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Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

The fuzziness and complexity of evaluation information are common phenomenon in practical decision-making problem, interval neutrosophic sets (INSs) is a power tool to deal with ambiguous information. Similarity measure plays an important role in judging the degree between ideal and each alternative in decision-making process, the purpose of this paper is to establish a multi-criteria decision-making method based on similarity measure under INSs.

Based on an extension of existing cosine similarity, this paper first introduces an improved cosine similarity measure between interval neutosophic numbers, which considers the degrees of the truth membership, the indeterminacy membership and the falsity membership of the evaluation values. And then a multi-criteria decision-making method is established based on the improved cosine similarity measure, in which the ordered weighted averaging (OWA) is adopted to aggregate the neutrosophic information related to each alternative. Finally, an example on supplier selection is given to illustrate the feasibility and practicality of the presented decision-making method.

In the whole process of research and practice, it was realized that the application field of the proposed similarity measure theory still should be expanded, and the development of interval number theory is one of further research direction.

The main contributions of this paper are as follows: this study presents an improved cosine similarity measure under INSs, in which the weights of the three independent components of an interval number are taken into account; OWA are adopted to aggregate the neutrosophic information related to each alternative; and a multi-criteria decision-making method using the proposed similarity is developed under INSs.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

Pharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.

To achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.

The feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.

The proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.

A new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

In this study, research problem has been designed as a fleet-based optimization problem. This paper aims to present fleet modelling with risk taxonomy. Fleet modelling has been assumed as strategic multi-criteria decision-making problem to capacity building. Capacity building risk management is an essential element within the scope of its strategy to ensure sustainable corporate performance. Optimization is a fundamental target in aviation business’ strategy and management since the manager make decisions in their multi-interrelated criteria environment. Also, aviation is a highly regulated sector, and its operational and business procedures have certain limits by both national and international authorities. For this reason, companies implement risk management for strategic optimization while performing operations in compliance with the legislation. Risk management with capacity building and resource dependency perspective applied for strategic optimization aims to capture opportunities and result in threats with minimum accidents and incidents.

The taxonomy and analytical hierarchy process (AHP) have been identified as methodologies in this research. The type of training in the high organizational performance of an approved training organization, strategy, resources and allocations with the corporate objectives, the amount and qualifications of the flight crew, their professionalism, maintenance team and licenses, hangar conditions and capacity, authority requirements and limits, region conditions, altitude and meteorology, student profile, together with a multi-criteria decision are to be considered. For each criterion, there are resources and thus resource dependence. In this study, the analytical network process method was used. In the construction of new taxonomy, specific criteria have been considered, and the analysis has been accomplished as multi-criteria decision-making problem because of the relationship and interaction between them. A number of professionals with high knowledge of the pilots and manager from Air Traffic Organization participated in the study.

The fleet modelling is both strategic and operational decision issue for training organizations. In this issue, there is a vital problem as which aircrafts should include fleet? Main criteria and sub-criteria are analyzed by AHP method and sorted according to their priorities and the fleet qualifications consisting of the most suitable aircraft/aircraft are presented. The finding and suggestions will contribute to establish sustainable organization in based on capacity building and resource dependency for managers. While analyzing main criteria, the important criteria which were found were strategic and then operational. After ordering main criteria, sub-criteria were analyzed and were multiplicated with their items. According to study findings, aircraft suitability for training model is the most important item. It follows respectively aircraft maintenance sustainability, cost of aircraft supply and faculty budget adequacy. However, operation characteristics of the square that is less important item was found. It was seen that the strategies used to manage dependencies used the bridge strategy. The results we obtained with the interviews with pilot managers are very significant in terms of resource dependence on the subject of fleet optimization. While first criterion is operational, it continues with strategic and financial criteria. After interviews with pilot managers, it was figured out that maintenance is also very important criteria. For managing this dependency, university has acquisitions, which is one of the strategy to manage dependency, rather than outsourcing. For this reason, maintenance criterion has lower importance than others. When thinking of other criteria, strategic and financial criteria have played an important role. University has tried to decrease dependency and increase sustainability.

Aircraft selection is a strategic decision of fleet modelling in both aviation business and also training organizations via influencing their corporate performance, operational performance, capacity building and their sustainability. There are some factors that limit the criteria, as research problem has been developed for approved training organizations not airlines. For this reason, our research is limited with fleet of training organizations. Our findings and suggestions may be useful for flight schools to managing their resource dependency and also to their capacity building. In this research, new taxonomy has been developed depending on training organizations’ qualifications. Airlines may improve this taxonomy to use in their decision-making process.

The fleets, which were established considering the taxonomy in this study, will be able to manage the risk of resource dependency more successfully. Pilot candidates will be able to provide a more ergonomic and higher quality education. This research and its findings will contribute to the development of organizations’ accurate and timely decision-making skills. Resource dependency may threat organizational sustainability in our research, New taxonomy and our holistic approach will support organizational efforts to achieve sustainable strategies.

New taxonomy to modelling fleet that has been developed in this research may provide contribution to approved training organizations for both managing resource dependency-based risks and to capacity building-related decision-making process. This research may serve organizations as strategic decision-making tool. And also this kind of study may contribute to improve sustainability of organizations and serve more good fleet for their pilot candidates. For these reasons, this research may create social implications, as both resource using and capacity building will make contribution for society and add value.

This research presents new risk taxonomy and criteria. Also new taxonomy and its criteria are analysed with AHP. It is thought that this research shows risk management-based approach for fleet modelling creates benefits for approved training organizations to using their limited sources effectively and efficiently. The article includes risk management and capacity building-related approach to decision-making. also, this research presents modeling which will contribute to the management field besides literature. In developing taxonomy process, the analysis has been conducted, based on expert opinions and referred to for these pairwise comparisons. Airlines managers and risk managers may examine their fleet modelling according to our taxonomy which is based on risk management.

Project selection is a critical decision for any organization seeking to commission a large-scale construction project. Project selection is a complex multi-criteria decision-making problem with significant uncertainty and high risks. Fuzzy set theory has been used to address various aspects of project uncertainty, but with key practical limitations. This study aims to develop and apply a novel Pythagorean fuzzy sets (PFSs) approach that overcomes these key limitations.

The study is particular to complex project selection in the context of increasing interest in resilience as a key project selection criterion. Project resilience is proposed and considered in the specific situation of a large-scale construction project selection case study. The case study develops and applies a PFS approach to manage project uncertainty. The case study is presented to demonstrate how PFS is applied to a practical problem of realistic complexity. Working through the case study highlights some of the key benefits of the PFS approach for practicing project managers and decision-makers in general.

The PFSs approach proposed in this study is shown to be scalable, efficient, generalizable and practical. The results confirm that the inclusion of last aggregation and last defuzzification avoids the potentially critical information loss and relative lack of transparency. Most especially, the developed PFS is able to accommodate and manage domain expert expressions of uncertainty that are realistic and practical.

The main novelty of this study is to address project resilience in the form of multi-criteria evaluation and decision-making under PFS uncertainty. The approach is defined mathematically and presented as a six-step approach to decision-making. The PFS approach is given to allow multiple domain experts to focus more clearly on accurate expressions of their agreement and disagreement. PFS is shown to be an important new direction in practical multi-criteria decision-making methods for the project management practitioner.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

The purpose of this paper is the application of a tool to assist the multi-criteria decision-making process for selecting an asset for a company in the metallurgical industry which manufactures metal parts for diverse industries.

This investment, complex due to the commitment of resources it requires, has been made with the assistance of decision-making methodologies, specifically versions I and IV of the ELECTRE method.

This model of multi-criteria decision making has been chosen over other models because it offers the possibility of including technical and economic decisions so they can be analyzed simultaneously, therefore the decision is not based solely on financial aspects. Many companies base their decisions exclusively on financial returns, however in this case it is also appropriate to include the technical parameters, since the asset being replaced is the most important asset of the company.

Applying version I of the methodology, the optimal technical configuration of the asset will be analyzed based on the features requirements, all of which are among options available in the market. Once a subset of technically and economically viable alternatives has been defined, version IV will be applied and a ranking of the alternatives from the best to the worst will be obtained and, based on this ranking, the final decision will be made.

Multi‐criteria decision making exists in the daily lives with broad application backgrounds. Sometimes because of incomplete information, the weights can only be estimated subjectively, which leads to an unsatisfactory result. The purpose of this paper is to describe an interactive technique to decide multi‐criteria weights by multiple decision makers in the condition of incomplete information by means of virtual environment.

The procedure assumed a problem with n criteria and r decision makers. The algorithms employed are presented.

It was found that the proposed framework is an effective weight‐deciding tool; the procedure quickly locates excellent compromise weights in a series of test problems.

The methodology helps decision makers determine a most preferred final solution by the virtual environment especially when there is incomplete information. The procedures provide a mechanism to guide the decision makers towards an acceptable compromise solution, without requiring excessive decision maker input. Studies show that the procedure performs well in terms of solution quality, simplicity, computational requirements, convergence and flexibility.