Preemptive depot returns for dynamic same-day delivery

Ulmer, Marlin W.; Thomas, Barrett W.; Mattfeld, Dirk C.

doi:10.1007/s13676-018-0124-0

Cited by 71 publications

(43 citation statements)

References 22 publications

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“…They apply a rollout algorithm (RA) of an a‐priori policy to determine the customers to serve and when to leave the depot. Ulmer et al also considers decision making for a single vehicle, but introduces an offline ADP approach based on state‐space aggregation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Most of the approaches for the SDD existing in the literature evaluate the quality of decisions using simulation. Except in the case of Ulmer et al , these simulations are run at the time of decision making or online. We show that such methods provide only limited improvement in solution quality relative to the PFA while greatly increasing computation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We note that Ulmer explores the pricing of incentives in SDD. As part of the solution method, Ulmer introduces a PFA that helps set basis prices for the incentives.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We note that Ulmer explores the pricing of incentives in SDD. As part of the solution method, Ulmer introduces a PFA that helps set basis prices for the incentives. The PFA in Ulmer and that presented in this paper are similar only in the sense that they are parameterized policies for which the best parameter setting is found by simulation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As part of the solution method, Ulmer introduces a PFA that helps set basis prices for the incentives. The PFA in Ulmer and that presented in this paper are similar only in the sense that they are parameterized policies for which the best parameter setting is found by simulation. Furthermore, Ulmer models only homogeneous vehicles and does not explore issues associated with assignments in a heterogeneous fleet.…”

Section: Literature Reviewmentioning

confidence: 99%

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Same‐day delivery with heterogeneous fleets of drones and vehicles

2018

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In this paper, we analyze how drones can be combined with regular delivery vehicles to improve same‐day delivery performance. To this end, we present a dynamic vehicle routing problem with heterogeneous fleets. Customers order goods over the course of the day. These goods are delivered either by a drone or by a regular transportation vehicle within a delivery deadline. Drones are faster, but have a limited capacity as well as require charging after use. In the same‐day context, vehicle capacity is not a constraint, but vehicles are slow due to urban traffic. To decide whether an order is delivered by a drone or by a vehicle, we present a policy function approximation based on geographical districting. Our computational study reveals two major implications. First, geographical districting is highly effective increasing the expected number of same‐day deliveries. Second, a combination of drone and vehicle fleets may significantly reduce the required delivery resources.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…We note that Ulmer explores the pricing of incentives in SDD. As part of the solution method, Ulmer introduces a PFA that helps set basis prices for the incentives.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Same‐day delivery with heterogeneous fleets of drones and vehicles

2018

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Robust optimization of dynamic route planning in same‐day delivery networks with one‐time observation of new demand

2019

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Local delivery networks expect drivers to make deliveries to and/or pickups from customers using the shortest routes in order to minimize costs, delivery time, and environmental impact. However, in real-world applications, it is often the case that not all customers are known when planning the initial delivery route. Instead, additional customers become known while the driver is making deliveries or pickups. Before serving the new demand requests, the vehicle will return to the depot for restocking. In other words, there exists a precedence relation in the delivery route to visit the depot before delivering new orders. The uncertainty in new customer locations can lead to expensive rerouting of the tour, as drivers revisit previous neighborhoods to serve the new customers. We address this issue by constructing the delivery route with the knowledge that additional customers will appear, using historical demand patterns to guide our predictions for the uncertainty. We model this network delivery problem as a precedence-constrained asymmetric traveling salesman problem using mixed-integer optimization. Experimental results show that the proposed robust optimization approach provides an effective delivery route under the uncertainty of customer demands. KEYWORDS demand uncertainty, dynamic vehicle routing, precedence-constrained traveling salesman problem, robust optimization, same-day delivery network Networks. 2019;73:434-452.wileyonlinelibrary.com/journal/net FIGURE 1 (A) Ill-placed route planning under uncertainty-the tour will back-track to a previously visited neighborhood to reach the newly emerged nodes; (B) well-placed route planning under uncertainty-the tour does not need to return to previously visited neighborhoods to reach the newly emerged nodes. Note that the white unshaded circles are the known nodes, on which the original TSP is calculated. At a particular time, the tour reaches w * , uncertainty is realized, and the new, shaded nodes are added to the problem. The tour will return to the depot, d, before visiting the new nodes

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Recent challenges in Routing and Inventory Routing: E‐commerce and last‐mile delivery

Archetti

Bertazzi

2020

Networks

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In the e‐commerce era, vendors have to satisfy a large number of on‐line orders, mainly from private customers, with low weight and volume, reduced delivery time, and overlap of customers' time windows. Production is made available all day long. New strategies and new technologies are emerging for deliveries. The processing time of the orders is reduced. These new features generate interesting challenges in formulating and solving Routing and Inventory Routing problems. After discussing these features and the corresponding challenges, we recall the relevant literature in Routing and Inventory Routing and provide future research directions, mainly related to routing problems with release dates, routing problems with crowdshipping, and inventory routing problems in the e‐commerce era.

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Preemptive depot returns for dynamic same-day delivery

Cited by 71 publications

References 22 publications

Same‐day delivery with heterogeneous fleets of drones and vehicles

Same‐day delivery with heterogeneous fleets of drones and vehicles

Robust optimization of dynamic route planning in same‐day delivery networks with one‐time observation of new demand

Recent challenges in Routing and Inventory Routing: E‐commerce and last‐mile delivery

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