Optimizing hub networks for truck platooning under uncertainty in cost savings
研究了在成本节约不确定条件下，通过枢纽网络优化卡车编队行驶，以最小化总成本并提高配送绩效，基于美国39个城市1253种商品的数据，平均降低总成本7.97%，一日内送达比例提升高达42%。
Platooning offers significant potential cost benefits for truckload transportation by utilizing vehicle-to-vehicle communication and automation through the formation and dissolution of platoons at hubs. This paper addresses optimization of platooning hub networks for transporting truckloads of commodities from their origins to destinations within the promised delivery times. Deterministic and stochastic optimization models are developed to design these networks with a minimum total cost, where each truckload of a commodity can be transported either directly along its shortest path from origin to destination or routed via platooning through hubs. The stochastic model incorporates uncertainty associated with the potential cost savings due to platooning. The Sample Average Approximation method is employed to solve the stochastic model. Using real-world data involving 1253 commodities across 39 U.S. cities, the computational analysis demonstrates significant cost savings and delivery performance improvements through platooning. On average, even under the highest hub operating costs, the proposed model achieves a 7.97% reduction in overall costs compared to the direct-shipment-only scenario, with the best-case improvement reaching 15.89%. Additionally, the platoon-enabled network significantly improves delivery performance, increasing the share of shipments delivered within one day by up to 42% compared to the direct-shipment-only case. Furthermore, the results demonstrate the stochastic model’s ability to adapt to cost uncertainties, making it a valuable tool for changing logistics environments.