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Wang, and J.-T. Teng in their paper develop and compare the seller's profit per unit time under each of three payment methods - advance, cash, and credit. In their paper, S. Sarkar, S. Tiwari, and B. C. Giri present a continuous-review vendor-buyer supply chain (SC) model and uncovers the best policy that minimizes the system's total expected cost. The authors investigate the contracts that the manufacturer offers to the retailers and finds the optimal pricing and ordering decisions made by retailers and the best contract that includes maximum profit for the supply chain. The analysis of large-scale data generated by humans, online activity in different web and social platforms is a challenging problem faced by researchers, practitioners, and academicians. [Extracted from the article]

In today's competitive environment, industries are required to increase reliability and safety level of their equipment with reasonable cost. Consequently, it is essential to select the appropriate maintenance strategy. In the paper industry, pumping of cooked pulp requires abrasion- and sometimes corrosion-resistant pumps that are able to handle up to 70 % solids content. But the maintenance engineer and supervisor often face failures of the pump. Hence, this article describes the application of multi criteria decision-making (MCDM) technique for the selection of optimum maintenance strategy for pumps used in the paper industry. The proposed hybrid MCDM model comprises fuzzy analytical hierarchy process (FAHP), grey relational analysis (GRA), and technique for order preference by similarity to ideal solution (TOPSIS) technique. The FAHP is used to compute the criteria weights whereas GRA-TOPSIS is used for determining the ranking of alternatives. This study discusses four maintenance strategies: corrective maintenance, predictive maintenance, time-based preventive maintenance, and condition-based maintenance. Four main criteria-safety, cost, added value, and feasibility-have been used to evaluate the optimum maintenance strategy. [ABSTRACT FROM AUTHOR]

The 2017 International Conference on Multiple Objective Programming and Goal Programming (MOPGP 2017) has been organized to disseminate recent theoretical and methodological developments, significant technical applications, case studies and survey results in the related area. Multiple Objective Programming (MOP), also known in literature as multi-objective optimization, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization, is a subarea of Multiple Criteria Decision Making (MCDM) or Multiple Criteria Decision Analysis (MCDA) and it is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. If we suppose that HT ht is ordered by the classical Pareto cone HT ht , then HT ht if HT ht . [Extracted from the article]

Purchasing occupies a strategic role in supply chain management for a firm and is the driver of competitive advantage. Owing to the high purchase cost to revenue ratio, decisions such as evaluation, selection, and performance management of suppliers are of the matter of immense interest to firms. Multi-criteria decision making tools allow the purchasing managers to evaluate the suppliers holistically. One such tool, data envelopment analysis (DEA) has been used extensively for supplier evaluation and selection. This paper presents a comprehensive review of 161 articles published since 2000, on the application of DEA in supplier selection. These articles are located from the Scopus database. With little existing literature on a full-fledged review, this work envisages to be first of its kind, by aiding DEA practitioners in purchasing function. The analysis of the study indicates the emergence of the theme of green supply chain and sustainability in recent years as well as the adoption of hybrid approaches to solving the problem of supplier selection using DEA. The paper presents various classifications of DEA methods based on input criteria, sectors of application, and industry-wide case studies, which can be used as a quick reckoner by an academician or a purchasing manager. [ABSTRACT FROM AUTHOR]

The dynamic multiple-criteria decision-making (MCDM) approach has drawn immense attention in the decision analysis domain over recent years. An essential problem of this dynamic process is how to retrieve the utmost from historical data. Although most previous dynamic MCDM studies process historical data only with external representatives, namely scores given to alternatives on each criterion, we argue that internal patterns of historical data are also essential to achieve a more comprehensive evaluation of alternatives. Nevertheless, a data-driven methodology aiming to capture internal patterns of historical data and consequently provide decision-makers with meaningful insights is still lacking. In this paper, we propose a framework that serves as integration within which both external representations and internal patterns are used to make a more comprehensive evaluation. Then we apply our framework to student evaluations and country-level risk assessments in the contexts of additive and fuzzy measures, respectively. Both cases show that either using external representatives or internal patterns alone leads to entirely different and even biased results. On the contrary, integrating external representations and internal patterns can offer more flexibility and higher interpretation power. [ABSTRACT FROM AUTHOR]

Since at the two conferences many presentations addressed a variety of different multiobjective optimisation problems, we decided to focus this special issue distinctively on recent developments in multiobjective optimisation falling within the a posteriori paradigm of multiple criteria decision making (MCDM). We observe that multiobjective optimisation under uncertainty and multiobjective optimisation in practical applications are the dominating research themes throughout this volume because a majority of studies address either one or both of them simultaneously. The type of optimisation problems addressed ranges from scheduling problems with two objectives to mixed integer linear optimisation problems and nonlinear optimization problems, both with only continuous and with continuous as well as binary variables. [Extracted from the article]

Stochastic multicriteria acceptability analysis (SMAA) is a family of multiple criteria decision making (MCDM) methods dealing with incomplete, imprecise, and uncertain information on the evaluations and preference model parameters. As it provides a general framework that has extensions to deal with various specificities in MCDM problems, the development of SMAA methods and their applications in real-life decision-making problems have been increased over the recent years. This paper provides an up-to-date literature review of different SMAA methods and their applications in various areas. First, we selected, from different on-line data base, 118 articles published between 1998 and 2017. We categorized the selected papers into theoretical and applied. While the theoretical papers were analyzed based on the method's aggregation procedure, type of problem, type of method's outputs and inputs, the applied papers were separated and analyzed by application areas. Then, we provide some descriptive statistics, analyzing the papers regarding to publication year and journals of publication. Finally, we provide some guidelines to assist decision-makers in the choice of a SMAA method on a specific decision-making context and some future research directions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to solve an agricultural technology packages selection problem by considering multiple dimensions which influence a maize producer's preferences. The decision-making process is aided by a new multicriteria method for eliciting scale constants in additive models: flexible and interactive tradeoff (FITradeoff). This method works with partial information, obtained from the decision maker (DM), and thus reduces the time that the DM has to spend on the process for eliciting his/her preferences as he/she may avoid answering difficult questions. The decision-making process makes use of a decision support system (DSS), in which the DM interactively gives preference statements in a structured manner. The DSS gives flexibility to the DM, in such way that he/she gives as much information as he/she is willing to. Graphical visualization is provided at each step in order to help the DM's analyses. Throughout the description of an application, some insights are provided including a discussion of the advantages and features of the FITradeoff method. [ABSTRACT FROM AUTHOR]

Analytical Hierarchy Process (AHP) has evolved since its inception and has contributed mainly to multi-criteria decision-making (MCDM) to solve complex problems. It is a widespread technique that uses group decision-making approach to solve problems in business operations, mathematics, and numerous other scientific fields. While its applications are numerous, a handful of researchers have studied the growth trajectory of AHP. Whilst these seminal works have discussed the evolution of AHP in different contexts and using contemporary techniques, we complement them by presenting and reflecting on the unchartered and pressing need of charting the managerial and theoretical evolution of AHP, over the last decade. We do this through a systematic and thematic review of the extant literature. This study uses the Scopus database to extract papers published between 2012 and 2021 in decision science with an overlap with operations research and information systems. The study follows Tranfield et al. (2003)'s guidelines to understand the past trends of AHP along three key dimensions and offers future directions. The study also showcases key trends using historiography and uses data visualization techniques to understand the use of AHP with other MCDM techniques. [ABSTRACT FROM AUTHOR]

Megacities around the world are highly concerned about the impacts of urban transportation. The rapid urbanization and motorization in megacities have a direct impact on sustainable development. Hence, it is very frequently to find the multiple actors from both private and public sectors involved in the design and operation of urban passenger transportation systems to make decisions that seek at optimizing their own particular objectives. However, urban passenger transport system is a very complex task involving multiple criteria related to economic, environmental and socio-political issues. Multi-criteria decision-making (MCDM) techniques actually aid the decision making process by assessing such problems. This paper presents a review of research papers published between 1982 and 2014 (up to May) concerning MCDM for the design and operation of urban passenger transport systems. From the analysis over more than 30 years, the current paper highlights the importance of taking into account the multiplicity of actors and their own decision criteria, as well as the resolution technique. Finally, some suggestions for future research are discussed. [ABSTRACT FROM AUTHOR]

In most project portfolio selection (PPS) situations, the presence of multiple attributes and decision-maker preference is inevitable. As Multi-criteria Decision Analysis (MCDA) methods provide a framework well-suited to deal with these challenges in PPS problems, the use of MCDA methods in real-life PPS problems has increased in recent years. This paper provides a comprehensive literature review of the use of different MCDA methods and their individual or combined utilization with other modeling techniques to support PPS problems. First, we summarize how MCDA methods are used in different modeling approaches. Second, we examine the mathematical models that are generally used to combine MCDA with mathematical programming techniques to solve PPS problems with resource constraints. Third, we present the drawbacks of combined utilization and discuss recent advances. Finally, we visualize the summary of the reviewed papers as a decision tree to assist researchers and practitioners in the use of MCDA methods in a specific PPS context and propose some future research directions. [ABSTRACT FROM AUTHOR]

This paper considers the problem of finding suitable sites for wind farms in a region of Catalonia (Spain). The evaluation criteria are structured into a hierarchy that identifies several intermediate sub-goals dealing with different points of view. Therefore, the recent ELECTRE-III-H hierarchical multi-criteria analysis method is proposed as a good solution to help decision-makers. This method establishes an order among the set of possible sites for the wind farms for each sub-goal. ELECTRE-III-H aggregates these orders into an overall order using different parameters. The procedure is based on the construction and exploitation of a pairwise outranking relation, following the principles of concordance (i.e. majority rule) and discordance (i.e. respect for the minority opinions). This paper makes two main contributions. First, it contributes to the ELECTRE-III-H method by studying its mathematical properties for the construction of outranking relations. Second, the case study is solved and its results show that we can effectively represent and manage the overall influence of the various criteria on the global result at different levels of the hierarchy. The paper compares different scenarios with strict, normal, and optimistic preference, indifference and veto thresholds. Results show that the best site differs for technical, economic, environmental, and social intermediate criteria. Therefore, the best overall solution changes depending on the preference and veto thresholds fixed at the intermediate level of the hierarchy. [ABSTRACT FROM AUTHOR]

Understanding customers' decision and behavior is the crux of marketing. Despite the broad applications of weighted sum approaches, like conjoint analysis, sophisticated methodological approaches are under-researched in this field. Conversely, multi-criteria decision making's (MCDM) objective is to focus on and forecast these decisions. Based on the Choquet integral, this paper presents an effective and precise calculation method to understand real customers' decision making and to overcome the weighted sum method's limitations. We compare the weighted sum approach with stable and instable Choquet integral methods in three experimental studies. Our results indicate that the weighted sum approach is valuable in pure order-related applications. In quantitative comparisons, both the stable and instable Choquet integral approaches match the decision makers' preferences more closely than the weighted sum approach. The paper demonstrates that well-developed traditional approaches have their merits and can still be applied in the right context. [ABSTRACT FROM AUTHOR]

In recent years, decision makers, policy analysts, and other actors, have become increasingly aware of sustainability, and begun to combine economic, social and environmental criteria in their efforts to maintain competitiveness, long-term growth and development. In multiple stakeholder settings, the presence of diverse objectives and conflicting criteria often leads to a complex multi-criteria decision problem. The multi-criteria decision analysis (MCDA) offers an integrated framework to model and study sustainability criteria and related inter-criteria relationships. In this paper, we review some of the most significant literature on environmental sustainability, and categorise it to show how and why MCDA models are widely used and becoming increasingly popular. Our systematic analysis suggests that, there has been significant growth in environmental applications of MCDA in diverse areas, ranging from energy management and policy to land use, recycling management and sustainable tourism. Among the various MCDA methods and techniques, analytical hierarchy process, TOPSIS, and goal programming are the most frequently used approaches. Many authors use a combination of different MCDA techniques to balance various factors important to achieve sustainability related goals. We expect sustainability related criteria to be an essential consideration in most future multi-criteria models. [ABSTRACT FROM AUTHOR]

Global warming, climate change, and social problems are the worst human-induced sustainability issues that economies across the globe have witnessed. Water pollution, greenhouse effect, poor working conditions, child labour and lack of coordination among channel partners have caused the considerable interruptions in the supply chain network. The purpose of the paper is to identify critical factors affecting behavioural and sustainable supply chain coordination and evaluate strategies for risk reduction in the supply chain coordination in the context of digitization. This study purposes a novel supply chain coordination framework which consists of four themes such as system, actor, objective and action on which the success or the failure of supply chain can be contingent. Our study integrates multi-criteria decision approach using Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to investigate factors that affected the behavioural and sustainable supply chain coordination in the context of digitization. The Fuzzy-AHP method qualified to hierarchically rank the factors based on the relative fuzzy weightage while Fuzzy-DEMATEL established the inter-relationships among the factors and classified them into cause and effect groups. The findings of our study identified the Environmental performance and decarbonization as the most significant factor and the speed to market as the least important factor in developing behavioural and sustainable supply chain coordination in the context of digitization. Our analysis from Fuzzy AHP-DEMATEL approach reveal that the social preferences (power balance, reciprocity, fairness) is a significant causal factor which can effectively abolish the issues plaguing behavioural and sustainable supply chain coordination in the context of digitization. The results from our study aim to facilitate decision makers in cultivating a sustainable supply chain framework that can boost trust among the channel partners environmental performance, social performance and channel efficiency of the supply chain, thereby ensuring sustainability and socio welfare of all the supply chain. [ABSTRACT FROM AUTHOR]

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) are among the most employed approaches for aggregating performances in Multi-Criteria Decision-Making (MCDM). TOPSIS and SAW are two MCDM methods based on the value function approach and are often used in combination with other MCDM methods in order to produce rankings of alternatives. In this paper, first, we analyse some common features of these two MCDM methods with a specific reference to the additive properties of the value function and to the sensitivity of the value function to trade-off weights. Based on such methodological insights, an experimental comparison of the results provided by these two aggregation methods across a computational test is performed. Specifically, similarities in rankings of alternatives produced by TOPSIS and SAW are evaluated under three different Minkowski distances (namely, the Euclidean, Manhattan and Tchebichev ones). Similarities are measured trough a set of statistical indices. Results show that TOPSIS, when used in combination with a Manhattan distance, produces rankings which are extremely similar to the ones resulting from SAW. Similarities are also Experimental results confirm that rankings produced by TOPSIS methods are closer to SAW ones when similar formal properties are satisfied. [ABSTRACT FROM AUTHOR]

The International Civil Aviation Organization has recently established new standards for the aeronautical telecommunication network using Internet protocol suite (ATN/IPS). As ATN services may operate on shared data communication links, optimizing the use of the shared resources has become fundamental to ensure that applications operate adequately. In this context, load balancing for ATN/IPS can be considered in an attempt to optimize the use of links based on their capacities. The literature is scarce of network management studies in which load balancing is taken into account. In this paper, we propose an integrated model for managing ATN/IPS links. In addition to objective parameters, we define subjective parameters of the model through the voluntary participation of air traffic controllers. The well-known multi-criteria decision making method Analytic Hierarchy Process was employed to define the subjective parameters of the integrated model, which met the requirements of communication services and applications of the aeronautical network. The network selection problem was then modeled in a multi-objective optimization problem after combining the criteria by using Simple Additive Weighting method. Computational experiments with real data from Brazilian ATN (ATN-Br) were performed. The ε -constraint method was applied to solve the multi-objective problem by varying the balance levels of the network. The results of the experiments show that the proposed integrated model maintains links with a usage rate closer to the trade-off point. In addition, the use of objective and subjective criteria allows the model to effectively prioritize critical communications in link degraded scenarios, which does not happen in the baseline model, in which priorities are pre-defined. [ABSTRACT FROM AUTHOR]

Decision support for Supplier Selection and Order Allocation (SSOA) is an important application area of multiple criteria decision making (MCDM) problems. In Amid et al. (Int J Prod Econ 131(1):139–145, 2011) proposed and developed a weighted maximin model to ensure the weight-consistent solution for SSOA in an MCDM problem under an uncertain environment. Essentially, this model is based on a weight-consistent constraint and a maximin aggregation operator. This paper reanalyzes the weighted maximin model in terms of the weight-consistent constraint, and then proposes a general weight-consistent model for SSOA in MCDM problems under uncertainty. In this paper, two existing models are reviewed and compared with the proposed model. Three datasets with different ranges of fuzzy demand and full factorial patterns of criteria weights are used to test the performances of the related models. The results showed that the proposed model always generates a weight-consistent Pareto-optimal solution in all cases, while the other existing models do not. [ABSTRACT FROM AUTHOR]

The Analytic Hierarchy Process (hereafter AHP) is a popular multi-criteria decision-making technique. The extant AHP literature usually depicts the geometric mean or the arithmetic mean as a measure of aggregation to process group decisions. However, both these measures are subject to the influence of extreme opinions, and aggregations based on them may not accurately portray the true group preference. In this paper, we propose the Common Priority Vector Procedure, which accentuates the majority group preference and diminishes the influence of extreme individual opinions. The method has been further extended to deal with multi-actor, multi-criteria and multi-group decisions. The development of Common Priority Vector Procedure, presented here, has been motivated by a real case study presented towards the end of the paper. [ABSTRACT FROM AUTHOR]

Interpretive ranking process (IRP) is a multi-criteria decision making method based on paired comparison in an interpretive manner. Due to paired comparisons, the number of interpretations to be made for n ranking variables are n (n - 1) / 2 to establish dominance with respect to each reference variable or criterion. IRP is a knowledge intensive method and thus a large number of comparisons poses a limitation on the number of rankling as well as reference variables to be considered in the design of the decision problem. This paper is intended to make the process of comparison more efficient so that this limitation on number of variables can be relaxed to handle comparatively large size problems as well. The number of interpretive comparisons can be drastically reduced by considering both implicit and transitive dominance relationships. It provides a critical review of IRP steps and suggests improvements to make it more efficient. It then illustrates the modified IRP method on a couple of already published examples (including an example on post-disaster management) and summarizes the reduction in interpretive comparisons that indirectly gives a measure of increase in its efficiency. [ABSTRACT FROM AUTHOR]

The weight and reliability of an individual assessment are two important concepts considered in the evidential reasoning (ER) approach. Through analyzing the existing studies on the combination of individual assessments with both their weights and reliabilities considered in the ER context, their deficiencies are identified in accordance with two principles. One principle is developed in the situation where a specific individual assessment is fully unreliable and the other is developed in the situation where all individual assessments are fully reliable. To address the deficiencies, this paper proposes a new method. In the method, a combination process that takes into account both the weights and reliabilities of individual assessments simultaneously is developed to generate the overall assessment. It is theoretically proven that the combination process satisfies the two principles. Three ways are designed to help a decision maker to flexibly provide individual assessments and determine their reliabilities. A strategic project evaluation problem for an enterprise located in Changzhou, Jiangsu, China is analyzed using the proposed method as a case study to demonstrate its validity and applicability. These are highlighted by its comparison with two existing methods. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is known to be a proactive reliability analysis model broadly utilized to recognize and evaluate potential failure modes in various industries. The normal risk priority number (RPN) method, however, has suffers from a lot of criticisms, such as requirement of precise risk estimation, lack of scientific basis in computing RPN, and neglecting the weights of risk factors. Therefore, this paper devises a new FMEA model to evaluate and prioritize the risk of failure modes by integrating probabilistic linguistic term sets and TODIM (an acronym in Portuguese for interactive multi-criteria decision making) method. The probabilistic linguistic term sets are utilized to handle the intrinsic ambiguity existed in the risk assessments of FMEA team members, whilst an extended TODIM method is employed for determining the priority ranking of the individuated failure modes. Further, based on the technique for order of preference by similarity to ideal solution (TOPSIS), an objective weighting method is presented to derive the relative weights of risk factors. Finally, two illustrative examples are implemented and comparisons with other existing methods are performed to demonstrate the rationality and superiority of our proposed FMEA model. [ABSTRACT FROM AUTHOR]

This paper explores the study of multi-choice multi-objective transportation problem (MCMTP) under the light of conic scalarizing function. MCMTP is a multi-objective transportation problem (MOTP) where the parameters such as cost, demand and supply are treated as multi-choice parameters. A general transformation procedure using binary variables is illustrated to reduce MCMTP into MOTP. Most of the MOTPs are solved by goal programming (GP) approach, but the solution of MOTP may not be satisfied all times by the decision maker when the objective functions of the proposed problem contains interval-valued aspiration levels. To overcome this difficulty, here we propose the approaches of revised multi-choice goal programming (RMCGP) and conic scalarizing function into the MOTP, and then we compare among the solutions. Two numerical examples are presented to show the feasibility and usefulness of our paper. The paper ends with a conclusion and an outlook on future studies. [ABSTRACT FROM AUTHOR]

This paper proposes a model for obnoxious waste location-routing problem (LRP) considering various types of wastes and several treatment technologies. The investigated distribution network includes three echelons of generation nodes, treatment and disposal facilities. A multi-objective LRP model is developed with three objective functions minimizing the treatment and disposal facility undesirability, different costs related to the problem, and eventually the risk associated with transportation of untreated materials. An effective memetic algorithm is developed in which a tabu search algorithm performs the local search. Comparison of exact and meta-heuristic methods run times confirms that the proposed method is effective. Eventually, the developed algorithm is tested on a real-life case study. [ABSTRACT FROM AUTHOR]

Supplier selection has long been a strategic cornerstone for the advancement of companies from the mining sector due to its impact on many sectors as inventory management, production planning, maintenance scheduling, financial resources and environment. In this regard, expert decision-makers should deem a number of performance conflicting criteria and supplier alternatives. Moreover, choosing a suitable Multi-Criteria Decision Making (MCDM) approach has become a key strategic consideration for supplier selection. Hence, this paper presents a combination of powerful MCDM methods to select the most suitable supplier of forklift filters. The Fuzzy Analytic Hierarchy Process (FAHP) is first used to calculate the initial weights of criteria and sub-criteria under uncertainty, followed by the implementation of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL) to assess the interrelations and feedback between them. The fuzzy theory is used in both AHP and DEMATEL methods to represent the human vagueness when making judgments. Then, FAHP and FDEMATEL are combined to obtain the final contributions of both criteria and sub-criteria on the basis of interrelations and uncertainty. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank the supplier alternatives. To validate the effectiveness of the proposed framework, a case study is presented. The results evidence that quality criterion is the most crucial aspect when selecting suppliers of forklift filters. [ABSTRACT FROM AUTHOR]

Systemic decision making is a new approach for dealing with complex multiactor decision making problems in which the actors' individual preferences on a fixed set of alternatives are incorporated in a holistic view in accordance with the 'principle of tolerance'. The new approach integrates all the preferences, even if they are encapsulated in different individual theoretical models or approaches; the only requirement is that they must be expressed as some kind of probability distribution. In this paper, assuming the analytic hierarchy process (AHP) is the multicriteria technique employed to rank alternatives, the authors present a new methodology based on a Bayesian analysis for dealing with AHP systemic decision making in a local context (a single criterion). The approach integrates the individual visions of reality into a collective one by means of a tolerance distribution, which is defined as the weighted geometric mean of the individual preferences expressed as probability distributions. A mathematical justification of this distribution, a study of its statistical properties and a Monte Carlo method for drawing samples are also provided. The paper further presents a number of decisional tools for the evaluation of the acceptance of the tolerance distribution, the construction of tolerance paths that increase representativeness and the extraction of the relevant knowledge of the subjacent multiactor decisional process from a cognitive perspective. Finally, the proposed methodology is applied to the AHP-multiplicative model with lognormal errors and a case study related to a real-life experience in local participatory budgets for the Zaragoza City Council (Spain). [ABSTRACT FROM AUTHOR]

This paper puts forward a new hybrid Grey Additive-Veto Model (GAVM) for selecting suppliers, which optimizes the choice by the decision-maker's (DM) preference. This study uses a Multi-Criteria Decision Making (MCDM) approach. The model proposed incorporates the uncertainty of real-world decision problems, through the Grey Numbers. Given the DM's compensatory rationality, the method reflects tradeoffs amongst different criteria. However, a compensatory model may not represent the DM faithfully and realistically. For this is proposed a performance veto condition. Additionally, the DM may consider that suppliers with a high uncertainty are not desirable or even that they are not acceptable. For this is also proposed width performance veto condition. Given practical implications, the need for an enriched analysis of the ranking is perceived because during the ranking process, there are some situations in which the DM is undecided (mistrust, hesitation) about the statement that a given supplier is better ranked than another supplier. This hesitation is mainly caused by uncertainty arising from the grey numbers, and from attitudes, functions and characteristics of vetoes that the DM has defined. As findings, this paper shows that GAVM is suited to dealing with multicriteria decisions with uncertain information, veto conditions and its potential applicability is illustrated by a numerical supplier selection example. In addition, the numerical example shows that the enriched analysis of the ranking proposed can be conducted to direct the DM's efforts to further enriching the insights for more assertive and coherent decision-making. [ABSTRACT FROM AUTHOR]

In this paper, we consider a noncooperative finite multicriteria two-person game G. We study the problem of refinement of Pareto Nash equilibria of G and we propose an approach based on modeling the preferences of the players by two binary relations. This approach follows three main steps: first, we associate to G another game G¯ defined by the two sets of strategies and two binary relations over the set of the strategy profiles, where each binary relation expresses the preferences of the corresponding player. Second, we define a Noncooperative Equilibrium NCE for the game G¯ and we prove that every NCE of G¯ is a Pareto Nash equilibrium of G. Third, we propose a procedure for finding the set of NCE of G¯ independently of how the binary relations are constructed. Moreover, we give three ways to model the preferences of the players by using scalarization and the outranking methods ELECTRE I and PROMETHEE II. All the steps of the proposed approach are completely illustrated through an accompanying example. [ABSTRACT FROM AUTHOR]

This paper analyzes the short-term market efficiency of the mutual fund industry around the world. Using a unique database of worldwide domestic equity funds, it employs a parametric (regression model) and non-parametric (data envelopment analysis (DEA) model) approaches to establish a relation between cost (expense ratio, turnover, loads, and risk) and benefit (return) of mutual funds. The empirical results of the parametric approach show a statistically significant negative relationship between expenses and risk-adjusted performance across countries. When we reexamine this relationship using a non-parametric approach, we show, in contrast to our previous result, a positive relationship between expenses and risk-adjusted performance. Thus, using the DEA methodology, we find strong evidence that equity mutual funds around the world are approximately mean-variance efficient. [ABSTRACT FROM AUTHOR]

Over the years, the AHP has been used either as part of a multi-method approach (i.e., the AHP is preceded by another MCDM method) or as an integrated method (i.e., the AHP is combined with another method to constitute a new hybrid methodology). ANP development Still, while the AHP has been improved and its use has become more sophisticated over the years, a major development has been the creation of the Analytic Network Process (ANP) which is a generalization of the AHP (Saaty, [30], [31]). Three Developments of the Analytic Hierarchy Process: Calibrated Fuzzy AHP, AHP, AHPSort and GAHPO. We are proud to publish this ANOR special issue on the topic of the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). [Extracted from the article]

This paper presents a method of decision making with returns in the form of discrete random variables. The proposed method is based on two approaches: stochastic orders and compromise programming used in multi-objective programming. Stochastic orders are represented by stochastic dominance and inverse stochastic dominance. Compromise programming uses the augmented Tchebycheff norm. This norm, in special cases, takes form of the Kantorovich and Kolmogorov probability metrics. Moreover, in the paper we show applications of the presented methodology in the following problems: projects selections, decision tree and choosing a lottery. [ABSTRACT FROM AUTHOR]

In this paper a multi-criteria decision aiding model is developed through the use of the Choquet integral. The proposed model is an extension of the TODIM method, which is based on nonlinear Cumulative Prospect Theory. The paper starts by reviewing the first steps of behavioral decision theory. A presentation of the TODIM method follows. The basic concepts of the Choquet integral as related to multi-criteria decision aiding are reviewed. It is also shown how the measures of dominance of the TODIM method can be rewritten through the application of the Choquet integral. From the ordering of decision criteria the fuzzy measures of criteria interactions are computed, which leads to the ranking of alternatives. A case study on the forecasting of property values for rent in a Brazilian city illustrates the proposed model. Results obtained from the use of the Choquet integral are then compared against a previously made usage of the TODIM method. It is concluded that significant advantages exist derived from the use of the Choquet integral. The paper closes with recommendations for future research. [ABSTRACT FROM AUTHOR]

This paper deals with preemptive priority based multi-objective network design problems in which construction times together with travel costs are taken into account. These cost and time objective functions are ordered lexicographically with respect to manager's strategies in order to decrease total cost and total construction time of the network. To solve this preemptive problem, instead of the standard sequential approach, a modified Benders decomposition algorithm is developed. It is proved that this algorithm decreases the (expected) number of computations and so this algorithm is efficient for large-scale network design problems. [ABSTRACT FROM AUTHOR]

The purpose of this paper is, to propose a methodology for the comparison and ranking of Entrepreneurial Ecosystems (EEs) in order to offer the decision maker a straightforward way of assessing the competitiveness of national economies. EEs are a coordinated set of individual and institutional agents, on the national level, aimed to pursue economic development through the discovery and exploitation of entrepreneurial opportunities. Among the various approaches found in EE research, the Global Entrepreneurship Monitor consortium evaluates the national economic development of more than 100 countries, concerning both individual and contextual factors. Entrepreneurship Framework Conditions of national EEs are rated on an annual basis by groups of domain experts on a number of predefined criteria. These ratings describe the various facets of the EEs, exposing strengths and weaknesses of the contextual environment, offering a snapshot of the competitiveness conditions of the national economy. However, comparative studies examining the relative performance of each EE, are needed in order to communicate useful information and help stakeholders make insightful decisions and design policy. The ranking of EEs as different alternatives based on multiple criteria is a Multi-Criteria Decision Making (MCDM) problem. The relative importance of each criterion is defined either by experts or by statistical methods. The selection of criteria weights is usually a cumbersome procedure. The utilization of non-weighted or equal weight methods, eliminates the need for definition of weights, it has less computational overhead and can produce distinct and objective rankings of the alternatives. The Greek EE is taken as a working example, and it is compared with a number of developed and developing countries, as alternatives. The consistence of the ranking results produced by the method proposed is compared with the evaluations of other EE metrics and the results of other well accepted MCDM methods. The application of the method produced a distinct ranking for each alternative EE. The Greek EE ranked below average among the countries of the comparison group, implying that it is less conducive for entrepreneurship development, although it is categorized among the most developed Innovation Driven economies. The theoretical and practical implications of the results are thoroughly discussed offering useful conclusions. [ABSTRACT FROM AUTHOR]

This paper introduces the element of beneficiaries' choice into a location-routing problem for disaster relief logistics suited for decision support systems. Decision makers in humanitarian logistics face the challenge where to establish distribution centers (DCs) for relief goods. For this purpose, two objectives are considered: the impact of the relief operations on the beneficiaries and the efficient use of monetary resources. The proposed multi-objective location-routing model minimizes unserved demand as well as cost for opening DCs and for routing relief goods. It anticipates the choice of beneficiaries to which DC to go (if at all), based on a model adopted from the literature on competitive location analysis. A mathematical programming formulation is presented. For small instances, the Pareto front can be determined exactly using an epsilon constraint method. For solving also realistic instances, an evolutionary algorithm has been implemented and evaluated. The algorithms are tested on real-world instances from Mozambique. The results show that when designing a distribution network, improvements can be achieved by taking the predicted behavior of beneficiaries into account. [ABSTRACT FROM AUTHOR]

Decision making in health technology assessment (HTA) involves multiple criteria (clinical outcomes vs. cost) and risk (criteria measured with estimation error). A survey conducted among Polish HTA experts shows that opinions how to trade off health against money should be treated as fuzzy. We propose an approach that allows to introduce fuzziness into decision making process in HTA. Specifically, in the paper we (i) define a fuzzy preference relation between health technologies using an axiomatic approach; (ii) link it to the fuzzy willingness-to-pay and willingness-to-accept notions and show the survey results in Poland eliciting these; (iii) incorportate uncertainty additionally to fuzziness and define two concepts to support decision making: fuzzy expected net benefit and fuzzy expected acceptability (the counterparts of expected net benefit and cost-effectiveness acceptability curves, CEACs, often used in HTA). Illustrative examples show that our fuzzy approach may remove some problems with other methods (CEACs possibly being non-monotonic) and better illustrate the amount of uncertainty present in the decision problem. Our framework can be used in other multiple criteria decision problems under risk where trade-off coefficients between criteria are subjectively chosen. [ABSTRACT FROM AUTHOR]

This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage. [ABSTRACT FROM AUTHOR]

In the era of the Internet and big data, data permeate the entire process of multiple criteria decision making (MCDM). Therefore, generation of rational solutions from current observations and historical data has become an important and interesting issue. To address this issue, this paper proposes a data-driven MCDM framework in the context of the evidential reasoning approach. Three challenges in the framework are met, including the transformation of observations into assessments, the learning of parameters and their constraints from historical data, and the generation of a data-driven solution. The proposed framework is then used to model the diagnosis of thyroid cancer and generate data-driven diagnostic results. The three challenges in the application are met to aid radiologists in improving the diagnostic accuracy of thyroid cancers. To examine whether the application of the proposed data-driven MCDM framework to the diagnosis of thyroid cancer can help improve diagnostic accuracy, we conduct a case study by using the examination reports of three radiologists from July 2015 to October 2017 in the ultrasonic department of a tertiary hospital located in Hefei, Anhui Province, China. [ABSTRACT FROM AUTHOR]

A common way to model decision maker (DM) preferences in multiple criteria decision making problems is through the use of utility functions. The elicitation of the parameters of these functions is a major task that directly affects the validity and practical value of the decision making process. This paper proposes a novel Bayesian method that estimates the weights of criteria in linear additive utility functions by asking the DM to rank or select the best alternative in groups of decision alternatives. Our method computes the entire probability distribution of weights and utility predictions based on the DM's answers. Therefore, it enables the DM to estimate the expected value of weights and predictions, and the uncertainty regarding these values. Additionally, the proposed method can estimate the weights by asking the DM to evaluate few groups of decision alternatives since it can incorporate various types of inputs from the DM in the form of rankings, constraints and prior distributions. Our method successfully estimates criteria weights in two case studies about financial investment and university ranking decisions. Increasing the variety of inputs, such as using both ranking of decision alternatives and constraints on the importance of criteria, enables our method to compute more accurate estimations with fewer inputs from the DM. [ABSTRACT FROM AUTHOR]

The study area is La Colacha sub-basins from Arroyos Menores basins, natural areas at West and South of Río Cuarto in Province of Córdoba of Argentina, fertile with loess soils and monsoon temperate climate, but with soil erosions including regressive gullies that degrade them progressively. Cultivated gently since some hundred 60 years coordinated action planning has become necessary to conserve lands while keeping good agro-production. Some of the authors were engineers trained here and other had former experience in Salta, Argentina, using a set of Decision Theory Methods, (DTM), to consider the possible systems of soil uses and actions to be recommended. After having improved data on soils and on hydrology for the study area, they have adapted as academic study these DTM for Arroyos Menores areas where gully erosions are severe, also intending to offer better view for the necessary planning to have policies of actions and uses, done by the local society decision and services, and in part by land owners. This paper concentrates on the application of a set of discrete multi-criteria models (MCDM), where alternatives were global, considering soil conservation and hydrologic management actions, and main types of use of soil, and where criteria were about the diverse consequences, grouped as environmental, economic, and social. As methods they have used for the sub areas PROMETHEE with sets of weights, comparing results with ELECTRE with same data and weights, and with AHP method needing comparisons of criteria by authors, getting somehow compatible results, but finding that the 'weighted PROMETHEE II' was the more adapted for the present study. The system and results are very global, and are offered as indications for planning, that will require using detailed engineering and administration aspects for series of local singular actions or for management pursued in time. [ABSTRACT FROM AUTHOR]

In this paper a multi-criteria group decision making model is presented in which there is a heterogeneity among the decision makers due to their different expertise and/or their different level of political control. The relative importance of the decision makers in the group is handled in a soft manner using fuzzy relations. We suppose that each decision maker has his/her preferred solution, obtained by applying any of the techniques of distance-based multi-objective programming [compromise, goal programming (GP), goal programming with fuzzy hierarchy, etc.]. These solutions are used as aspiration levels in a group GP model in which the differences between the unwanted deviations are interpreted in terms of the degree of achievement of the relative importance amongst the group members. In this way, a group GP model with fuzzy hierarchy (Group-GPFH) is constructed. The solution for this model is proposed as a collective decision. To show the applicability of our proposal, a regional forest planning problem is addressed. The objective is to determine tree species composition in order to improve the values achieved by Pan-European indicators for sustainable forest management. This problem involves stakeholders with competing interests and different preference schemes for the aforementioned indicators. The application of our proposal to this problem allows us to be able to comfortably address all these issues. The results obtained are consistent with the preferences of each stakeholder and their hierarchy within the group. [ABSTRACT FROM AUTHOR]

In multiple attribute decision analysis, many methods have been proposed to determine attribute weights. However, solution reliability is rarely considered in those methods. This paper develops an objective method in the context of the evidential reasoning approach to determine attribute weights which achieve high solution reliability. Firstly, the minimal satisfaction indicator of each alternative on each attribute is constructed using the performance data of each alternative. Secondly, the concept of superior intensity of an alternative is introduced and constructed using the minimal satisfaction of each alternative. Thirdly, the concept of solution reliability on each attribute is defined as the ordered weighted averaging (OWA) of the superior intensity of each alternative. Fourthly, to calculate the solution reliability on each attribute, the methods for determining the weights of the OWA operator are developed based on the minimax disparity method. Then, each attribute weight is calculated by letting it be proportional to the solution reliability on that attribute. A problem of selecting leading industries is investigated to demonstrate the applicability and validity of the proposed method. Finally, the proposed method is compared with other four methods using the problem, which demonstrates the high solution reliability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper we deal with an urban and territorial planning problem by applying the Non Additive Robust Ordinal Regression (NAROR). NAROR is a recent extension of the Robust Ordinal Regression family of Multiple Criteria Decision Aiding methods to the Choquet integral preference model which permits to represent interaction between considered criteria through the use of a set of non-additive weights called capacity or fuzzy measure. The use of NAROR permits the Decision Maker (DM) to give preference information in terms of preferences between pairs of alternatives with which she is familiar, and relative importance and interaction of considered criteria. The basic idea of NAROR is to consider the whole set of capacities that are compatible with the preference information given by the DM. In fact, the recommendation supplied by NAROR is expressed in terms of necessary preferences, in case an alternative is preferred to another for all compatible capacities, and of possible preferences, in case an alternative is preferred to another for at least one compatible capacity. In the considered case study, several sites for the location of a landfill are analyzed and compared through the use of the NAROR on the basis of different criteria, such as presence of population, hydrogeological risk, interferences on transport infrastructures and economic cost. This paper is the first application of NAROR to a real-world problem, even if not already with real DMs, but with a panel of experts simulating the decision process. [ABSTRACT FROM AUTHOR]

This paper presents an application of extended goal programming in the field of offshore wind farm site selection. The strategic importance of offshore shore wind farms is outlined, drawing on the case of the United Kingdom proposed round three sites as an example. The use of multi-objective modelling methodologies for the offshore wind farm sector is reviewed. The technique of extended goal programming is outlined and its flexibility in combining different decision maker philosophies described. An extended goal programming model for site selection based on the United Kingdom future sites is then developed and a parametric analysis undertaken at the meta-objective level. The results are discussed and conclusions are drawn. [ABSTRACT FROM AUTHOR]

The vulnerability assessment is important for earthquake prevention and mitigation. Since many criteria need to be considered during the evaluation process, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes an approach which integrates the results of different MCDM methods to provide regional earthquake vulnerability assessment. The key idea of this approach is to determine the most trustable MCDM method by calculating the weights of several MCDM methods using the Spearman's ranking correlation coefficients. The most trustable MCDM method is the one with the highest weight, which indicates that it has the strongest agreements with other MCDM methods, and is used to provide a final assessment using the combination of other MCDM methods. The proposed approach is applied to evaluate the earthquake vulnerability of 31 Chinese regions using six MCDM methods and eleven vulnerability evaluation indices. The results indicate that the proposed approach can integrate the inconsistent evaluation results of different MCDM methods and produce a comprehensive assessment of regional earthquake vulnerability. [ABSTRACT FROM AUTHOR]

Multi-criteria decision making (MCDM) provides effective methods for dealing with the challenge of sorting credit ratings. This paper presents a novel data-driven MCDM sorting approach to predicting credit ratings. Our methodology combines the fuzzy TOPSIS-Sort-C model with the fuzzy best-worst approach, supported by a fuzzy cognitive map, to effectively deal with criteria interactions. This approach provides a corporate credit risk assessment, taking into account the uncertainties in credit risk assessment and relevance of its criteria by using fuzzy c-means and correlation-based feature selection. Our empirical analysis of 1138 US companies demonstrates the reliability of our model in dealing with a range of financial and non-financial indicators. The results demonstrate the potential of our methodology in credit rating assessment, with a good predictive performance relative to existing models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new way to analyze bimatrix games. This new approach consists of considering the game as a bicriteria matrix game. The solution concepts behind this game are based on getting the probability to achieve some prespecified goals. We consider as a part of the solution, not only the payoff values, but also the probability to get them. In addition, to avoid the choice of only one goal, two different approaches are used. Firstly, sensitivity analysis of the solution set is carried out on the range of goals, secondly a partition of the goal space in a finite number of regions is presented. Some examples are included to illustrate the results in the paper. [ABSTRACT FROM AUTHOR]

This paper discusses necessary and sufficient preference conditions for utility models based on a risk-value framework. These conditions provide additional insights into traditional utility models regarding decision making by risk-value tradeoffs, and can help decision makers identify specific functional forms of utility measure in practice. [ABSTRACT FROM AUTHOR]

Classical Operations Research assumed objectivity. Operations researchers hardly bothered to ask the decision maker about his or her preferences, assuming that a well-defined single objective function was an adequate representation of the decision problem. Many multicriteria decision methods began in response to this failure of Operations Research, Other methods took a totally different and more subjective point of view. In this paper, we discuss objective and subjective descriptions, their interpretation and use in decision making. In the center of the ability to distinguish between these objective and subjective components stand scientific methods and scientists. We argue that it is possible and necessary to understand where and how objective measures should be replaced by subjective measures and judgement in the decision making process. [ABSTRACT FROM AUTHOR]