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Although green logistics has become a new focus of cooperation between government and enterprises under environmental constraints, how local governments formulate subsidy policies to effectively guide the green transformation of regional logistics and how to facilitate the reasonable cost-sharing are rather critical. This paper will deeply explore the dynamic process of the tripartite participation (government, platform, and logistics enterprises) in the selection of regional green logistics strategy, and reveal the evolutionary game relationship of the three parties.

To explore the dynamics involving the government, platform and logistics enterprises for the green logistic transformation, and reveal the evolutionary gaming among the three parties, based on the bounded rationality premise, this study constructs the tripartite asymmetric evolutionary game models, uses the stability theorem of differential equation to explore the evolution and stability strategy of the system in different cases and explicates the paths of influence on the tripartite behaviors via simulations.

Results of this study indicate that there exist stable equilibrium strategies among the three parties regarding the regional green logistics, and they are affected by different factors. The government's subsidy, subsidy intensity and the platform's cost-sharing proportion can generate positive effects, but the latter two can also impact negatively beyond the effective ranges. The findings provide a theoretical basis for local governments, platforms and logistics enterprises to formulate justifiable subsidy intensity and determine reasonable sharing proportion.

Firstly, considering the significant relevance of local government, it is included in the evolution model, and the tripartite game (among government, platform and enterprises) is explored; Secondly, by comparing the equilibrium results under different game conditions, this paper analyzes the evolution of each party's game strategy to achieve the optimal return under bounded rationality and the important factors determining the strategic selection; Finally, the key factor of platform cost sharing is involved, and to what extent the change of platform cost sharing ratio will influence the systematic stability is explored.

The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper aims to discuss the aforementioned statement.

A Markov-optimised mean GM (1, 1) model is proposed to forecast the demand for the cold chain logistics of agricultural products. The mean GM (1, 1) model was used to forecast the demand trend, and the Markov chain model was used for optimisation. Considering Guangxi province as an example, the feasibility and effectiveness of the proposed method were verified, and relevant suggestions are made.

Compared with other models, the Markov-optimised mean GM (1, 1) model can more effectively forecast the demand for the cold chain logistics of agricultural products, is closer to the actual value and has better accuracy and minor error. It shows that the demand forecast can provide specific suggestions and theoretical support for the development of cold chain logistics.

This study evaluated the development trend of the cold chain logistics of agricultural products based on the research horizon of demand forecasting for cold chain logistics. A Markov-optimised mean GM (1, 1) model is proposed to overcome the problem of poor prediction for series with considerable fluctuation in the modelling process, and improve the prediction accuracy. It finds a breakthrough to promote the development of cold chain logistics through empirical analysis, and give relevant suggestions based on the obtained results.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

From a supply chain perspective, logistics firms collaborate with other supply chain members to extend their business scope. Investment in circular economy projects in the supply chain can not only broaden the scope of business but also increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects. How to coordinate multiple circular economy supply chain projects is at the core of its operation.

This paper first analyzes some typical supply chain projects in China and summarizes the main features of these projects. Secondly, considering the benefits of the project and the stakes of each project, a multi-stage stochastic programming model is established. Finally, Cplex, nested decomposition, LocalSolver and other methods are adopted to simulate and analyze the model.

The final experimental results find that the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

There are still some limitations to this study; for example, it is limited to the analysis of circular economy supply chain projects in China. The study focused on third-party logistics companies, and other enterprises in the circular economy supply chain were not considered. The research also assumed that the benefits of each circular economy project and the stakes of each project were known, which may not always be the case in real-world scenarios.

This manuscript found that investing in other circular economy projects in the supply chain can broaden the scope of business and increase the value of the entire supply chain. Third-party logistics companies are gradually participating in the construction and operation of many circular economy projects, such as recycling and repurposing initiatives. It highlights the importance of coordinating multiple circular economy supply chain projects to increase the value of the entire supply chain. The multi-stage stochastic programming model presented in this research can provide a useful tool for logistics enterprises and third-party logistics companies to optimize their investment decisions and maximize their profits in the context of a circular economy.

As the saying goes, “food is heaven of people” the development of agriculture is not only related to the quality of human life but also profoundly affects the efficiency of economic operation. However, under the background of rapid economic and technological development, China’s agricultural modernization process is very slow and incompatible with the development trend of economic modernization. Therefore, it is particularly necessary to innovate the traditional agricultural development model to break through the bottlenecks encountered in the transformation process of agricultural modernization. The proposal of the agricultural logistics ecosphere is to solve the problems of poor industrial linkage, low technical level and backward operation mode in the development process of traditional agriculture, and it is an important starting point for agricultural supply-side structural reform. This study aims to answer three questions in three aspects, namely, what is the agricultural logistics ecosphere, analyzes the general composition of the agricultural logistics ecosphere and how the subjects of the agricultural logistics ecosphere cooperate. It also puts forward suggestions for the coordinated development of the agricultural logistics ecosphere under the leadership of Taoxin. Also, it inspires the transformation of the agricultural development model in other regions and countries.

This study adopts a case study and a qualitative analysis method to collate the first-hand and secondary data obtained from the interview and form a tertiary coding. It explores the mechanism of coordinated development of the agricultural logistics ecosphere from four levels: goal synergy, resource synergy, ability synergy and benefit synergy.

First, the formation of the agricultural logistics ecosphere will not be achieved overnight, as operational management matures and ecosphere experiences the germination-development-mature evolution process. Second, target synergy is the foundation and premise for the formation of agricultural logistics ecosphere: to attract external resources with Taoxin characteristic core resources, to attract external resources to achieve synergy, to provide a guarantee for the realization of the maximum value of the agricultural logistics ecosphere and then to realize the interest synergy of internal and external subjects. Third, driving agricultural products upward in the “10 + 1” model: it is the core resource of the agricultural logistics ecosphere led by Taoxin and attracts external subjects to gather with core resources to further improve the value of the ecosphere. The improvement of the value of the ecosphere can broaden the ecosphere network. This is both an important construction of the ecosphere, and it is also the path choice of the coordinated development of the agricultural logistics ecosphere.

This study has a very important practical value and theoretical significance. In practical terms, the integration of “primary, secondary and tertiary industries” is helpful to foster new momentum for the development of agriculture and rural areas and promote the general framework of the agricultural logistics economy, the analysis of the general mechanism and extension of the transition of the new economy.

Each individual needs to be vaccinated to control the spread of the COVID-19 pandemic in the shortest possible time. However, the vaccine distribution with an already strained supply chain in low- and middle-income countries (LMICs) will not be effective enough to vaccinate all the population in stipulated time. The purpose of this paper is to show that there is a need to revolutionize the vaccine supply chain (VSC) by overcoming the challenges of sustainable vaccine distribution.

An integrated lean, agile and green (LAG) framework is proposed to overcome the challenges of the sustainable vaccine supply chain (SVSC). A hybrid best worst method (BWM)–Measurement of Alternatives and Ranking According to COmpromise Solution (MARCOS) methodology is designed to analyze the challenges and solutions.

The analysis shows that vaccine wastage is the most critical challenge for SVSC, and the coordination among stakeholders is the most significant solution followed by effective management support.

The result of the analysis can help the health care organizations (HCOs) to manage the VSC. The effective vaccination in stipulated time will help control the further spread of the virus, which will result in the normalcy of business and availability of livelihood for millions of people.

To the best of the author's knowledge, this is the first study to explore sustainability in VSC by considering the environmental and social impact of vaccination. The LAG-based framework is also a new approach in VSC to find the solution for existing challenges.

Halal logistics play a crucial role in maintaining a product’s “halal integrity” from farm to fork. These logistics operations are exposed to risks during transportation, storage and handling, that negatively affect the halal supply chain’s operational efficiency and integrity. Therefore, the main purpose of this research is to propose a risk assessment framework for halal logistics.

Initially, the risks associated with halal logistics are identified through an extensive literature review, and then the significant ones are validated with the expert’s input. After that, an appropriate multicriteria decision-making method named best worst method (BWM) is used to prioritise the identified risks. Finally, the robustness of the obtained ranking is assessed with sensitivity analysis.

This research identifies 26 halal logistics risks and then categorises these risks into 6 significant dimensions and proposes a framework. The result obtained from the BWM analysis shows that organisational, transportation and design risks are the primary risks present in halal logistics. Among the halal logistics risks, partnership risk, risk of contamination during transportation and risks of unskilled workers are the top three halal logistics risks that need to be addressed on a priority basis for maintaining halal integrity. Furthermore, this study also suggests some valuable suggestions for mitigating these risks that the logistics providers could use.

The proposed framework is helpful for the assessment of risks associated with halal logistics. The prioritisation of the identified risks supports the managers in formulating mitigation strategies that might reduce the effect of these associated risks in logistics operations.

This study focusses on halal logistics, and to the best of authors’ knowledge, this may be the first attempt to systematically examine the risks related to halal logistics.

Manufacturing in Mauritius is mostly export-oriented. Any supply chain (SC) failure or resilience deficit may result in cancellation of orders and loss of customers, market share and revenue and reduce capability to compete globally. Addressing this challenge is complex, although digital technologies and artificial intelligence (AI) models can improve resilience by assisting decision-making and mitigate risks, thus infusing greater predictability across the SC.

Supply chains are facing increasing disruptions and uncertainties owing to extreme weather events, the war in Ukraine, market volatility and the ongoing COVID-19 pandemic, among other factors. Manufacturing industries and their supply chains essentially create thousands of jobs that enable economic growth and sustain export capability. In addition, they need to maintain or increase both productivity and efficiency and recover quickly from unforeseen or unexpected challenges – that is they need to be resilient. Transformation initiatives, whether in production or supply chain management (SCM), are never easy. Process changes not supported by data or hurried human decisions can sometimes have unintended consequences, mainly adverse. However, in times of greater uncertainty (war and pandemic), setbacks can have greater consequences on the business. Manufacturers are already apprehensive and report slowing exports as recession concerns have caused consumers and businesses to pull back on spending. There is therefore a need to reduce uncertainty and augment resilience by unlocking and synthesising insights that emanate from the power of data analytics, AI and machine learning to improve the resilience efficiency balance.

This chapter will discuss the opportunities arising from the adoption and implementation of digital technologies and AI in SCM, leading to better value creation, less greenhouse gas emissions and resilience. The hurdles that enterprises are facing to integrate AI in their logistics and SCs will also be highlighted. This work comments on initiatives that uphold the objectives of SDG 8 – decent work and economic growth, SDG 9 – industry, innovation & infrastructure and SDG 13 – climate action.

This paper breaks through the limitations of the research on bullwhip effect in the traditional supply chain, extends the research perspective to digital supply chain and discusses the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two.

This paper starts with the weakening mechanism of supply chain digitization on bullwhip effect, builds bullwhip effect models of traditional supply chain and digital supply chain, respectively, simulates the influence of supply chain digitization transformation on bullwhip effect by using Matlab software and analyzes the causes of bullwhip effect in supply chain led by T company and the digitization process.

Firstly, digitization can reduce bullwhip effect in multi-level supply chain by reducing information feedback deviation. Second, digital transformation is conducive to improving the overall performance of the supply chain. Third, government incentives can promote the digital transformation of supply chain and inhibit bullwhip effect.

Although the study considers the heterogeneous subject -- the government's incentive effect on digital transformation and information sharing – it does not include the influence of the end node in the supply chain, that is the consumer. In addition, this paper only analyzes and discusses the bullwhip effect on the amplification of demand, without considering the situation that the market contraction will lead to the reduction of demand.

This paper considers the distortion degree and delay degree of information feedback, carries out quantitative analysis of bullwhip effect, builds the bullwhip effect model of traditional supply chain and digital supply chain, uses Matlab software to analyze the difference of the influence of supply chain digital transformation on bullwhip effect suppression and puts forward the corresponding control strategy.

The research shows that digital transformation can reduce the bullwhip effect in multi-layer supply chain by reducing the information feedback deviation, which is conducive to improving the overall supply chain performance, and government support can accelerate the digital transformation of supply chain to a certain extent.

First, break through the limitations of traditional supply chain research, expand the research perspective to digital supply chain and discuss the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two. Second, quantify the bullwhip effect through information feedback bias and provide an analysis method for the weakening of the bullwhip effect. Third, the driving role of the government in the digital transformation of the supply chain is considered in the study, so that the model is more close to the actual situation of enterprise operation.