An Improved ACO for the Urban Logistics Delivery Problem Considering Dynamic Traffic Conditions and Customer Service Quality

The urban logistics delivery problem is a complex path planning task that must balance logistics costs and time-window-based quality of service under time-varying traffic conditions. Most urban logistics models assume constant travel speeds, thereby overlooking dynamic traffic conditions and customer time-window constraints on service quality. Moreover, conventional ant colony optimization (ACO) algorithms optimize routing decisions but fail to explicitly coordinate vehicle service start times. As a result, vehicles may depart at suboptimal times, which can exacerbate road congestion, degrade time window-based quality of service, and increase overall logistics costs. To address these issues, this study formulates a dynamic urban logistics delivery model under dynamic traffic conditions. The model incorporates time window-based service quality into the routing optimization process. It develops an enhanced ACO algorithm with three-dimensional pheromone representation and redesigned transition probability functions to jointly optimize travel routes and service start times. Dynamic delivery and adjustment strategies are further integrated into the enhanced ACO framework, yielding a two-stage hybrid ACO to enhance optimization performance. Experiments on a real-world dataset from Chengdu, China, reveal that the proposed method consistently outperforms baseline ACO, NSGA-II, and SPEA-II under dynamic traffic conditions. Results also show that accounting for time-varying traffic helps avoid peak congestion periods and improves delivery performance.