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As one of the important energy industries, the safe production of coal mines not only concerns the safety of employees, but also affects the sustainable development of society. Recent rescue cases have shown that the layout of material bases and the allocation of emergency materials need to be further optimized. For this problem, this paper adopts the material hierarchical reserve mode for the first time, combining the material hierarchical reserve mode, multi-level material bases selection model, and the Fuzzy Analytic Hierarchy Process (FAHP) method to determine the optimal location of the coal mine material bases in Liaoning Province. Determine the reserve level of emergency materials through evaluation and clustering analysis of various emergency materials. Improve the traditional gravity model and construct a multi-objective planning model for multi-level material bases site selection. Apply the reserve level of emergency materials and real regional data to the site selection model, and use the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to solve the model. Then, the optimal site selection scheme was selected using FAHP, and sensitivity analysis was conducted. The research results indicate that: 1) 9 county-level, 6 city-level and 2 provincial-level material bases will be constructed; 2) location selection of material bases is not only affected by costs, but also by the density of road networks; 3) use of hierarchical material reserve mode can significantly reduce the construction cost of material bases. Compared to the traditional material reserve mode, it can reduce costs by 86.6%. Compared to a single-level material bases, it can still reduce costs by 74.7%. The research results of this paper can be used for the selection of emergency material bases and the allocation of emergency material reserves, which can reduce costs and promote the improvement of regional emergency rescue capabilities.

In the dynamic landscape of Industry 4.0, the selection of Third-Party Logistics Providers (3PLs) has emerged as a critical strategic decision for businesses seeking to optimize their supply chain operations. This paper aims to develop a new hybrid Multi-Criteria Decision-Making (MCDM) approach based on Industry 4.0 components for selecting the best 3PL provider by integrating Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Measurement Alternatives and Ranking according to Compromise Solution (FMARCOS). The criteria for 3PL evaluation and selection are determined with respect to literature studies and experts’ consultation. In the proposed approach, judgments of different experts are expressed by linguistic terms based on fuzzy numbers. The criteria weights are calculated by applying FAHP, then the ranking and selection of the best potential 3PL provider have been done using FMARCOS. A case study from a manufacturing company is illustrated. Finally, sensitivity analysis on the criteria weights and comparative analysis among MCDM methods (FMABAC, FWASPAS, FCOSOSO, FSAW, FCOPRAS, FTOPSIS and FVIKOR) are conducted for the validity of the results. The results indicate that the integrated FAHP and FMARCOS model offers a robust and adaptable framework for 3PL selection, enabling companies to navigate the complexities of Industry 4.0 with a strategic and informed approach. This research contributes to the evolving discourse on logistics optimization in the era of Industry 4.0 and provides practical insights for industry practitioners, academics, and policymakers.

The objective of the present paper is to develop a systematic and sustainability-focused evaluation framework to determine the best locations for wind plants in Vietnam. For the decision-making that influences wind plant site selection, while some criteria can be evaluated based on a numerical database, numerous qualitative criteria must be under experts’ judgments in the form of linguistic terms and can be represented through fuzzy numbers. In this direction, a two-phase framework of multi-criteria decision making (MCDM) methodologies using the merits of Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Weighted Aggregated Sum-Product Assessment (FWASPAS) is proposed for the first time. In the first phase using DEA, the most potential locations regarding their efficiency on various quantitative criteria are selected. These locations are further assessed in the second phase in terms of qualitative criteria viz. technical, economic, environmental, and socio-political factors. For this evaluation, FAHP was used for criteria weighting, and FWASPAS ranks the provinces. A comparison with the existing locations, sensitivity analysis of criteria, and comparative analysis of methods are performed to support the outcomes of the proposed work. The results illustrate that all methods reach common rankings, in which Binh Thuan province got the highest rank. This shows that the applied models are robust in nature. Implementation of the proposed approach can enable governments and stakeholders to save costs and make the most out of resources in the planning phase of wind power plants or any other renewable energy projects.

The rapid popularization of multimode terminals that simultaneously run multiple services (such as browsing web pages during a video session) has brought a decent amount of attention to the network selection problem of multimode terminals. However, most network selection algorithms proposed for vertical handoff are only suitable for terminals running a single service. This paper proposes a network selection algorithm for multiservice multimode terminals in heterogeneous wireless networks. The algorithm considers user preferences, network attributes, and service characteristics. Entropy and fuzzy analytic hierarchy process (FAHP) are used to calculate the objective weights of the network attributes and the weights determined by the service characteristics, respectively. The comprehensive weights of network attributes are obtained by combining the user preferences and service priority. At the same time, different utility functions are used to calculate the utility values of the network attributes for multiservice. Finally, the simple additive weighting (SAW) method is used to synthesize the utility values and the comprehensives weights, while the most appropriate network is selected by a technique for order preference by similarity to an ideal solution (TOPSIS) and a threshold. The simulation results show that the proposed algorithm can accurately select the most appropriate network by considering different factors. Compared to the existing two MMT network selection algorithms, it can reduce the number of vertical handovers and obtain better user experience while satisfying user's preferences and service's requirements, thus solving the multiservice multimode terminals network selection problem.

Today's software complexity makes developing defect-free software almost impossible. Consequently, developing classifiers to classify software modules into defective and non-defective before software releases have attracted great interest in academia and software industry alike. Although many classifiers have been proposed, no one has been proven superior over others. The major reason is that while a research shows that classifier A is better than classifier B, we can find other research that shows the opposite. These conflicts are usually triggered when researchers report results using their preferable performance evaluation measures such as, recall and precision. Although this approach is valid, it does not examine all possible facets of classifiers performance characteristics. Thus, the performance evaluation might improve or deteriorate if researchers choose other performance measures. As a result, software developers usually struggle to select the most suitable classifier to use in their projects. The goal of this paper is to apply the fuzzy analytical hierarchy process (FAHP) as a popular multicriteria decision-making technique to reliably evaluate classifiers' performance. This evaluation framework incorporates a wider spectrum of performance measures to evaluate classifiers performance rather than relying on selected preferable measures. The results show that this approach will increase software developers' confidence in research outcomes and help them in avoiding false conclusions and infer reasonable boundaries for them. We exploited 22 popular performance measures and 11 software defect classifiers. The analysis was carried out using KNIME data mining platform and 12 software defect data sets provided by the NASA metrics data program (MDP) repository.

Cloud manufacturing (CMfg) is an innovative manufacturing paradigm in which geographically distributed various manufacturing resources are encapsulated into manufacturing services (MSs), and also these MSs can provide users with various services. Users can find and choose qualified MSs to form cliques to help them complete their manufacturing tasks via Internet. Trust makes cooperative endeavors happen. Hence, it is an important problem to evaluate the trustworthiness of MSs in CMfg. In this paper, we present a comprehensive assessment approach to evaluate the trustworthiness of a MS in CMfg. The approach evaluates a MS from both objective and subjective perspectives, named as credit assessment and reliability assessment, respectively. Multinomial logistic regression analysis is presented to assess the credit of a MS, while fuzzy analytic hierarchy process is used to assess its reliability. Finally, a case study is presented and the results show the comprehensive assessment approach is efficacious at the trustworthiness evaluation of a MS.