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Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

Technological advancements have benefited businesses all over the world in how they set up production lines, create new products/services and trade goods. Multinational corporations can communicate instantly with their distant operations by utilizing information technology tools and communication networks. Businesses have taken a significant shift and new factors have emerged which affect company's competitiveness. In case of resorting to an outsourcing option, a comprehensive approach for valuing the essential criteria is often missing. While specifically focusing on the decisions that have a huge impact on company's performance, it is crucial to pay close attention to the ways of selecting suppliers. The purpose of research is to choose the optimal manufacturing alternative from a set of possibilities.

The current research utilizes the Delphi technique for collection of vital criteria such as “quality”, “cost”, “delivery”, “warranties and claims”, “supplier profile”, “relationship and communication” and their respective sub-criteria. The purpose of research is to choose the optimal manufacturing alternative from a set of possibilities. In this regard, Analytical Hierarchy Process (AHP) technique is employed.

The current research enlightens that outsourcing can yield promising beneficial results. The results highlighted that in Hi-tech public sector organizations, international alternative is found best in almost all criteria especially in vital criteria such as “Quality”, “Cost”, “Delivery”, “Supplier Profile,” etc. Similarly, in case the outsourcing is done to a Domestic alternative, still the Domestic alternative is found effective in comparison to in-house manufacturing setups. The research showed unexpected results. Because previously it was assumed that in-house manufacturing would be more beneficial. However, the current findings support the “NASA” strategy which moved toward outsourcing to private sector.

Limitations of the proposed methodology also produce opportunities for further exploration of the topic. One key limitation of the research described in this study is that the parameters and their sub-parameters interdependency were not taken under consideration. This means that quality and cost are not dependent upon each other. However, in reality quality and cost are interlinked. This means if quality is increased, cost is also increased. Similarly, for products having zero percent of re-claim, the product would have to be manufactured with high quality.

The study is advantageous for both suppliers and purchasers, in any type of businesses where decision-making problem are under consideration. This model aids suppliers in revealing, how they can expand their profile, by focusing on the current research's selection criteria. In this way alternatives profile can now be perfected. Moreover, buyers can now rank suppliers on their quality management, financial status and other essential factors in order to conduct purchasing decisions. For the decision maker, the results illustrate which critical factors to evaluate when screening suppliers by applying current model techniques.

It is obvious that nearly almost every industry is forced to look for alternatives for all of its operations if outsourcing is an option. The study's findings have major benefits for all industries with an important role in manufacturing and supply chain operations. These objectives will serve the industries well and they will be able to prioritize their alternative selection criteria based on their operations. The findings of this study can assist any organization in their selection of vendors by providing a more detailed explanation of the impact that various criteria have on the decision-making process.

To the best of authors' knowledge, no previous study has used two approaches (AHP and Delphi study) to propose a model for making manufacturing decisions with domestic, in house and international alternatives in Hi-tech public sector organizations. The model not only benefits the manufacturers for choosing suitable suppliers but also aids suppliers to build their profile in an improved fashion by focusing on the vital attributes. This research benefits managers to improve their ability to make effective purchasing decisions, and also opens new avenues for researchers to further explore such findings in other areas as well.

In this study, the deployment of an integrated Quality Function Deployment (QFD) decision framework is presented to help cities design targeted strategies to become “smart”. Arguably smart cities leverage advanced technologies to enhance their smartness to improve everyday urban life. To this end, a QFD – Analytic Hierarchy Process – Analytic Network Process (QFD-AHP-ANP) framework is proposed to deliver guidance for selecting the appropriate mix of smart technologies based on the specific smart needs of each city.

The AHP and ANP methods are incorporated into QFD to enhance its methodological robustness in formulating the decision problem. AHP accurately captures and translates the “Voice of the Experts” into prioritized “Smart City” dimensions, while establishing inter-relationships between these dimensions and “Smart City Technologies”. Meanwhile, ANP explores tradeoffs among the technologies, enabling well-informed decisions. The framework’s effectiveness is evaluated through an illustrative application in the city of Thessaloniki.

Applying the framework to this real-world context confirms its practicality and utility, demonstrating its ability to particularize local, social, political, environmental and economic trends through the resulting mix of technologies in smart urban development strategies.

The importance of this study lies in several aspects. Firstly, it introduces a novel QFD decision framework tailored for smart city strategic planning. Secondly, it contributes to the operationalization of the smart city concept by providing guidance for cities to effectively adopt smart technologies. Finally, this study represents a new field of application for QFD, expanding its scope beyond its traditional domains.

The purpose of the study is to propose a framework for fleet management and make suitable distribution solution choices in the food industry.

This study reviews the literature to examine food distribution criteria. These criteria are used in the analytic hierarchy process (AHP) assessment and combined with discrete events simulation in a structured framework, which is validated through an empirical study.

The empirical case results demonstrate that both the AHP and discrete events simulation converge toward the same solution in most cases.

This study contributes to the literature on distribution management and develops a framework that can both guide future research and aid logistics practitioners in analysing distribution decision-making systems in dynamic environments.

The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.

Food manufacturing supply chain systems are the most relevant wheels of the world economy since they provide essential products supporting daily life. Nevertheless, various supply inefficiencies have been reported to compromise food safety in different regions. Sustainable supplier management and digitalization practices have become cornerstone activities in addressing these shortcomings. Therefore, this paper proposes an integrated method for sustainability management in digital manufacturing supply chain systems (DMSCS) from the food industry.

The Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) was used to weigh the criteria and subcriteria under uncertainty. Second, the Intuitionistic Fuzzy Decision-Making Trial and Evaluation Laboratory (IF-DEMATEL) was applied to determine the main DMSCS sustainability drivers whilst incorporating the expert's hesitancy. Finally, the Combined Compromise Solution (CoCoSo) was implemented to pinpoint the weaknesses hindering DMSCS sustainability. A case study from the pork supply chain was presented to validate this method.

The most important criterion for DMSCS sustainability management is “location” while “manufacturing capacity” is the most significant dispatcher.

This paper presents a novel approach integrating IF-AHP, IF-DEMATEL, and CoCoSo methods for sustainability management of DMSCS pillaring the food industry.