Heterogeneous group decision making with thermodynamical parameters

There often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the decision-making problems with heterogeneous information. The paper proposes a framework for group decisionmaking problems with heterogeneous information with thermodynamical parameters consisting of three parts to achieving this goal. The first part builds the rectifications of criteria weights according to decision makers’ confidence in evaluations. The second part adopts thermodynamical parameters to measure the numerical values and the data distribution of heterogeneous information to characterize the heterogeneous information fully. The last part applies the TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) to aggregate the decision-making results based on the characterized heterogeneous information without transforming it into a unified form. By depicting decision makers’ different sensitive attitudes towards uncertainty by several mathematical expressions, experiments are performed to assess the sensitive attitudes’ impacts on decisionmaking results with the proposed framework. Finally, a case study on the selection of a green supplier under the low-carbon economy is provided to illustrate the flexibility and feasibility of the proposed framework

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

Algorithms for probabilistic uncertain linguistic multiple attribute group decision making based on the GRA and CRITIC method: application to location planning of electric vehicle charging stations

Electric vehicles (EVs) could be regarded as one of the most innovative and high technologies all over the world to cope with the fossil fuel energy resource crisis and environmental pollution issues. As the initiatory task of EV charging station (EVCS) construction, site selection play an important part throughout the whole life cycle, which is deemed to be multiple attribute group decision making (MAGDM) problem involving many experts and many conflicting attributes. In this paper, a grey relational analysis (GRA) method is investigated to tackle the probabilistic uncertain linguistic MAGDM in which the attribute weights are completely unknown information. Firstly, the definition of the expected value is then employed to objectively derive the attribute weights based on the CRiteria Importance Through Intercriteria Correlation (CRITIC) method. Then, the optimal alternative is chosen by calculating largest relative relational degree from the probabilistic uncertain linguistic positive ideal solution (PULPIS) which considers both the largest grey relational coefficient from the PULPIS and the smallest grey relational coefficient from the probabilistic uncertain linguistic negative ideal solution (PULNIS). Finally, a numerical case for site selection of electric vehicle charging stations (EVCS) is designed to illustrate the proposed method. The result shows the approach is simple, effective and easy to calculate

Learning consumer preferences from online textual reviews and ratings based on the aggregation-disaggregation paradigm with attitudinal Choquet integral

Online reviews contain a wealth of information about customers’ concerns and sentiments. Sentiment analysis can mine consumer preferences and satisfaction over products/services. Most existing studies on sentiment analysis only considered how to extract attribute types or attribute values of products/services from textual reviews, but ignored the role of attribute-level ratings in reflecting consumer preferences and satisfaction. Based on sentiment analysis and preference disaggregation, this paper unifies the quantitative and qualitative information extracted from attribute-level ratings and textual reviews, respectively, to obtain attribute types and attribute values of products/services. To acquire individual consumer preferences concerning product/service attributes, this paper proposes a method within an aggregation-disaggregation paradigm based on the attitudinal Choquet integral to transform overall online ratings into the form of pairwise comparisons. Compared with the additive value function used in most studies, more consumer preferences in terms of the importance of attributes, the interactions between pairwise attributes, and the tolerance of consumers to make compensation between attribute values in the aggregation process can be deduced by our proposed method. Several real cases on TripAdvisor.com are given to show the applicability of the proposed method

Key Challenges to Sustainable Humanitarian Supply Chains: Lessons from the COVID-19 Pandemic

COVID-19 has had a major impact on health, economic, social, and industrial activities. It has disrupted supply chain management and affected the movement of essential supplies to a large extent. This study aims to identify and evaluate the challenges hampering sustainable humanitarian supply chain management (SHSCM). Twenty critical challenges to SHSCM are identified using a comprehensive literature review, and three strategies were developed. The challenges and strategies were verified using expert input. The challenges were evaluated using the neutrosophic analytic hierarchical process (AHP) method. The neutrosophic TODIM (an acronym in Portuguese for interactive multicriteria decision making) method was then used to select the best strategy. The findings reveal that facility location problems, short lead times for emergency supplies, spread of rumors, rapid emergence of new clusters, and doubt concerning the available remedy are five critical challenges in SHSCM during COVID-19. Public–private partnerships are identified as the best strategy in SHSCM. Finally, this paper discusses the implications to sustainable development goals in the post-COVID-19 pandemic era.</jats:p

A Decision Method for Online Purchases Considering Dynamic Information Preference Based on Sentiment Orientation Classification and Discrete DIFWA Operators

© 2013 IEEE. Online reviews are crucial for evaluating product features and supporting consumers' purchase decisions. However, as a result of online buying behaviors, consumer habits, and discrete dynamic distribution characteristics of online reviews, and consumers typically randomly choose a limited number of reviews from discrete time frames among all reviews and give more weight to recent review information and less weight to earlier information to support their online purchase decisions; moreover, existing studies have ignored the discrete random dynamic characteristics and dynamic information preferences of consumers. To address this issue, this paper proposes a method based on sentiment orientation classification and discrete DIFWA (DDIFWA) operators for online purchase decisions considering dynamic information preferences. In this method, we transformed review texts from original discrete time slices to discrete random features, extracted product features based on the constructed feature and sentiment dictionaries, and matched pairs of features and sentiment phrases in the dictionaries. We subsequently employed three types of semantic orientation by defining semantic rules to extract the product features of each review. Of note, the semantic orientations were transformed from discrete time to semantic intuitionistic fuzzy numbers and semantic intuitionistic fuzzy information matrixes. Furthermore, we proposed two DDIFWA operators to aggregate the dynamic semantic intuitionistic fuzzy information. Specifically, we obtained the rankings of alternative products and their features to support consumers' purchase decisions using the intuitionistic fuzzy scoring function and the 'vertical projection distance' method. Finally, comparisons and experiments are provided to demonstrate the plausibility of our methods

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl