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Critical infrastructure (CI) plays an essential role in reading, reacting and responding while dealing with natural disasters. This study address food supply chain resilience by proposing an FSC resilience model that explains the food product and transport flow via production, processing, distribution and retailing in circumstances of (CI) collapses post a natural disaster.

A combination of qualitative methods was conducted to obtain a comprehensive overview of the food and beverage sector in Puerto Rico. The full dataset comprised of seven focus groups for a total of 52 participants and 12 in-depth interviews.

FSC resilience is seen in this study through the managerial actions taken by members of the Chain: innovating, transforming, adapting, and flexibilising business models and operations.

This study is the first to address FSC resilience from the perspective of net food importer economy in the context of natural disasters and prolonged Critical infrastructure (CI) breakdown, and the first one in proposing an FSC resilience model that explains the food product and transport flow via production, processing, distribution and retailing in circumstances of CI collapses post a natural disaster.

Food supply chain (FSC) in Greece is dominated by small and medium-sized enterprises (SMEs), who face several challenges in adopting green practices. The purpose of this paper is to identify the key drivers and barriers influencing the environmental performance of SMEs within the Greek dairy supply chain (SC).

Descriptive research methodology attempts to prioritize the drivers and barriers for improving the environmental sustainability performance. Analytical hierarchy process and sensitivity analysis are used to understand the complex nature of the influencing factors.

The analysis identifies five barriers and six drivers for the implementation of green practices within the dairy SC. While external drivers significantly influence the market structure and logistics network, government, competitors and customers are the driving factors for improving environmental performance.

The study contributes to filling the literature gap on key factors influencing the implementation of green practices within the FSC. The identified influential factors will contribute toward building a framework for improving sustainability performance within the Greek dairy SC.

The study is expected to benefit the Greek and European SMEs by driving their environmental practices within the perishable SC network.

The paper provides directions for researchers, practitioners and policy makers in understanding the challenges for implementing green practices in the dairy SC. The holistic approach followed in this paper is a building block for a conceptual framework on implementing environmental sustainability within the FSC. Apart from contributing to the current literature by extending the research horizon to SMEs’ green adoption capability, this study also provides better understanding of the pivotal role of internal and external key factors in influencing sustainability performance.

The article presents the phenomenon of hazards related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the food supply chain (FSC) by identifying possible stakeholders of a seafood company who might be influenced by the hazards.

A case study analysis was conducted with a review of the organization's documentation alongside a semi-structured interview and an impact effort matrix.

Seven out of 18 stakeholders had to strongly engage in minimizing the effects of hazards related to SARS-CoV-2. The most important areas of cooperation regarded safety were identified. Both external and internal documents and reports regarding the minimizing of negative effects of hazards related to SARS-CoV-2 were required by institutional clients, official authorities and the studied organization itself. The proper identification of stakeholders and up-to-date knowledge about them allowed the organization to react faster and protect the FSC.

The authors’ research was based on qualitative methods, so it lacked a diagnostic survey, along with similar studies for comparison of results and approaches.

The surveyed company may be a good benchmark for others to follow when choosing the appropriate approach in the field of stakeholder analysis for addressing new emerging risks.

The findings are important, timely and original, and they focus on a subject rarely studied in the literature. The information from the paper applies to numerous groups of food companies.

This paper aims to find the best tools to influence the improvement of sustainability in food supply chains (FSCs) by conducting a systematic review of articles. The reader will learn how the different industry 4.0 tools (I4.0T) benefit the FSC and the limitations of each tool.

A review of 436 articles published during the period 2019 to 2022 referenced in the Scopus and Web of Science databases was performed. The review was limited to articles published in English and directly related to Industry 4.0, circular economy and sustainability in the food supply chain.

The results show different contributions of I4.0, with some being more influential than others in improving sustainability in FSCs; for example, Internet of Things and Blockchain have been shown to contribute more toward transparency, traceability, process optimization and waste reduction.

The paper's contribution consisted of ranking according to their importance and the I4.0T that affect sustainability in FSCs by classifying the aspects of each tool and the sustainability factors through a categorization by the Analysis Hierarchy Process.

This study explains Indonesian consumers' choice of poultry meat attributes and the willingness to pay (WTP) for these attributes using a discrete choice experiment.

The survey was conducted for the traditional and modern channels and involved a sample of 440 respondents in the Greater Jakarta area. A discrete choice experiment was employed as the study framework and in designing the questionnaire. A multinomial logistic regression analysis was used to evaluate consumers' preference for poultry attributes in modern and traditional channels.

Consumers preferred warm poultry meat, government certification and product information labeling on poultry meat. The WTP for warm poultry meat was the highest, which is indicating that freshness is crucial for consumers to ensure quality. Moreover, consumers had more trust in government certification than private certification for food safety and were willing to pay more for product information labeling on poultry meat.

The government can use the model as a decision support to improve poultry meat quality at sales channels in Indonesia including to close sales channels where sick poultry are sold and thus address food safety concerns caused by the avian influenza outbreak.

This study shows that understanding the WTP for poultry meat attributes enables the government to control the poultry sales channels and stimulates producers to supply the market with a safer poultry meat quality using the price mechanism.

This study advocates the importance of taking an evolutionary perspective in the strategic configuration of closed-loop supply chains (CLSC) in the transition to a circular economy. Building on the supply chain management and industrial dynamics research domains, an evolutionary analytical framework was developed and applied in the empirical context of the ongoing industrial transition to e-mobility.

This study is designed as an in-depth exploratory case study to capture the multi-layer dynamic complexities and their interplay in CSLC development. The empirical investigation was based on two-year interactions between the authors and various departments in a leading European heavy vehicle manufacturer. The proposed evolutionary analytical framework was used for investigating the dynamics of four CLSC configurations through ten possible trajectories.

The findings demonstrate that the evolution of each CLSC configuration comes with multiple challenges and requirements and point out the necessity for the co-development of technologies, product design and production, and infrastructure through long-term relationships among key supply chain actors. However, this evolutionary journey is associated with multiple dilemmas caused by uncertainties in the market and technology developments. All these factors were properly captured and critically analyzed, along with their interactions, thanks to the constructs included in the proposed evolutionary analytical framework.

The proposed evolutionary framework is applicable for examination of SC transformation in the context of market and technology development, and is particularly relevant for transitioning from linear SC to CLSC. The framework offers a single actor perspective, as it does not directly tackle dynamics and effects of actions taken by SC actors.

The developed framework can support SC managers in identifying, framing, and comparing alternative strategies for CLSC configuration in the transition process.

This study proposes the framework for understanding and guiding the evolutionary process of CLSC development. Its uniqueness lies in the integration of concepts from innovation and evolutionary theories coming from industrial dynamics and SCM literature streams.

The purpose of the food traceability is used to retain the good quality of raw material supply, diminish the loss and reduced system complexity.

The proposed hybrid algorithm is for food traceability to make accurate predictions and enhanced period data. The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers.

In order to survive with the existing financial circumstances and the development of global food supply chain, the authors propose efficient food traceability techniques using the internet of things and obtain a solution for data prediction.

The operation of the internet of things is addressed to track and trace the food quality to check the data acquired from manufacturers and consumers. The experimental analysis depicts that proposed algorithm has high accuracy rate, less execution time and error rate.

The purpose of this paper is the need to build a novel approach that would allow flexible modeling and solving of food supply chain management (FSCM) problems. The models developed would use the data (data-driven modeling) as early as possible at the modeling phase, which would lead to a better and more realistic representation of the problems being modeled.

An essential feature of the presented approach is its declarativeness. The use of a declarative approach that additionally includes constraint satisfaction problems and provides an opportunity of fast and easy modeling of constrains different in type and character. Implementation of the proposed approach was performed with the use of an original hybrid method in which constraint logic programming (CLP) and mathematical programming (MP) are integrated and transformation of a model is used as a presolving technique.

The proposed constraint-driven approach has proved to be extremely flexible and efficient. The findings obtained during part of experiments dedicated to efficiency were very interesting. The use of the constraint-driven approach has enabled finding a solution depending on the instance data up to 1,000 times faster than using the MP.

Due to the limited use of exact methods for NP-hard problems, the future study should be to integrate the CLP with environments other than the MP. It is also possible, e.g., with metaheuristics like genetic algorithms, ant colony optimization, etc.

There is a possibility of using the approach as a basis to build a decision support system for FSCM, simple integration with databases, enterprise resource planning systems, management information systems, etc.

The new constraint-driven approach to FSCM has been proposed. The proposed approach is an extension of the hybrid approach. Also, a new decision-making model of distribution and logistics for the food supply chain is built. A presolving technique for this model has been presented.

The purpose of this paper is to implementing environmental education concepts to manage environmental health impacts by letting the people in the community learn through the participatory learning process.

The participatory action research (PAR) and the environmental education concept were conducted in managing the learning process for the people in Map Kha Sub-district, Rayong province. The purposive sampling technique and the stakeholder analysis were used to derive the informants of this study. They are those people living or working in the Mab Kha Sub-district area at least 2 years, aged more than 20 years old, and were willing to participate in all steps of the study. A total of 30 informants were divided into three groups as follows: The Key Informants, The Casual Informants and The General Informants. Data collection employed workshops with three techniques – Participatory Rural Appraisal (PRA), Future Search Conference (FSC) and Appreciation Influence Control (AIC) – to organize cooperative learning processes to managing environmental health impacts. The Content Analysis was utilized in this study through the categorization, grouping, analysis, interpretation and systemization of data. The study period was from June 2014 to December 2015.

The study found that most people are concerned and aware of the environmental pollution problems affecting the health in the areas and need to solve such problems. They are also prepared for various problems. However, they face the problems on that they still lack of the chance to be informed about the information on the pollution problems and lack of the chance to participate. For the participatory learning process used to manage the environmental health impacts in the industrial community, it is found that the people and the leading network partners perform well through the knowledge building process on the environmental pollution problems and the planning and evaluation lead to desired behavior of people and industrial community to manage environmental health impacts.

The study results emphasized that the participatory learning process of the network associates was the critical key in forming the community power to manage the environmental health impacts. Therefore, the learning process should be easy, not complicated, take a short time and be friendly that will make the community understand the problem and help protect the environment systematically.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.