exhibit significant modifications in the value of the corresponding coefficients. We design algorithms of different complexity to generate a series of comparable profiles. These profiles are compared within environments that allow for an immediate identification of the generating algorithms and within increasingly complex settings involving almost identical profiles derived from different algorithms. AI and ML techniques outperform standard statistical methods when distinguishing the algorithms generating the profiles. Building on these results, we perform a retrospective analysis where AI and ML techniques are applied to two empirical scenarios defined by different data series of patients transplanted through the period 2006–2019. The first scenario contains the variables describing the evolution of patients inputted correctly. In the second, we modify the content of the vectors of characteristics defining the evolution of patients by exchanging the values of a subset of realizations from two categorical variables. AI and ML techniques are consistently accurate when categorizing patients correctly within both scenarios, a feature particularly relevant when the quality of the information sources composing the medical chain varies. This latter problem is exacerbated among hospitals located in developing countries, where the quality of the data gathered limits their identification and extrapolation capacities.

Abstract Designing and developing sustainable circular supply chain networks for electric vehicle (EV) lithium‐ion battery recycling and production requires complex environmental sustainability and economic viability assessment. EVs use a lot of data for battery management and delivering optimum performance, and the Internet of Things (IoT) plays a major role in managing this data. This study develops a bi‐objective mixed‐integer linear programming model for designing a sustainable circular supply chain to manage the manufacturing, remanufacturing, and distribution of EV lithium‐ion batteries under uncertainty using the IoT and big data. The proposed model simultaneously minimizes total costs and CO2 emissions and uses IoT to improve network performance and create a traceable and secure environment. A fuzzy multi‐objective method solves the bi‐objective optimization model under uncertainty, and a simulation algorithm examines the effectiveness of the proposed model through simulated problems.

The best-worst method (BWM) is a popular multi-criteria decision-making (MCDM) method known for the low number of pairwise comparisons and high consistency. General BWM (GBWM) is a new version of BWM that considers the interdependencies between interwind factors in MCDM problems. This study proposes a fuzzy stochastic GBWM for weighting intertwined factors using a scenario-based approach in complex intertwined or hierarchical networks under uncertainty. Fuzzy stochastic GBWM provides decision-makers with a wide range of weights, from fuzzy-stochastic weights to stochastic weights (defuzzified weights), fuzzy weights, and deterministic weights to use with different assumptions in the decision-making process. We demonstrate the efficacy and applicability of the proposed method with a well-known car-buying problem in the literature and a real-world problem in the transportation industry.

Supply chain collaboration plays an important role in profit maximization. Contract coordination is a successful strategy for collaboration and profit-sharing in supply chains. The study of contract coordination in supply chains under uncertainty and stochastic environmental conditions has attracted the attention of many researchers. We propose a single-period newsvendor model with one manufacturer and one retailer in the supply chains. The proposed model considers two variables, including order quantity and investment in corporate social responsibility, where the demand is stochastic and follows a normal standard distribution. The model considers quantity flexibility, advanced purchasing discounts (APDs), and buyback contracts under cooperative conditions. Numerical examples are used to exhibit the applicability of the model under various conditions, including the two-demand distribution and three coordination contracts. We demonstrate an ideal scenario where the APD contracting results in the most overall supply chain profit and increases for each supply chain partner.

The twenty-two papers in this special section focus on fuzzy decision systems for sustainable transport. Sustainable transport has gained widespread recognition by countries, local governments, cities, and transport authorities around the world to bring about positive change for the environment, ensure wider accessibility, and reduce carbon emissions. Many countries and cities have redesigned their transport systems to make them more sustainable. Road vehicles, such as cars, trucks, buses, and two-and three-wheeled vehicles, account for almost three-quarters of CO2 emissions in transport. Emissions from them continue to increase, and more international policy and cooperation focus is needed in these areas. Fuzzy decision systems are one of the most important advances in computational intelligence. The recent theoretical developments in the area of fuzzy decision systems provide novel perspectives for the key mechanisms of decision making and information processing that can handle uncertain, ambiguous, noisy, and missed input information in sustainable transport problems and decisions.

Multiple criteria decision-making (MCDM) methods do not account for the potentially strategic evaluations of experts. Once the ranking is delivered, decision makers (DMs) select the first alternative without questioning the credibility of the evaluations received from the experts. We formalize the selection problem of a DM who must choose from a set of alternatives according to both their characteristics and the credibility of the reports received. That is, we transform an MCDM setting into a game-theoretical scenario. We build our analysis on a recent extension of hesitant fuzzy numbers incorporated within the formal structure of technique for order of preference by similarity to ideal solution. We define the restrictions that must be imposed regarding the credibility of the evaluations and the capacity of experts to form coalitions and manipulate rankings based on their subjective preferences. This feature constitutes a considerable drawback in real-life scenarios, mainly when dealing with environmental and sustainable strategic problems. In this regard, sustainable transportation problems incorporate both technical variables and subjective assessments whose values can be strategically reported by experts. We extend a real-life study case accounting for the evaluations of several experts to demonstrate the importance of strategic incentives for the rankings obtained when implementing MCDM techniques. We numerically illustrate the interactions between the experts’ reporting strategies and the formal tools available for the DMs to counteract potential manipulations of the final ranking.

Abstract The quality of its players is one of the most significant features determining the failure or success of a sports team. The wide array of factors contributing to the performance of the players together with the inherent financial limitations of the clubs have transformed the selection of players into a complex problem. The current paper presents an integrated approach that combines multiple‐criteria decision‐making analysis and mathematical programming to support the decision maker through the building process of a soccer team. First, the fuzzy analytic network process is applied to evaluate the significance of the different performance criteria for each position in the field. The score attained by the different players in each potential position is computed using PROMETHEE II. A biobjective integer programming model has been designed to evaluate the transfer status of the players. Finally, data envelopment analysis is used to identify the most efficient Pareto solution determining the status of each player. In order to demonstrate the applicability of the proposed approach, the position in the field and transfer status of 60 players being considered by a real soccer team have been determined.

In most global optimization problems, finding a global optimum point in the whole multi-dimensional search space implies a high computational burden. We present a new approach called subdividing labeling genetic algorithm (SLGA) for continuous nonlinear optimization problems. SLGA applies mutation and crossover operators on a subdivided search space where an integer label is defined on a polytope built on a n-dimensional space. After calculating the fitness of each point composing the polytope, SLGA implements a mutation operator to generate offspring and computes an integer label for the population of the polytope. Then, after completely labeling the polytope, a crossover operator is implemented so as to approach the optimum point by reducing the search space. In this regard, new population is generated by subdividing the search space and further implementing the mutation operator. SLGA has been used to optimize the De Jong functions, as well as nonlinear constrained and unconstrained problems with discrete, continuous and mixed variables. It has also been compared with other well-known algorithms. Experimental results show that the SLGA method has good performance and reduces the number of generations within the solution space, which enhances its convergence capability.

The increase in the amount and variety of evaluations provided by the users of different websites regarding the products displayed is becoming an increasingly familiar scenario. That is, decision makers (DMs) constantly receive linguistic evaluations (LEs) from unknown evaluators when considering different choice alternatives. The imprecision of the LEs and the fact that the evaluators may have biased interests when describing a product must be considered by the DMs when computing their expected utilities. We define a Bayesian-updated probability (BUP) function that accounts for the fuzziness inherent in the LEs and the reputation of the evaluator to represent the beliefs of DMs. The proposed BUP process allows the DMs to subjectively adjust the probability mass that is shifted across evaluation intervals when updating their beliefs and computing their corresponding expected utilities. We illustrate the behavior of the BUP function numerically and describe potential decision support applications.

Image segmentation is one of the most important and challenging problems in image processing. The main purpose of image segmentation is to partition an image into a set of disjoint regions with uniform attributes. In this study, we propose an improved method for edge detection and image segmentation using fuzzy cellular automata. In the first stage, we introduce a new edge detection method based on fuzzy cellular automata, called the texture histogram, and empirically demonstrate the efficiency of the proposed method and its robustness in denoising images. In the second stage, we propose an edge detection algorithm by considering the mean values of the edges matrix. In this algorithm, we use four fuzzy rules instead of 32 fuzzy rules reported earlier in the literature. In the third and final stage, we use the local edge in the edge detection stage to more accurately accomplish image segmentation. We demonstrate that the proposed method produces better output images in comparison with the separate segmentation and edge detection methods studied in the literature. In addition, we show that the method proposed in this study is more flexible and efficient when noise is added to an image.

Earthquakes occurring in urban areas constitute an important concern for emergency management and rescue services. Emergency service location problems may be formulated in discrete space or by restricting the potential location(s) to a specified finite set of points in continuous space. We propose a Multicriteria Spatial Decision Support System to identify shelters and emergency service locations in urban evacuation planning. The proposed system has emerged as an integration of the geographical information systems (GIS) and the multicriteria Decision-Making method of Preference Ranking Organization Method for Enrichment Evaluation IV (PROMETHEE IV). This system incorporates multiple and often conflicting criteria and decision-makers’ preferences into a spatial decision model. We consider three standard structural attributes (i.e., durability density, population density, and oldness density) in the form of spatial maps to determine the zones most vulnerable to an earthquake. The information on these spatial maps is then entered into the ArcGIS software to define the relevant scores for each point with regards to the aforementioned attributes. These scores will be used to compute the preference functions in PROMETHEE IV, whose net flow outranking for each alternative will be inputted in ArcGIS to determine the zones that are most vulnerable to an earthquake. The final scores obtained are integrated into a mathematical programming model designed to find the most suitable locations for the construction of emergency service stations. We demonstrate the applicability of the proposed method and the efficacy of the procedures and algorithms in an earthquake emergency service station planning case study in the city of Tehran.

The current paper studies an endogenous growth model driven by the technological development level of a country, which conditions both its factor productivity and the financial investment decisions of agents. Heterogeneity in the level of technological development among countries may not only lead to temporal divergences in income and productivity levels but also to divergent growth paths and poverty traps for identical available technologies. The authors illustrate the structural instability resulting from differences in the technological development level of countries and the subsequent financial constraints arising from such differences. Consequently, a strong national system of innovation should prove vital to ameliorate the negative real effects that follow from a severe financial shock. The obvious and imminent implications regarding the expected evolution of the European Monetary Union are derived both formally and numerically.

The purpose of this study is primarily theoretical—to propose and detail a model for system evolution and show its derivation from the fields of enterprise architecture (EA), cybernetics, and systems theory. Cybernetic thinking is used to develop the coevolution path model (CePM) to explain how enterprises coevolve with their environments. The model reinterprets Ashby's law of requisite variety, Stafford Beer's viable system model, and Conant and Ashby's theorem of the “good regulator” to exemplify how various complexity management theories could be synthesized into a cybernetic theory of EA—informing management of mechanisms to maintain harmony between the evolution of the enterprise as a complex system and the evolution of its complex environment.

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency of peer operating units that employ multiple inputs to produce multiple outputs. Several DEA methods have been proposed for clustering operating units. However, to the best of our knowledge, the existing methods in the literature do not simultaneously consider the priority between the clusters (classes) and the priority between the operating units in each cluster. Moreover, while crisp input and output data are indispensable in traditional DEA, real-world production processes may involve imprecise or ambiguous input and output data. Fuzzy set theory has been widely used to formalize and represent the impreciseness and ambiguity inherent in human decision-making. In this paper, we propose a new fuzzy DEA method for clustering operating units in a fuzzy environment by considering the priority between the clusters and the priority between the operating units in each cluster simultaneously. A numerical example and a case study for the Jet Ski purchasing decision by the Florida Border Patrol are presented to illustrate the efficacy and the applicability of the proposed method.