A parallel route building algorithm for the vehicle routing and scheduling problem with time windows

Potvin, Jean-Yves; Rousseau, Jean-Marc

doi:10.1016/0377-2217(93)90221-8

Cited by 336 publications

(153 citation statements)

References 6 publications

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“…To avoid this behaviour, the regret heuristic uses a so-called regret value which represents the expected costs of inserting a node not in this iteration but in a future iteration. Such heuristics have been used by Potvin and Rousseau (1993) for the VRPTW and by Trick (1992) for the Generalized Assignment Problem. The regret value for a node v is obtained as follows.…”

Section: Insertion Based Repairmentioning

confidence: 99%

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

Hiermann

Puchinger

Røpke

et al. 2016

European Journal of Operational Research

395

222

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Due to new regulations and further technological progress in the field of electric vehicles, the research community faces the new challenge of incorporating the electric energy based restrictions into vehicle routing problems. One of these restrictions is the limited battery capacity which makes detours to recharging stations necessary, thus requiring efficient tour planning mechanisms in order to sustain the competitiveness of electric vehicles compared to conventional vehicles. We introduce the Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and recharging stations (E-FSMFTW) to model decisions to be made with regards to fleet composition and the actual vehicle routes including the choice of recharging times and locations. The available vehicle types differ in their transport capacity, battery size and acquisition cost. Furthermore, we consider time windows at customer locations, which is a common and important constraint in real-world routing and planning problems. We solve this problem by means of branch-and-price as well as proposing a hybrid heuristic, which combines an Adaptive Large Neighbourhood Search with an embedded local search and labelling procedure for intensification. By solving a newly created set of benchmark instances for the E-FSMFTW and the existing single vehicle type benchmark using an exact method as well, we show the effectiveness of the proposed approach.

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Section: Insertion Based Repairmentioning

confidence: 99%

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

Hiermann

Puchinger

Røpke

et al. 2016

European Journal of Operational Research

395

222

View full text Add to dashboard Cite

show abstract

“…In LANTIME, an initial solution is created for the VRPTW using the parallel insertion algorithm of Potvin and Rousseau [22]. The parallel insertion algorithm must ensure that the solution is feasible with respect to all constraints.…”

Section: Algorithm Design 321 Lantime Algorithmmentioning

confidence: 99%

Minimum cost VRP with time-dependent speed data and congestion charge

Wen

Eglese

2015

Computers & Operations Research

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A heuristic algorithm, called LANCOST, is introduced for vehicle routing and scheduling problems to minimize the total travel cost, where the total travel cost includes fuel cost, driver cost and congestion charge. The fuel cost required is influenced by the speed. The speed for a vehicle to travel along any road in the network varies according to the time of travel. The variation in speed is caused by congestion which is greatest during morning and evening rush hours. If a vehicle enters the congestion charge zone at any time, a fixed charge is applied. A benchmark dataset is designed to test the algorithm. The algorithm is also used to schedule a fleet of delivery vehicles operating in the London area.

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“…• Regret heuristic: This heuristic is based on the notion of regret, used for example by Potvin and Rousseau [31] for the Vehicle Routing Problem with Time Windows (VRPTW), and extended by Ropke and Pisinger [30]. Let U be the set of unplanned requests and, for each request i ∈ U , let ∆f j i be the insertion cost of i in the j th best route at the best position.…”

Section: General Repair Operatorsmentioning

confidence: 99%

The Dial-A-Ride Problem with Transfers

Masson

Lehuédé

Péton

2014

Computers & Operations Research

131

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The Dial-A-Ride Problem with Transfers (DARPT) consists in defining a set of routes that satisfy transportation requests of users between a set of pickup points and a set of delivery points, in the presence of ride time constraints. Users may change vehicles during their trip. This change of vehicle, called a transfer, is made at specific locations called transfer points. Solving the DARPT involves modeling and algorithmic difficulties. In this paper we provide a solution method based on an Adaptive Large Neighborhood Search (ALNS) metaheuristic and explain how to check the feasibility of a request insertion. The method is evaluated on real-life and generated instances. Experiments show that savings due to transfers can be up to 8% on real-life instances.

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A parallel route building algorithm for the vehicle routing and scheduling problem with time windows

Cited by 336 publications

References 6 publications

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

Minimum cost VRP with time-dependent speed data and congestion charge

The Dial-A-Ride Problem with Transfers

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