Adopting digital technologies in a business can help with sustainable supply chain management. These technologies can make e-commerce development faster and empower the emergence of B2B e-commerce businesses. In this study, our focus was to develop a framework for an Internet of things (IoT) embedded sustainable supply chain to deliver textile items using a B2B e-commerce business model. We formulated a mixed-integer non-linear programming (MINLP) model to minimize the total supply chain cost, including the B2B orders’ packaging, handling, and transportation, with carbon emission taxation. Furthermore, the purchasing cost of the RFID tags and IoT facilities that were provided on the transport vehicles was high. The proposed model was solved by using the global solver in the LINGO software package and finding the optimized value of the total supply chain network cost. We tested the proposed model in different case scenarios, i.e., small- to significant-sized problems. Then, a sensitivity analysis was performed to observe the variations in the overall cost of the supply chain network when there were changes in the main parameters of the proposed model. The results of the models showed that models can be helpful for efficient logistics planning and supply chain design.

The continuous decline in the sustainable agro-food supply chain (AFSC) towards a Circular Economy (CE) has become a matter of great concern for the key stakeholders, including government organizations, businesses, end-users, and farmers. In line with this, the main purpose of this study is to develop a sustainable food Supply Chain Network (SCN). The SCN enables the collection of agro-food grains from different farmers’ locations and delivers the same to food processing units. To design an efficient and effective sustainable pickup and delivery network, a Mixed-Integer Non-Linear Programming (MINLP) mathematical model is formulated. The proposed model achieves the sustainability goal by minimizing the collection costs. The developed MINLP model is solved by using an exact optimization approach in LINGO 18 software. Further, to test the efficacy of the developed model, various computational experiments are performed, varying from small to large size data. The results of these experiments reflect that our model can support businesses in designing an efficient and effective sustainable pickup and delivery network. Lastly, it has been shown that innovative packaging materials can help to minimize the wastage of food.

In recent years, with the globe facing a significant sustainability challenge due to emissions from various transport systems, numerous transit agencies throughout the world have begun to replace their conventional fleet with electric. Transitioning from diesel to electric buses is a complex process that necessitates extensive planning to ensure a smooth transition. To reduce initial financial costs, it is necessary to select the best battery size and number of chargers with their optimal location. So that any infeasibility of operation owing to electric buses' range, charger availability, and total fleet constraint can be eliminated. So, this paper addresses the facility location problem for charging stations and optimize the battery size while considering the diesel bus schedule as input to solve the problem. The suggested framework combines current diesel schedules, such as trip arrival time, departure time, distance travelled, layover periods, and potential charging location, to develop an optimization model that finds the best location to installed charging stations and the right battery size for replacing the diesel buses with electric buses. By aligning electric bus requirements with charging stations and battery size, the model ensures the feasibility of the operation and minimizes the cost of battery procurement and charging station installation. To test the effectiveness of the developed methodology, a realistic network from Bangalore is selected and tested. By implementing this, transit agencies can optimize the bus charging schedule, battery size, charging location and number of chargers.