An Adaptive Variable Neighborhood Search Algorithm for a Vehicle Routing Problem Arising in Small Package Shipping

Stenger, A.; Vigo, Daniele; Enz, Steffen; Schwind, Michael

doi:10.1287/trsc.1110.0396

Cited by 123 publications

(78 citation statements)

References 26 publications

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“…However, none of these works considered the ride-pooling problem of servicing multiple rides with a single trip. The ride-pooling problem is more related to the vehicle-routing problem and the dynamic pickup and delivery problem (11–15), where spatiotemporally distributed demand must be picked up and delivered within prespecified time windows. A major challenge when addressing this problem is the need to explore a very large decision space, while computing solutions fast enough to provide users with the experience of real-time booking and service.…”

mentioning

confidence: 99%

On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

Alonso–Mora

Samaranayake

Wallar

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

924

741

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SignificanceRide-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min.

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mentioning

confidence: 99%

On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

Alonso–Mora

Samaranayake

Wallar

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

924

741

View full text Add to dashboard Cite

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“…() and Stenger et al. (, ). The first paper studies a profit‐maximizing extension of the classical MDVRP with pricing where visiting a customer generates a net profit.…”

Section: Introduction and Literature Reviewmentioning

confidence: 98%

The r‐interdiction selective multi‐depot vehicle routing problem

Sadati

Aksen

Aras

2019

Int Trans Operational Res

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The protection of critical facilities has been attracting increasing attention in the past two decades. Critical facilities involve physical assets such as bridges, railways, power plants, hospitals, and transportation hubs among others. In this study we introduce a bilevel optimization problem for the determination of the most critical depots in a vehicle routing context. The problem is modeled as an attacker–defender game (Stackelberg game) from the perspective of an adversary agent (the attacker) who aims to inflict maximum disruption on a routing network. We refer to this problem as the r‐interdiction selective multi‐depot vehicle routing problem (RI‐SMDVRP). The attacker is the decision maker in the upper level problem (ULP) who chooses r depots to interdict with certainty. The defender is the decision maker in the lower level problem (LLP) who optimizes the vehicle routes in the wake of the attack. The defender has to satisfy all customer demand either using the remaining depots or through outsourcing to a third party logistics service provider. The ULP is solved through exhaustive enumeration, which is viable when the cardinality of interdictions does not exceed five among nine depots. For the LLP we implement a tabu search heuristic adapted to the selective multi‐depot VRP. Our results are obtained on a set of RI‐SMDVRP instances synthetically constructed from standard MDVRP test instances.

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“…In this problem, there is a penalty cost per customer if it is served by an external carrier, and the objective is to minimize the costs to serve all customers either by the private fleet or by an external carrier [5,3]. Most work on the VRPPC is focussed on the design of heuristics with Tabu search [6,20] and adaptive variable neighborhood search [23] currently showing the most promising results.…”

Section: Related Literaturementioning

confidence: 99%

Shared Capacity Routing Problem − An omni-channel retail study

Paul

Agatz

Spliet

et al. 2019

European Journal of Operational Research

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More and more retailers allow customers to order goods online and then pick them up in a store. In this setting, these orders are typically served from a dedicated warehouse.This often means that the stores are visited by different vehicles to replenish the store inventory and to supply the pick-up points. Motivated by a collaboration with an omnichannel grocery retailer in the Netherlands, we study how to best share capacity between the routes associated with these different sales channels. As operational constraints prevent jointly planning the routes, we consider the replenishment routes as fixed when planning the routes to serve the pick-up orders. An order can be transferred to the replenishment route, if capacity allows. We consider the problem of deciding which customer orders to transfer and which to deliver directly such that the total costs are minimized. We present an exact and heuristic approach to solve this problem. Computational experiments on both real-world and artificial instances show that substantial savings can be achieved by sharing vehicle capacity across different channels.

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An Adaptive Variable Neighborhood Search Algorithm for a Vehicle Routing Problem Arising in Small Package Shipping

Cited by 123 publications

References 26 publications

On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

The r‐interdiction selective multi‐depot vehicle routing problem

Shared Capacity Routing Problem − An omni-channel retail study

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