In recent years, aging equipment in urban rail transit (URT) systems has led to frequent disruptions. Bus bridging services are an effective means of responding to such disruptions. Although prior studies have proposed bus bridging strategies to convert existing bus routes into bridging services, these strategies are inflexible with limited coverage. To address these limitations, this study proposes a novel bus bridging method that not only enables bus stops adjacent to the disrupted URT stations to directly provide bridging services, but also introduces two complementary bridging strategies. The first strategy involves fine-tuning partial segments of existing bus routes without skipping any regular stops, allowing detours to serve the disrupted URT stations, namely F-BB. The second strategy extends routes from the terminal of the existing bus routes to detour to the disrupted URT stations, namely E-BB. To accommodate time-varying travel demands, a time-segmented optimization (TSO) method is adopted to adjust the bridging strategies across different periods. A passenger flow control strategy is further developed to assign stranded passengers to designated waiting areas of bridging routes. A mixed-integer nonlinear programming (MINLP) model is formulated to jointly consider the travel demands of regular and stranded passengers, which is solved using a rolling-horizon-based adaptive large neighborhood search (RH-ALNS) algorithm. The proposed model and algorithm are validated through a case study in Chengdu, China. Numerical results indicate that, compared with four scenarios excluding E-BB, F-BB, TSO, and all bridging strategies, the proposed method reduces total travel time by 0.26%, 55.62%, 1.67%, and 89.92%, respectively.
The rapid growth of on-demand e-commerce has shifted logistics toward fragmented, time-sensitive orders, often forcing carriers to prioritize delivery speed over vehicle utilization, resulting in low loading rates and high empty mileage. While the Physical Internet (PI) provides a foundation for collaboration, existing mechanisms remain hub-centric and treat each transport unit as indivisible, leaving the upstream order-grouping phase unoptimized and the idle capacity within partially loaded units untradeable. To bridge this gap, this research proposes an order-to-collaboration framework within the Cyber-Physical Internet (CPI) network. The framework integrates three components. First, an Out-of-Order (OOO) clustering rule consolidates fragmented orders at the intake stage. Second, a three-state Protocol Shipment Unit (PSU) loading classification links each loading condition to a specific carrier action: direct routing, auctioning idle capacity, or bidding for external capacity. Third, a multi-attribute combinatorial auction prices intra-PSU idle capacity using the loading, routing, and goods tables of CPI cyber space. Slot discretization substantially reduces the admissible bidding space by restricting bids to contiguous slot bundles, thereby reducing the size of the allocation problem and supporting real-time tractability in the tested configurations. Large-scale numerical experiments on a Greater Bay Area logistics network show that the framework raises the average PSU loading rate to 0.81 and reduces transportation cost by 29% and CO2 emissions by 23%, while achieving a 71% auction success rate with a 32% revenue gain for auctioneers and 28% cost saving for bidders. Response surface analysis further identifies an effective multi-objective operating region for joint parameter tuning. The framework advances theoretical understanding of carrier collaboration in CPI and offers a structured mechanism with potential to support efficient, profitable, and low-carbon on-demand logistics.
Recent technological advancements have facilitated various initiatives in the last-mile location routing problem (LM-LRP), yet existing LM-LRP literature largely evaluates these initiatives in isolation and lacks a unified framework. This study aims to address this limitation by conducting a systematic literature review of emerging initiatives in LM-LRP to synthesize their technological and operational implications. First, transportation nodes and modes are categorized to show how emerging initiatives change the spatial configuration of logistics facilities and affect sustainability dimensions, while increasing coordination requirements across LM-LRP networks. The literature is then assessed based on key LM-LRP features, including distribution network structure, objective-function design, and last-mile applications. A digital twin perspective is further proposed as a unified decision-support system to connect LM-LRP components with data-driven modeling and simulation approaches. Sustainability trade-offs are subsequently examined across emerging LM-LRP initiatives, showing that their economic, environmental, and social benefits remain context-dependent. Key research gaps are identified, including limited operational realism, fragmented technology integration, and insufficient governance and policy considerations. Based on these findings, a forward-looking framework is proposed to summarize initiative adoption and future development pathways. Finally, this review provides actionable research insights that can facilitate the implementation of emerging initiatives for more efficient last-mile distribution networks.
