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A sustainable freight transportation system involves freight processes that are economically efficient, socially inclusive and environment friendly. For enhancing sustainability in the freight operations, mode selection is a crucial strategic decision. Therefore, the purpose of this paper is selecting the best mode, or a combination of modes based on various criteria to carry shipments from origin to destination.

This study has used an integrated grey relational analysis based intuitionistic fuzzy multi-criteria decision-making process (GRA–IFP) and fuzzy multi-objective linear programming model. Three scenarios have been developed for analyzing sensitivity of decision variables with the variations in parameters under relevant conditions. A real case of Indian third-party logistics service provider has been used to demonstrate the effectiveness of the model.

The most relevant criterion emerged out in this study for multi-mode selection problem is costs. It can be concluded from the study that multi-modal freight transportation has the potential to improve the sustainability of freight transportation by reducing the costs, damages, emissions, traffic congestion and by increasing the speed of delivering the shipment. The sensitivity analysis further shows that road is the economical mode, whereas sea and rail together are the greenest as well as socially responsible modes of transportation.

This study provides an integrated tool, which can be used by freight transporters to decide upon the sustainable modes of transportation for their various shipments.

The purpose of this paper is to quantify and evaluate transport sustainability from an integrated perspective. It is argued that transportation systems being interdisciplinary and complex, the integrated approach towards quantification and evaluation would present its sustainability goals’ attainment in a much better way.

The proposed integrated methodology is divided into five major steps that are sub-divided. It adopts a three-tier classification system i.e. pillar, sub-division and indicator. In total, 116 sustainable transportation indicators (STIs) were classified into 10 subdivisions corresponding to three sustainability pillars. The weighting and aggregation of STIs were done using a combination of a statistical tool called principal component analysis/factor analysis, a machine learning tool called fuzzy logic and equal weighting method. The final output that the proposed integrated methodology generates is a new composite index called the integrated sustainable transportation index (IIST).

To check the credibility of the proposed integrated methodology, it was applied to 26 states and the National Capital Territory (NCT) of Delhi, India. Based on the computed IIST, 27 study zones were ranked and mapped. The results showed that while Tamil Nadu (0.643), Telangana (0.574) and Maharashtra (0.564) were the best-performing states, Goa (0.347), Chhattisgarh (0.436) and Meghalaya (0.439) were the worst-performing states, as far as the relative transportation sustainability is concerned.

The proposed integrated methodology could be useful for transportation evaluators, planners and policymakers who can recognize, track and improve the strengths and overcome the weaknesses within a transportation system.

The novelty of this research work is that it uses a multi-stage multi-tool approach to quantify and evaluate transport sustainability that minimizes the demerits of single-stage individual tools while maximizing their merits.

The purpose of this study is to analyze sustainability issues in the context of logistics service providers (LSPs) and to evaluate the sustainability index of an LSP in the Indian scenario.

The case study method is used to understand the sustainability issues of an Indian LSP. The situation–actor–process (SAP) and learning–action–performance (LAP) framework has been applied to critically examine the sustainability issues of the case organization. The information about the case study is collected from both primary and secondary sources, and a detailed analysis of different sustainability issues in the context of an Indian case has been done. The sustainability index for the case organization has been also evaluated.

It can be observed from the in-depth case analysis that LSPs have started making use of green practices for long-term sustainability, but adoption is very slow in developing countries such as India. Based on SAP-LAP analysis of an Indian LSP, suitable actions are recommended for improving the performance of LSPs.

Adoption of sustainable practices by LSPs can lead to sustainable performance. Sustainability index can assist LSPs to benchmark their sustainability performance to meet the market requirements.

The novelty of this study is to analyze the issues related to implementation of sustainable practices in an Indian LSP through SAP-LAP framework. It also illustrates the evaluation of sustainability index.

The first purpose is to assess the quality of containerized multimodal export and the second is to develop and demonstrate the design of a service network with quality approach.

The article used the structural equation model to develop a model to measure the quality of multimodal transportation for containerized exports and finalized the model with an alternative approach. The evolutionary algorithm had been used to design a service network based on quality.

Provided factors affecting quality of multimodal transportation and reverse to one hypothesis, the construct variation in cost, time shape and quantity did not affect the quality of multimodal transportation for containerized exports. The model without variation construct was finalized by exploring causality.

This research had scope till container loading onto the vessel and assessed the quality for containerized cargo only, and second research purpose is limited by assumed values of fitness function and the limited number of nodes, in service network design demonstration.

This research provided a tool to measure the quality of multimodal transportation for containerized exports and demonstrated the field application of the model developed in service network design. This approach included all factors applicable across the container movement. The integrated approach of the article provided an organized method to design a service network for containerized exports.

This work provided the tool to assess the quality of multimodal transportation for containerized exports and developed an approach to design a service network of multimodal transportation based on quality. This approach has considered the factors of multimodal transportation comprehensively in contrast to the optimization approaches based on operation research techniques.

This study aims to examine the roles of board gender and cultural diversities in driving social sustainability practices through the moderating effect of board structure policies in the logistics and transportation sector.

The authors conducted fixed-effects regression with 2005–2019 data from Thomson Reuters Eikon.

The results showed that female directors are significant predictors of social sustainability across the four dimensions of human rights, workforce, product responsibility and community development. Additionally, directors with different cultural backgrounds (but not the workforce) are significant determinants of community development, human rights and product responsibility. Furthermore, although board structure policies positively moderate the relationship between board gender diversity and social sustainability, they fail to moderate the relationship between board cultural diversity and social sustainability.

The findings have crucial implications for the logistics and transportation sector's social sustainability and may help the sector align with employees' and society's expectations. The incorporation of board gender and cultural diversities into the research design was a response to calls by the European Union (EU) and the United Nations (UN) to address board configuration and stakeholders' concerns.

In the supply chain performance measurement (SCPM) there seems to be no consensus about measures for performance evaluation and suitable criteria from resilience and sustainability paradigms. In this way, this research aims to identify the attributes that a supply chain (SC) should follow to be resilient and sustainable, and then to evaluate their importance according to industry experts.

This study suggests a hybrid approach. The authors identified the most commonly used criteria using literature review, and then applied fuzzy Delphi technique (FDT) with the objective of surveying experts to find the attributes used in practice and asked to assess their relevance.

The resilient-sustainable supply chain (RSSC) is formed by four dimensions: resiliency, economic, environmental and social. A total of 15 criteria are identified, and the most important are visibility, flexibility, supply chain risk management (SCRM) culture, work conditions and communication.

This study used a literature review, so it is subject to a time frame, and the criteria could no longer be relevant as the time and business conditions change. Also, the findings may not be completely applicable throughout different industries, and therefore the finding cannot be replicated to other businesses.

This study will assist decision-makers among other interested parties to construct and/or strengthen an integrated SC that mixes resiliency and sustainability.

This study contributes to the state-of-art by producing a characterization of the resilient and sustainable supply chain for the automotive industry. Also, this research produces a new and holistic framework for resilient and sustainable SCPM supporting the decision-making process.

Staffing is one of the most influential human resource (HR) activities and is the primary method of hiring and retaining human resources. Among staffing’s several activities, recruitment and selection are one of the most crucial activities. It is possible to rehire former firm employees using the talent management strategy known as “boomerang recruitment”. The boomerang recruitment trend has tremendously grown because many employees who believe they are qualified for the position now wish to return to their old employers. According to data, boomerang employees can be 50% less expensive than conventional ways of hiring. The purpose of this study is to identify the generic critical factors that play a role in the boomerang hiring process based on the literature review. Next, the objective is to determine the relative weight of each of these factors, rank the candidates, and develop a decision-making model for boomerang recruitment.

This paper focuses on the grey-based multicriteria decision-making (MCDM) methodology for recruiting some of the best candidates out of a few who worked for the organization earlier. The grey theory yields adequate findings despite sparse data or significant factor variability. Like MCDM, the grey methods also incorporate experts' opinions for evaluation. Furthermore, sensitivity analysis is also done to show the robustness of the suggested methodology.

Seven (7) recruitment criteria for boomerang employees were identified and validated based on the opinions of industry experts. Using these recruitment criteria, three candidates emerged as the top three and created a pool out of six. In addition, this study finds that Criteria 1 (C1), the employee's past performance, is the most significant predictor among all other criteria in boomerang hiring.

Since the weights and ratings of attributes and alternatives in MCDM methods are primarily based on expert opinion, a significant difference in expert opinions (caused by differences in their knowledge and qualifications) may impact the values of the grey possibility degree. However, enough attention was taken while selecting the experts for this study regarding their expertise and subject experience.

The proposed method provides the groundwork for HR management. Managers confronted with recruiting employees who want to rejoin may use this model. According to experts, each attribute is not only generic but also crucial. In addition, because these factors apply to all sectors, they are industry-neutral.

To the best of the authors’ knowledge, this is the first study to apply a grey-based MCDM methodology to the boomerang recruitment model. This study also uses an example to explain the computational intricacies associated with such methods. The proposed system may be reproduced for boomerang recruiting in any sector because the framework is universal and replicable. Furthermore, the framework is expandable to include new criteria for different work.

A poor performance of the cold supply chain (CSC) may increase the loss of quality and potency of perishables and temperature-sensitive products that deteriorate the financial and environmental aspects of the same. The purpose of the current research work is to identify the critical performance factors (criteria) and their co-factors (sub-criteria) that are responsible for the performance measurement of CSC and suggest the best possible solutions (alternatives) to improve the same.

To achieve the objective of the research, a hierarchical model has been developed and analyzed using Analytic Hierarchy Process (AHP)-Fuzzy TOPSIS as a hybrid approach to obtain the severity weights of the identified criteria and prioritization toward their relative importance for the best possible alternatives.

Analysis reveals that the criteria “energy consumption” comes out to be the most critical criteria, and alternative “application of passive cold devices” is the most effective solution for improving the performance of CSC. Higher energy consumption leads to a higher rate of greenhouse gas (GHG) emissions increasing the global warming phenomenon, high operational cost and degradation of natural energy resources. The Application of Passive Cold Devices (PCDs) utilizes solar energy to operate the refrigeration units reducing the energy consumption, environmental and operating cost of CSC.

The research work provides several insights into the critical issues related to the CSC and suggests significant findings that enable the management and decision-makers to adopt these practices for performance evaluation and improvement of the same. The key findings of the work, such as “application of passive cold devices” and “application of IoT in cold chain logistics”, facilitate an improved platform to improve the CSC performance and proposed several directions that will enhance the merit of future research.

The presented study consolidates the various perspectives associated with CSC performance, explores the most critical criteria and proposes the best suitable cold chain practices for organizational growth. The work also provides an analytical analysis with the essence of practicalities and sensitivity analysis to support the robustness of the results. By enriching the literature and quantitative analysis of the new proposed model, this paper forms vast managerial and research implications in the field of CSC.

Halal integrity assurance is the primary objective of Halal supply chain management. Several halal-related risks are present that have the potential to breach halal integrity. Therefore, this study aims to develop the framework for the assessment of halal-related risk from a supply chain perspective.

Risk related to halal is identified through the combined approach of the systematic literature review and experts’ input. Further, these risks are assessed using the integrated approach of intuitionistic fuzzy number (IFN) and D-number based on their severity score. This integrated approach can handle fuzziness, inconsistency and incomplete information that are present in the expert’s input.

Eighteen significant risks related to halal are identified and grouped into four categories. These risks are further prioritised based on their severity score and classified as “high priority risk” or “low priority risks”. The findings of the study suggests that raw material status, processing methods, the wholesomeness of raw materials and common facilities for halal and non-halal products are more severe risks.

This study only focusses on halal-related risks and does not capture the other types of risks occurring in the supply chain. Risks related to halal supply chain management are not considered in this study. Prioritisation of the risks is based on the expert’s input which can be biased to the experts' background.

The proposed risk assessment framework is beneficial for risk managers to assess the halal related risks and develop their mitigation strategies accordingly. Furthermore, the prioritisation of the risks also assists managers in the optimal utilisation of resources to mitigate high-priority risks.

This study provides significant risks related to halal integrity, therefore helping in a better understanding of the halal supply chain. To the best of the authors' knowledge, this is the first comprehensive study for developing a risk assessment model for the halal supply chain.

In past years, the global supply chain has witnessed devastating effects of coronavirus (COVID-19) disease. However, the COVID-19 pandemic has renewed the interest of the Sustainable Supply Chain (SSC) stakeholders on sustainability. The stakeholders are now rethinking their business processes and strategy to make them sustainable. In this context, the relevant literature is required to support emerging markets to formulate sustainability-focussed strategies. The purpose of this study is to provide a comprehensive analysis of potential antecedents that leads towards sustainable development of freight transportation in emerging markets.

Initially, the antecedents of the Sustainable Freight Transport (SFT) system are derived from the literature survey followed by verification from the experts. Then, the potential antecedents are categorized under four (social, organizational, operational and environmental) broad categories. Afterwards, a Neutrosophic Analytic Network Process (N-ANP) method is employed to obtain the priority weights of the identified potential antecedents.

The paper identified and ranked 17 antecedents of the SFT system. According to the study’s findings, the top three antecedents of SFT are “the presence of a multimodal transportation system,” “circularity in SFT” and “traffic congestion management”. The results from the study advocate the promotion of existing multi-modal transport facilities which is promising to achieve sustainability. The results suggested the adoption of the digital twin to manage the transport operations.

This study sheds light on how to achieve sustainability in the freight transportation system post-COVID era highlighting the potential antecedents. The study’s findings will assist practitioners in developing SFT strategies in the face of such pandemics in future.