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This study considers the fresh food city logistics that involves the last-mile distribution of commodities to the customer locations from the local distribution centres (LDCs) established by the e-commerce firms. In this scenario, the last-mile logistics is crucial for its speed of response and the effectiveness in distribution of packages to the target destinations. We propose a clustering-based routing heuristic (CRH) to manage the vehicle routing for the last-mile logistic operations of fresh food in e-commerce. CRH is a clustering algorithm that performs repetitive clustering of demand nodes until the nodes within each cluster become serviceable by a single vehicle. The computational complexity of the algorithm is reduced due to the downsizing of the network through clustering and, hence, produces an optimum feasible solution in less computational time. The algorithm performance was analysed using various operating scenarios and satisfactory results were obtained.

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PurposeDue to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.Design/methodology/approachA mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.FindingsThe model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.Research limitations/implicationsIn literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.Originality/valueThe suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.

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.