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.

Large-scale promotions lead to a huge number of orders, and the quantity of deliveries grows sharply, which puts considerable strain on cities’ logistics and imposes high related shipping costs. To alleviate these consequences, in this paper we provide a new contingent free shipping policy with delayed delivery (DD-CFS) for an online retailer during large-scale promotions and study its CFS threshold decisions, considering consumers’ different sensitivities to delivery time delays.We start by analyzing a consumer’s purchasing decision based on consumer utility theory. Next, we establish a mathematical model to help the online retailer find the optimal CFS threshold to maximize its expected profit. Finally, we analyze the benefit of delayed delivery to the online retailer and conduct a sensitivity analysis to examine the impacts of important parameters on the online retailer’s CFS threshold decisions, profit, and the value of the delayed delivery. We find that the DD-CFS policy can lead to more profits during the large-scale promotions period compared with the traditional CFS policy. As the delayed delivery time and the consumer’s negative attitude towards delayed delivery time increase, the online retailer should reduce the low CFS threshold value. On the other hand, as the shipping fee and the consumer’s negative attitude towards the shipping fee increase, the online retailer should raise the high and low CFS threshold values.

The global focus on carbon reduction has intensified, prompting numerous high-energy-consuming enterprises to venture into the carbon cap-and-trade system. However, in recent years, the emergence of destabilizing factors has introduced disruptions to supply chains. The study addresses the two-stage ordering problem for a manufacturer under the carbon cap-and-trade system. In the first stage, the manufacturer engages in green investments and places orders with both an unreliable and a reliable supplier. After updating demand forecast information in the second stage, orders are placed with the backup supplier, and carbon allowances are settled at the end of the period. Under these conditions, three supply scenarios of the unreliable supplier are considered: time-varying supply with imperfect demand updates, all-or-nothing supply with imperfect demand updates, and time-varying supply with perfect demand updates. Optimal ordering decisions are provided for each scenario. We find that when demand updates are imperfect, the manufacturer will invariably engage with the unreliable supplier. However, when demand updates are perfect, the manufacturer may choose to forgo the unreliable supplier. Next, we analyze the influence of carbon trading prices on ordering decisions in these scenarios. We find that when the probability of disruption is substantial, dual sourcing must exist in the first stage under the all-or-nothing supply. Finally, we conduct numerical analysis by utilizing parameters, such as carbon trading prices, as referenced in the existing literature. Through numerical analysis, we find that opting for the all-or-nothing supplier becomes economically advantageous for the manufacturer when the backup supplier is profitable. Conversely, when the backup supplier is not profitable, the manufacturer tends to opt for the unreliable supplier with time-varying supply. Moreover, optimal profit for the manufacturer is not achieved when demand updates are perfect.