The ventilating crisis of Corporate Social Responsibility (CSR) issues in the textiles industry resulted in the engagement of many researchers in the analysis of CSR and its related factors throughout the globe. Some researchers in developed nations extend their policies beyond the boundary of CSR in the textiles industry, but some developing contexts are still limited to this boundary, including India, due to facts that have not yet been revealed. Hence, to fill this gap, this study reveals the factors that are resisting the implementation of CSR in the textiles industry with the assistance of a proposed model, and this model is validated with a case industry situated in southern India. Common barriers are collected through various reliable means, and among those common barriers, the essential barrier was identified with the assistance of the case industrial manager through an Analytical Hierarchy Process (AHP) under a fuzzy environment. The results show that financial constraints comprise the main barrier to the implementation of CSR in the Indian textiles industry, which is then further validated with feedback from case industry managers. This study offers both societal and scientific insights, identifies limitations, and provides an approach that may be extended in the future once additional factors are implemented.

The “agricultural superdocking” mode which has been strong supported by the government has become the main way for fresh agricultural products to enter the market in China. Based on the analysis of fresh agricultural products supply chain inventory management under the “agricultural superdocking” mode, this paper constructs an integrated inventory model for fresh agricultural products of “farmers’ professional cooperatives + distribution centers + supermarkets.” Considering multiple members at each echelon of a supply chain, a model that maximizes the overall profit of the supply chain is proposed. The model assumes that the market demand of fresh agricultural products is affected by freshness and sales prices, and the distribution center is responsible for not only storage, processing, and distribution but also coordinating the production and supply information of farmers’ professional cooperatives and the order sales information of supermarkets. An improved genetic algorithm is developed to solve the nonlinear optimization problem. Results of a case study show that the optimal supply and replenishment strategy under the given supply chain distribution process are obtained.

Grain supply chain transportation problem is a nontrivial and intractable issue for many developing countries. Grain as a bulk commodity is usually transported by bulk carriers. By taking into account the special condition of Ukraine, we proposed the containerized grain supply chain transportation optimization problem. In this problem, the sustainable supply chain system delivers grains in containers among primary elevators, intermodal yards, and port container terminals. Then, a containerized grain transportation model was developed to minimize the total cost of the sustainable supply chain system. Specifically, 20-foot containers were proven as more reasonable to be used in this paper. We also considered different transportation tools: trucks that can load one 20-foot container, trailers that can load two 20-foot containers, and wagons that can hold two 20-foot containers. Additionally, a disruption model was proposed by considering different disruption scenarios. Based on an analysis of the simulation results, some cost minimization strategies were proposed. Finally, a sensitivity analysis that aimed to analyze the effect of the proposed strategies on the minimal total cost and sustainability of the supply chain was conducted. The main conclusions drawn from the simulation are that the established food supply chain model is meaningful and accurate, and the incorporation of the disruption model aligns with practical requirements. Additionally, an increase in intermodal yard capacity, truck park size, and wagon park size decreases the total cost of the supply chain. The first two have a positive effect on the sustainability of the supply chain, while the latter increases the disruption risk of the supply chain.

Multimodal transport document collaboration is the foundation of multimodal transport operations. Blockchain technology can effectively address issues such as a lack of trust and difficulties in information sharing in current multimodal transport document collaboration. However, in current research on blockchain-based electronic documents, the bottleneck lies in the collaboration aspect of multimodal transport among multiple entities, known as the “one-bill coverage system” collaborative problem. The collaboration problem studied in this paper involves selecting suitable transport routes according to the shipper’s transport needs, and selecting the most suitable specific carrier from numerous carriers. To address the collaboration problem among multiple parties in the multimodal transport “one-bill coverage system”, a multiparty collaboration mechanism is designed. This mechanism includes two aspects: firstly, designing the architecture of the multimodal transport blockchain transport platform, which reengineers the operation process of the “one-bill coverage system” for container multimodal transport; secondly, constructing a multiparty collaboration decision-making model for the “one-bill coverage system” in multimodal transport. The model is solved and analyzed, and the collaboration strategy obtained is embedded in the application layer of the platform. Smart contracts related to the “one-bill coverage system” for multimodal transport are written in the Solidity language and deployed and executed on the Remix platform. The design of this mechanism can effectively improve the collaboration efficiency of participants in the “one-bill coverage system” for multimodal transport.

This study deals with berth allocation for Maritime Autonomous Surface Ships (MASSs) in the context of the mixed operation of MASSs and manned vessels from the perspective of port-shipping companies’ collaboration. Two berth allocation strategies, namely the separated-type and the mixed-type, are proposed in this article. Two mixed integer nonlinear programming models aimed at minimizing the total docking cost of the vessels in the port and the waiting time for berths are developed and solved using Gurobi, respectively. A large-scale simulation of the mixed-type berth allocation model is carried out using an improved simulated annealing algorithm. Several experiments are conducted to test the effectiveness of the model and to draw insights for commercializing autonomous vessels. The presented results show that multi-objective modeling and optimization should be conducted from the collaboration of port-shipping companies, which is more efficient from the perspective of shipping companies or ports, respectively. When berth resources are limited or there is a high requirement for operational safety, the separated-type berth allocation strategy is more efficient. When the number of MASS-dedicated berths reaches a certain proportion, the total docking cost of the vessel no longer changes, indicating that more dedicated berths are not better.

The seriousness of vessel air pollution has forced the International Maritime Organization (IMO) to introduce a series of relevant laws and regulations. This paper proposes a monitoring scheme based on the synergistic operation of motherships and UAVs. This scheme innovatively adopts a harbor sea patrol vessel or the other official vessel (mothership) as the mobile power supply base for UAVs and realizes efficient and accurate monitoring of vessel air pollution in the pre-monitored area at sea by carrying multiple UAVs. The focus of this paper is on the path optimization problem for multi-UAV collaboration with mothership (MUCWM) monitoring, where the objective is to minimize the total monitoring time for MUCWM. The following three main aspects are studied in this paper: (1) multi-UAV monitoring path optimization; (2) the collaboration mechanism between the mothership and multiple UAVs; and (3) mothership traveling path optimization. In order to effectively solve the above problems, this thesis constructs a path optimization model for multi-UAV collaborative mothership monitoring of air pollution from vessels in port waters; solves the model using the improved adaptive differential evolution (IADE) algorithm; and verifies the effectiveness of the model and the algorithm by using the position data in the Automatic Identification System (AIS) of vessels in Ningbo Zhoushan Port. Through the performance comparison and sensitivity analysis of the algorithm, it is confirmed that the algorithm can effectively solve the path planning problem of the collaborative operation between the mothership and multiple UAVs. The research results in this paper not only help to reduce the air pollution level of harbor vessels and improve the efficiency of sea cruising but also play an important supporting role in the enforcement of relevant emission regulations.