This research addresses a vehicle routing problem with simultaneous pickup and delivery with time windows from multiple depots (MVRPSPDTW) over a time horizon in the B2C e-commerce logistics system. We consider an e-commerce logistics system with a multi-period, which consists of customers, logistics service providers (LSPs), suppliers, and a decision-making platform. A mixed-integer non-linear programming (MINLP) model is developed and tested on small- and large-scale instances. To handle more realistic large-scale problems, we have used two approaches (i) exact optimisation approach using (i.e. CPLEX tool) and metaheuristic algorithms (i.e. Differential Evolutionary Algorithm (DE), Parallel Differential Evolutionary Algorithm (Par-DE), Genetic Algorithm (GA), and Block-based Genetic Algorithm (BBGA)) to minimise the total transportation cost and penalty due to the delay by logistics service providers. The computation experiment is conducted on the real practical scenario data and the comparative result is demonstrated.

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 the recent era, the rapidly increasing trend of e-commerce business creates opportunities for logistics service providers to grow globally. With this growth, the concern regarding the implementation of sustainability in logistic networks has received attention in recent years. Thus, in this work, we have focused on the vehicle routing problem (VRP) to deliver the products in a lesser time horizon with driver safety concern considerations in business (B2B) e-commerce platforms. We proposed a sustainable logistics network that captures the complexities of suppliers, retailers, and logistics service providers. A mixed-integer nonlinear programming (MINLP) approach is applied to formulate a model to minimize total time associated with order processing, handling, packaging, shipping, and vehicle maintenance. Branch-and-bound algorithms in the LINGO optimization tool and genetic algorithm (GA) are used to solve the formulated mathematical model. The computational experiments are performed in eight different case scenarios (small-sized problem to large-sized problem) to validate the model.

There has been an enormous growth in the availability of refurbished goods in the online marketplace. These days, consumers can buy either the new products or refurbished products based on their budget and individual preferences. As a result, e-commerce firms need to redesign their existing forward and reverse logistics networks while focusing on supply chain sustainability. This paper proposes a novel forward and reverses logistics network design (FRLND) along with a consumer pickup and demand facility within the promised time window while addressing the complexities related to ecommerce platforms, suppliers, manufacturers, third-party logistics providers, retailers, and customer tiers. A mixedinteger non-linear programming (MINLP) model is developed to minimize the overall anticipated cost that consists of costs related to procurement, production, inventory holding, shortages, material for return units, recycling, repairing, disposal, and transportation cost, across the entire supply chain network. The problem under consideration is NP-hard in nature. The special challenges of the problem in consideration are to consider all pickup and distribution nodes of retailers/customers within the range of promising time horizons. For solution purposes, the Block-based Genetic Algorithm, Fruit-fly Algorithm, and CPLEX are used. Computational experiments show the comparative charts and trends that are put on to an extensive range of practical scenarios. The experiments reveal that the Genetic Algorithm performs well than the Fruit Fly algorithm in terms of rate of convergence and solution quality in all cases of interest. CPLEX solution provides the minimum optimal value.