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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.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

The main purpose of this study is to present a new approach to managing process changes in uncertain conditions. The proposed approach is applied in one of the largest production companies in Turkey to manage the changes in their warehouse processes which formed after the merger.

In the model, interval-valued hesitant fuzzy the decision-making trial and evaluation laboratory (IVHF-DEMATEL) methodology is integrated into one of the most used BPR tools, change matrix. The main focus of the proposed model is to increase both flexibility and applicability in uncertain conditions. Thus, while the change matrix enables companies to be agile and responsive to changes, IVHF-DEMATEL provides a better way to continuously evaluate and determine critical processes, and strategies to align with evolving conditions.

Initial analysis revealed two major problems, the slowness of shipments caused by the increase in costs and the confusion in the organizational structure. However, the conventional methods fall short of effectively determination of critical objectives in terms of dealing with uncertainty. Therefore, a comprehensive roadmap for managing the change is developed with the integration of IVHF-DEMATEL and change matrix so that a successful transition is achieved.

It is believed that the study will contribute to the existing literature by providing a novel approach in which the IVHF-DEMATEL methodology is integrated into the change matrix. Also, the study provides a guideline for practical applications by presenting a step-by-step implementation of the model.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

This study aims to evaluate sustainable investment policies for housing in Qatar.

This paper proposes a new model for analyzing sustainable investment policies for housing demand in Qatar via a hybrid quantum fuzzy decision-making model. The study processed the criteria with the facial expression-based Quantum Spherical fuzzy DEMATEL and ranked the alternatives with the facial expressions-based quantum spherical fuzzy TOPSIS. Four factors were determined due to a comprehensive literature review (Environment, Housing Design, Building Design, and Surrounding the building), with five sustainable investment policy alternatives (Electricity production with renewable energies, Recycling systems and materials in construction, Transport with less carbon emission, Biodiversity for residents, and Resilience to natural disasters).

The analysis indicates that the design of the building is the most important factor (0.254), while the environment is the most influencing factor (0.253) regarding housing demand in Qatar. Transport with less carbon emission and electricity production with renewable energies are the most critical alternative investment policies.

This study provides useful insights for regulators, policymakers, and stakeholders in Qatar’s sustainable investment policies for housing demand. The main motivation of this study is that there is a need for a novel model to evaluate the sustainable investment policies for housing demand. The main reason is that existing models in the literature are criticized due to some issues. In most of these models, emotions of the experts are not taken into consideration. However, this situation has a negative impact on the appropriateness of the findings. Because of this situation, in this proposed model, facial expressions of the experts are considered. With the help of this issue, uncertainties in the decision-making process can be handled more effectively.

The purpose of this paper is to improve the productivity and quality of the wire arc additive manufacturing process by benchmarking the strategies from the selected six strategies, namely, heat treatment process, inter pass cooling process, inter pass cold rolling process, peening process, friction stir processing and oscillation process.

To overcome the lack of certainty associated with correlations and relationships in quality functional deployment, fuzzy numbers have been integrated with the quality functional deployment framework. Twenty performance measures have been identified from the literature under five groups, namely, mechanical properties, physical properties, geometrical properties, cost and material properties. Using house of quality weights are allocated to performance measures and groups, relationships are established between performance measures and strategies, and correlations are assigned between strategies. Finally, for each strategy, relative importance, score and crisp values are calculated.

Inter pass cold rolling process strategy is computed with the highest crisp value of 15.80 which is followed by peening process, heat treatment process, friction stir processing, inter pass cooling process,] and oscillation process strategy.

To the best of the authors’ knowledge, there has been no research in the literature that analyzes the strategies to improve the quality and productivity of the wire arc additive manufacturing process.

The purpose of this paper was to understand the difficulties related to asset management observed by experts in Brazilian organizations in light of the requirements outlined in the ISO 55001:2014 standard.

A survey was performed with asset management experts. The collected data were analyzed using frequency analysis, hierarchical cluster analysis and fuzzy technique for order preference by similarity to deal solution (TOPSIS).

Based on data analysis, the most critical difficulties observed were related to managing and controlling the impact of changes in the company that affect asset management objectives; to the committing to and supporting the asset management system by the top management of the organization; to manage the processes for dealing with risks and opportunities for the asset management system and plans, and correcting failures in asset performance; and to plan and conduct actions in an integrated manner to identify and minimize adverse impacts associated with the asset management system, and afterwards verifying their effectiveness.

The findings of this study have important theoretical and practical contributions, since they indicate the most critical points observed in asset management in Brazil, which can be used as a source for future research and by professionals to prioritize difficulties in future planning and develop action plans to overcome them. The step-by-step methodological approach presented in this study provides professionals and researchers with a replicable method of identifying potential asset management difficulties in a given specific reality.

The construction industry is one of the most hazardous working environments globally. Studies reveal that wearable sensing technologies (WSTs) have practical applications in construction occupational health and safety management. In the global south, the adoption of WSTs in construction has been slow with few studies investigating the critical drivers for its adoption. The purpose of this study is to investigate the factors driving WSTs adoption in Ghana where investment in such technologies can massively enhance health and safety through effective safety monitoring.

To meet the objectives of this study, research data was drawn from 210 construction professionals. Purposive sampling technique was used to select construction professionals in Ghana and data was collected with the use of well-structured questionnaires. The study adopted the fuzzy synthetic evaluation model (FSEM) to determine the significance of the critical drivers for the adoption of WSTs.

According to the findings, perceived value, technical know-how, security, top management support, competitive pressure and trading partner readiness obtained a high model index of 4.154, 4.079, 3.895, 3.953, 3.971 and 3.969, respectively, as critical drivers for WSTs adoption in Ghana. Among the three broad factors, technological factors recorded the highest index of 3.971, followed by environmental factors and organizational factors with a model index of 3.938 and 3.916, respectively.

Theoretically, findings are consistent with studies conducted in developed countries, particularly with regard to the perceived value of WSTs as a key driver in its adoption in the construction industry. This study also contributes to the subject of WSTs adoption and, in the case of emerging countries. Practically, findings from the study can be useful to technology developers in planning strategies to promote WSTs in the global south. To enhance construction health and safety in Ghana, policymakers can draw from the findings to create conducive conditions for worker acceptance of WSTs.

Studies investigating the driving factors for WSTs adoption have mainly centered on developed countries. This study addresses this subject in Ghana where studies on WSTs application in the construction process are uncommon. It also uniquely explores the critical drivers for WSTs adoption using the FSEM.

Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.