An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows

By Stefan Røpke, David Pisinger. Transportation Science, 2006

2,548 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/edfde8ed2d97e052da145323ffbf2eff1943c483

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In Automotive Engineering

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Citations in the corpus

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations (2016, positive): “…in this iteration After a learning period (of η L iterations) this score ¯ ν i is used to update the selection probabilities of operator i using the following equation (cf. Ropke and Pisinger, 2006) The weight ν i,j +1 of heuristic i in the next period j +1 is calculated using iteration (4.16).”
Horizontal cooperation among freight carriers: request allocation and profit sharing (2008, methodology): “Here we use the heuristic proposed by Ropke and Pisinger (2006) which is probably the best available and can be applied to instances involving one or several depots.”
A review of recent research on green road freight transportation (2014, methodology): “The former problem was solved through adaptive large neighborhood search (ALNS) (Ropke and Pisinger, 2006) and the latter was solved through a polynomial time speed optimization procedure (SOP) as will be discussed later.”
Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care (2013, neutral): “( Li and Lim, 2001), (Pankratz, 2005), (Ropke and Pisinger, 2006) and (S.”
Modular vehicle routing for combined passenger and freight transport (2022, methodology): “The basic algorithm is identical to the one proposed in Ropke and Pisinger (2006). The implemented heuristics are adjusted to consider the modularity and consolidation in the model.”
Hybrid metaheuristics in combinatorial optimization: A survey (2011, methodology): “Further special MIP-based neighborhoods have, for example, been described by Oncan et al. [108] for partitioning problems, Ropke and Pisinger [109] for pickup and delivery problems with time windows, and Pirkwieser and Raidl [110] for a Periodic Location-Routing Problem.”
Models and branch‐and‐cut algorithms for pickup and delivery problems with time windows (2007, positive): “Prior to solving the problem, an upper bound is computed by using either the adaptive large neighbourhood search algorithm of Ropke and Pisinger [21] for the PDPTW or the tabu search heuristic of Cordeau and Laporte [7] for the DARP.”
The time-dependent vehicle routing problem with soft time windows and stochastic travel times (2014, positive): “We apply three removal heuristics similar to ones given by Ropke and Pisinger (2006).”
Branch and Cut and Price for the Pickup and Delivery Problem with Time Windows (2009, neutral): “Upper bounds are found using the adaptive large neighborhood heuristic proposed by Ropke and Pisinger (2006).”
Learning Improvement Heuristics for Solving Routing Problems (2019, background): “Despite lacking theoretical guarantee on the solution quality, heuristics often can ﬁnd desirable solutions within reasonable computational time [7], [8], [9].”
Routing a mixed fleet of electric and conventional vehicles (2015, methodology): “Worst removal was introduced by Ropke and Pisinger (2006b) and aims at removing vertices that are unfavorable at their current position in a route.”
An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones (2019, methodology): “Furthermore, the remaining algorithm-parameters concerning the adaptive part of the metaheuristic were set to the values as documented in Ropke and Pisinger (2006). Therefore, the reaction factor is set to ρ = 0.”
An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics (2012, methodology): “ALNS was first developed by Ropke and Pisinger (2006) for the pickup and delivery problem and these authors later solved several variants of the vehicle routing problem with a general algorithm based on the same methodology (Pisinger and Ropke (2007)).”
Static pickup and delivery problems: a classification scheme and survey (2007, methodology): “Another LNS heuristic for the VRP-PDTW was later proposed by Ropke and Pisinger (2006a) .”
A general heuristic for vehicle routing problems (2007, methodology): “Ropke and Pisinger [3] used a modified version of this algorithm in the VRP for splitting requests on a route into two strongly connected subsets.”