Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade

Picture fuzzy Additive Ratio Assessment Method (ARAS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for multi-attribute decision problem and their application

The purpose of this paper is to study the multi-attribute decision-making problem under the fuzzy picture environment. First, a method to compare the pros and cons of picture fuzzy numbers (PFNs) is introduced in this paper. Second, the correlation coefficient and standard deviation (CCSD) method is used to determine the attribute weight information under the picture fuzzy environment regardless of whether the attribute weight information is partially unknown or completely unknown. Third, the ARAS and VIKOR methods are extended to the picture fuzzy environment, and the proposed PFNs comparison rules are also applied in the PFS-ARAS and PFS-VIKOR methods. Fourth, the problem of green supplier selection in a picture-ambiguous environment is solved by the method proposed in this paper. Finally, the method proposed in this paper is compared with some methods and the results are analyzed.

A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies, Recovery Measures, and Future Research Directions

The COVID-19 pandemic has caused significant disruptions in the freight transport sector. The number of studies on the impact of COVID-19 on freight transport and possible mitigation strategies are growing. However, a systematic and comprehensive review highlighting the research themes, main findings, research methods, and future research directions of these studies remains scarce. Therefore, this study presents a mixed review comprising scientometric and systematic reviews to cover these research gaps. Results show that 68 studies have been published on this topic since the beginning of 2020 and that they cover three main themes: the impacts of COVID-19 on freight transport, mitigation strategies, and recovery during and after COVID-19. In addition, we describe the research methods, main findings, and possible research directions in each of them. Thus, the findings of our work present both theoretical and practical analyses of COVID-19-related research on freight transport and provide important future research directions in this domain.

Micro-hub location selection for sustainable last-mile delivery

Sustainable Last-Mile Delivery (LMD) is one of the key phases in city logistics. Micro-hubs in cities are new emerging solutions for an easier and viable last-mile delivery process. The important question in smart and modern cities is the determination of the best micro-hub location for the LMD. This paper solves the micro-hub location selection for sustainable LMD using the multi-criteria decision-making (MCDM) techniques. The main reason for solving the micro-hub location selection is to make the last-mile delivery process in Pardubice as easier and effortless as possible. The Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (CRITIC) method, and Weighted Aggregated Sum Product Assessment (WASPAS) method are coupled to solve the micro-hub location selection for sustainable LMD. First, five criteria and alternatives are identified and discussed with the experts. Second, the hybrid criteria importance is determined by combining the BWM and CRITIC methods. Third, the obtained hybrid weights are integrated within the WASPAS method to rank the micro-hub locations. The findings of the Hybrid BWM-CRITIC-WASPAS model show the Alternative 2 („Hůrka“) as the best possible location for Pardubice in the context of the LMD. In addition, a comparative analysis with some of the existing MCDM approaches is conducted for the same problem and its results show a high level of matching with the applied hybrid BWM-CRITIC-WASPAS method, which means that Alternative 2 („Hůrka“) is strongly recommended as a micro-hub location for sustainable LMD in Pardubice.

Modified WASPAS method based on the pythagorean fuzzy frank interaction aggregation operators and its application in cloud computing product selection

Frank operations are more robust and flexible than other algebraic operations, and interaction operational laws consider interrelationship between membership functions in Pythagorean fuzzy number. Combining the strengths of both, we define some Frank interaction operational laws of Pythagorean fuzzy numbers for the first time in this article. Based on this, the Pythagorean fuzzy Frank interaction weighted averaging and geometric operators are developed. Meanwhile, we discuss their basic properties and related special cases. Furthermore, a novel multiple attribute decision-making framework is established based on the modified WASPAS method in Pythagorean fuzzy environment. The proposed method is implemented in a real-case study of cloud computing product selection to test the proposed methodology’s plausibility. A sensitivity analysis is conducted to verify our method’s reliability, and the effectiveness and superiority are illustrated by comparative study.

Applicability of Industry 4.0 Technologies in the Reverse Logistics: A Circular Economy Approach Based on COmprehensive Distance Based RAnking (COBRA) Method

The logistics sector plays one of the most important roles in the supply chain with the aim of providing a fast, flexible, safe, economical, efficient, and environmentally acceptable performance of freight transport flows. In addition, the popularization of the concept of a circular economy (CE) used to retain goods, components, and materials at their highest usability and value at all times, illustrates the importance of the adequate performance of reverse logistics (RL) processes. However, traditional RL is unable to cope with the requirements of modern supply chains and requires the application of Industry 4.0 technologies, which would make it more efficient. The main aim of this study was to evaluate the applicability of various Industry 4.0 technologies in the RL sector in order to point out the most applicable ones. To solve the defined problem, a novel multi-criteria decision making (MCDM) model was defined by combining the best—worst method (BWM) to obtain the criteria weights, and the newly developed comprehensive distance-based ranking (COBRA) method to rank the technologies. Another aim of the study was to validate the newly established method. The results indicated that the most applicable technologies were the Internet of Things, cloud computing, and electronic—mobile marketplaces. These technologies will have a significant impact on the development of RL and the establishment of CE systems, thus bringing about all the related positive effects.

Prioritization of healthcare systems during pandemics using Cronbach's measure based fuzzy WASPAS approach

Pandemics are well-known as epidemics that spread globally and cause many illnesses and mortality. Because of globalization, the accelerated occurrence and circulation of new microbes, the infection has emerged and the incidence and movement of new microbes have sped up. Using technological devices to minimize the visit durations, specifying days for handling chronic diseases, subsidy for the staff are the alternatives that can help prevent healthcare systems from collapsing during pandemics. The study aims to define the efficient usage of optimization tools during pandemics to prevent healthcare systems from collapsing. In this study, a new integrated framework with fuzzy information is developed, which attempts to prioritize these alternatives for policymakers. First, rating data are assigned respective fuzzy values using the standard singleton grades. Later, criteria weights are determined by extending Cronbach´s measure to fuzzy context. The measure not only understands data consistency comprehensively, but also takes into consideration the attitudinal characteristics of experts. By this approach, a rational weight vector is obtained for decision-making. Further, an improved Weighted Aggregated Sum Product Assessment (WASPAS) algorithm is put forward for ranking alternatives, which is flexibly considering criteria along with personalized ordering and holistic ordering alternatives. The usefulness of the developed framework is tested with the help of a real case study. Rank values of alternatives when unbiased weights are used is given by 0.741, 0.582, 0.640 with ordering asR 1 ≻ R 3 ≻ R 2 . The sensitivity/comparative analysis reveals the impact of the proposed model as useful in selecting the best alternative for the healthcare systems during pandemics.

T-spherical fuzzy COPRAS method for multi-criteria decision-making problem

In this manuscript, we introduce a multi-criteria decision-making (MCDM) method under T-spherical fuzzy set environment. Firstly, we propose a method to use the correlation coefficient and standard deviation (CCSD) method to determine the attribute weight under T-spherical fuzzy environment, when the attribute weight information is completely unknown or partially unknown. Secondly, we introduce a T-spherical fuzzy complex proportional assessment (COPRAS) method. Finally, a numerical example is given to illustrate the application of the T-spherical fuzzy COPRAS method, and some comparative analysis is carried out to verify the feasibility and effectiveness of the proposed method.