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Given the expansion of the COVID-19 cases and the average infection rate globally, constructing a robust healthcare supply chain system for the crisis is highly crucial. The third-party logistics providers (3PLs), who can match the market demand with reliable manufacturers worldwide, have emerged as orchestrators. In addition to the basic transportation and storage services, some 3PLs can also provide procurement assistance to relatively small retailers. To illustrate the value of the above-mentioned business model, we build a game-theoretic model to capture participants’ optimal strategy in a healthcare supply chain consisting of a manufacturer, a 3PL provider, and a retailer. We also investigate the conditions where the performance in this business model outperforms the traditional model. It is concluded that the 3PL’s positive effect appears when the decentralized supply chain is characterized by high logistics outsourcing costs and high-level price sensitivity. We further design an incentive mechanism that can coordinate the supply chain. Finally, a series of numerical experiments are carried out to demonstrate the effectiveness of our model.

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This paper introduces a new model of the customer-centric, two-product split delivery vehicle routing problem (CTSDVRP) in the context of a mixed-flow manufacturing system that occurs in the power industry. Different from the general VRP model, the unique characteristics of our model are: (1) two types of products are delivered, and the demand for them is interdependent and based on a bill of materials (BOM); (2) the paper considers a new aspect in customer satisfaction, i.e., the consideration of the production efficiency on the customer side. In our model, customer satisfaction is not measured by the actual customer waiting time, but by the weighted customer waiting time, which is based on the targeted service rate of the end products. We define the targeted service rate as the ratio of the quantity of the end product produced by the corresponding delivery quantities of the two products to the demand of the end product. We propose a hybrid ant colony-genetic optimization algorithm to solve this model with actual data from a case study of the State Grid Corporation of China. Finally, a case study is explored to assess the effectiveness of the CTSDVRP model and highlight some insights. The results show that the CTSDVRP model can improve customer satisfaction and increase the average targeted service rate of the end products effectively.

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