Dirk Briskorn

Station based and free floating are the two established parking regimes of micromobility systems. The former restricts pickup and drop-off to designated stations, which is less convenient for users but reduces the nuisance of sloppily parked rental bikes and scooters. Free-floating micromobility allows users to use any public parking space within the operating area, which increases user flexibility but creates organizational overhead to deal with improperly parked vehicles. Time-varying parking zones, realized via curbside management software (CMS) and geofencing technology, promise a reasonable compromise in the convenience-clutter trade-off. Based on spatiotemporal information, the city administration can either permanently (e.g., pedestrian zones) or temporarily (e.g., weekly farmers’ market) block certain urban areas in their CMS. The micromobility providers, also having access to the CMS, must ensure that vehicles are not returned to undesignated areas with digital fences during the announced times. This paper introduces an optimization approach for micromobility providers to plan time-varying parking zones, given the dynamic municipal parking limitations. Opening and closing urban areas for parking not only requires a digital reaction (i.e., (un)blocking via geofencing) but also produces costs (e.g., removing the remaining scooters of previous periods from the market square). Hence, our optimization task aims to minimize the total costs associated with dynamic parking zones, whereas representative user trips are guaranteed travel within a given time budget. Based on this setting, we show that most urban stakeholders can profit from time-varying parking zones (compared with a static operating area). Our case study based on Berlin-Mitte shows that dynamic parking zones reduce urban space usage at decreasing service costs and only slight user concessions regarding their convenience. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0855 .

To relieve congested city centers, cargo tunnels are a recently revitalized last-mile delivery concept that is currently evaluated in various capability studies in different parts of the world. Under the cargo tunnel concept, freight is lifted into a tunnel at an easy-to-access outskirt depot and loaded onto electrically-powered autonomous cargo vehicles, rail cars, or maglev shuttles. These small shuttles move the goods underground through the tunnel toward small inner-city hubs, where environmentally-friendly vehicles such as cargo bikes take over the final transport leg toward urban customers. A central decision task in this context is the cargo tunnel scheduling problem, where a given set of shipments to be transported through the tunnel must be assigned to the departure slots of tunnel vehicles, such that neither vehicle capacities nor storage capacities in the hub are violated, all shipments reach their destination hubs in time, and the utilization of tunnel vehicles is minimized. This paper is dedicated to deriving exact solutions for this optimization problem. We present three alternative solution approaches, namely, two mixed integer programs and a Dantzig–Wolfe decomposition based on column generation and branch-and-price. An extensive computational study evaluates the performance of these exact solution methods. Furthermore, we show that a lack of shipment consolidation, long unloading times, and scarce storage capacities within the inner-city hubs bear the risk that significant parts of the tunnel capacity remain unused.

To streamline their fulfillment processes, many e-commerce retailers today use automated packaging machines for their outbound parcels. An important performance–waste tradeoff is associated with these machines: To reduce packaging waste when handling different sized goods, packaging machines should be able to handle different carton sizes. However, more carton sizes lead to a more involved scheduling process, so that the throughput performance deteriorates (and vice versa). To investigate this tradeoff, this paper develops scheduling procedures for a specific type of packaging machine, called blocking machines. These packaging machines provide multiple back-to-back packaging devices, each continuously processing a dedicated carton size, but blocking each other whenever incoming goods are not properly ordered according to carton sizes on the infeed conveyor. To reduce the resulting throughput loss, we derive various scheduling problems for optimizing the inflow of goods, provide a thorough analysis of the computational complexity, and derive an exact dynamic programming approach that is polynomial in the number of orders to be packed. This allows us to solve even large real-world instances to proven optimality with which we can analyze the performance–waste tradeoff of blocking machines.

Underground passageways for the transport of goods and people have fascinated mankind throughout the centuries. Recent initiatives in different parts of the world aim at rekindling the freight tunnel concept for urban logistics. According to this concept, freight is lifted into a tunnel, moved via electrically powered vehicles, rail cars, or maglev shuttles toward inner-city microhubs, and finally delivered with environmentally friendly vehicles to urban recipients. The main justification for this concept to outweigh the huge tunnel boring cost is a substantial reduction of road-based urban freight traffic. To warn about unfulfilled expectations, however, we show that the combination of time-critical goods, small inner-city hubs with restricted storage capacity, and the wrong booking procedure can hinder a loading of tunnel vehicles to capacity. To do so, we consider a polynomially solvable scheduling problem where due date-restricted shipments are to be assigned to tunnel vehicles and delivered to a given set of microhubs with limited storage capacity. Scarce hub capacities, tight due dates, and the wrong booking policy may not only lead to unused tunnel capacity but also to shipments that cannot be feasibly transported through the tunnel. If they are handed over to trucks as a fallback option, we show that a Braess-like paradox can occur: The time-critical, small-lot transports of the leftover shipments can produce even more surface traffic than trucking all shipments right from the start without the tunnel. Supplemental Material: The online appendices are available at https://doi.org/10.1287/serv.2023.0005 .