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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 purpose of this study is to investigate and analyze the knowledge management (KM) model of Nonaka and Takeuchi, focusing on institutional and infrastructure factors in a specific field of design offices in a product-oriented organization. In other words, this research does not intend to recreate the model of Nonaka and Takeuchi, but seeks to expand and modify this model according to the specific context and institutional and infrastructure factors that may specifically affect the effectiveness of the model.

This study used two distinct questionnaires, administered to the same set of respondents, to comprehensively address different dimensions of KM. The first questionnaire, focused on KM components, assessed aspects such as knowledge creation, sharing and utilization. The second questionnaire evaluated institutional and infrastructural factors critical to KM, covering dimensions like organizational culture, values, leadership, context, hardware, software and network systems. This dual-questionnaire approach is justified, as it allows for a detailed and differentiated analysis: one tool captures the operational aspects of KM, while the other explores the supporting infrastructure. This methodology ensures that the study accurately measures both the effectiveness of KM practices and the adequacy of the supporting environment, thus providing a robust assessment of the KM system.

This study identifies seven key factors influencing KM processes: organizational culture, values, leadership, context, hardware, software and network systems. These factors shape how knowledge is created, shared and used, and support proposed modifications to the Nonaka and Takeuchi KM model. Using the TOPSIS method, this study found that organizational context, culture and values rank above average, while KM policies are moderate, and information technology is below average in the design offices of a product-oriented organization in Tehran. Further research in different sectors could help validate these findings.

This study introduces a novel enhancement to the Nonaka and Takeuchi KM model by incorporating a comprehensive analysis of institutional and infrastructural factors. Unlike existing models, which primarily focus on generalized KM principles, this research uniquely integrates specific factors such as organizational culture, leadership and technological infrastructure. The originality of this work lies in its tailored approach for product-oriented organizations, offering a more precise and actionable framework for improving KM practices. This advancement not only deepens theoretical insights but also provides practical value by addressing the specific needs and dynamics of the target organizations.

The uncertain supply chain network design (SCND) problem, considering suppliers’ environmental, social and governance (ESG) ratings, has been infrequently addressed in the literature. Looking at the importance of ESG ratings in achieving supply chain sustainability, this study aims to fill the gap by incorporating supplier ESG factors into SCND within an uncertain environment.

This paper presents a multi-period, multi product SCND model that integrates ESG factors and accounts for uncertainties in supply and production capacities. The model seeks to minimize total operational costs by determining the optimal selection of plant and warehouse locations across multiple time periods. Uncertainties in supply and production capacities are managed through a chance-constrained programming approach with right-hand side stochasticity. A Lagrangian relaxation-based heuristic method is applied to address the NP-hard nature of the problem.

The efficacy of the proposed model is illustrated through a numerical example, demonstrating its capability to optimize material flows across the supply chain under uncertain conditions. The model simultaneously considers economic and ESG factors in procurement decisions. A sensitivity analysis is conducted to examine different operational scenarios and their implications on the model’s outcomes.

To the best of the authors’ knowledge, this study is one of the first to integrate ESG factors into SCND under uncertainty. The proposed model provides a robust framework for decision-makers to optimize supply chain operations while considering both economic and ESG objectives in an uncertain environment.

This study aims to review the stages of the traditional disaster timeline, propose an extended version of this timeline and discuss the disaster strategies relevant to the different stages of the extended timeline.

An extensive review of the existing literature was made to discuss the need for an extended version of the conventional disaster timeline and to explain the differences between the various disaster management strategies. The research approach was based on theoretical and practical reasoning underpinned by the literature.

The proposed extended disaster timeline allows better allocation of a wider range of management strategies. Successful disaster management depends on prioritisation of efforts and the use of the right strategy(s) at the right time: before, during and after an incident.

This study provides a better conceptualisation of the disaster stages and corresponding strategies. It clarifies the role of each strategy, thus linking it more effectively with the disaster timeline. Subsequently, this study is expected to improve decision-making associated with the disaster management process. In the end, it is expected to help transforming the conventional disaster timeline into a more practical one that is result-oriented more than only being a conceptual model.

Disaster management strategies are used interchangeably very often in the literature. A few attempts were made to capture multiple strategies in one study to demonstrate what constitutes effective disaster management without mixing irrelevant strategies with the different disaster stages.