Vehicle routing and scheduling with time-varying data: A case study

Maden, Will; Eglese, Richard W.; Black, Dan

doi:10.1057/jors.2009.116

Cited by 172 publications

(94 citation statements)

References 11 publications

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“…The paper describes a case study using real speed data on a road network in the north of England. Maden et al [1] introduce a vehicle routing and scheduling algorithm, called LANTIME, that is able to accept data from a Road Timetable and to construct a set of vehicle routes that aims to minimize the total time required to deliver goods from a depot to a set of customers subject to a set of constraints. The LANTIME algorithm uses the parallel insertion algorithm firstly to generate an initial feasible solution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Compared with the formulation in Maden et al [1], the objective of the model has been changed from minimizing the total travelling time to minimizing the total cost.…”

Section: Model Formulationmentioning

confidence: 99%

“…The LANTIME algorithm (Maden et al [1]) solves the least time VRP problem with multiple trips in a road network with time-dependent speeds. A new algorithm, called LANCOST, is based on the LANTIME algorithm and some new enhancements are added to solve the minimum cost version of the problem.…”

Section: Algorithm Design 321 Lantime Algorithmmentioning

confidence: 99%

“…The paper introduces a heuristic method to solve the minimum cost VRP with time-dependent travel times in a road network with a congestion charge zone. The heuristic method is an enhanced version of the LANTIME algorithm which was introduced by Maden et al [1]. The new heuristic method is called LANCOST.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

“…Compared with the formulation in Maden et al [1], the objective of the model has been changed from minimizing the total travelling time to minimizing the total cost.…”

Section: Model Formulationmentioning

confidence: 99%

Section: Algorithm Design 321 Lantime Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wen

Eglese

2015

Computers & Operations Research

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“…Maden et al (2009) analizaron un VRPTW que reportó una reducción de hasta un 7% en las emisiones de CO2 en un estudio de caso realizado en el Reino Unido. Bauer et al (2010) estudiaron el efecto del transporte intermodal en la reducción de emisiones.…”

Section: Introductionunclassified

Una Solución al Enrutamiento de Vehículos en Ciudades Montañosas Considerando Aspectos Ambientales y Económicos

2018

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_____________________________________________________________________________________________________________________________________ ResumenEste trabajo aborda un problema de enrutamiento de vehículos en ciudades de montaña, para determinar la más óptima. Actualmente el transporte verde se ha convertido en un tema de gran interés debido a su relación directa con el costo operativo y su impacto en el medio ambiente. Tradicionalmente se ha considerado que la ruta más corta minimiza tanto costos como emisiones. Sin embargo, la topografía del terreno en montañas es una particularidad que debe ser considerada con miras a lograr soluciones más acordes con la realidad. Para este estudio, se propone un modelo matemático multiobjetivo, el cual determina una ruta que logra un adecuado balance entre el costo del transporte y el impacto ambiental. El modelo se aplicó en un canal de distribución minorista de una ciudad andina de Colombia. Se concluyó que, en escenarios de montaña, la ruta más corta no es necesariamente la mejor, debido al impacto de la pendiente en el consumo de combustible y en las emisiones de CO2. Palabras clave: enrutamiento de vehículos; consumo de combustible; emisiones de CO2; pendiente de la carretera; multiobjetivo A Vehicle Routing Solution for Hilly Cities Considering Environmental and Economic Issues AbstractThis paper addresses a vehicle routing problem in mountainous cities with the objective of finding the optimum route. Currently, green transportation has become a topic of great interest due to its direct relation with operational costs and its impact on the environment. Traditionally, the shortest route has been considered to minimize both costs and emissions. However, topography in mountains is a particularity that must be considered in order to achieve more realistic solutions. For this study, a multi-objective mathematical model is proposed to determine a route that achieve a proper balance between transportation cost and environmental impact. The model was applied in a retail distribution channel of an Andean city in Colombia. It was concluded that in mountain scenarios, the shortest route is not necessarily the optimum, due to the impact of slope on fuel consumption and CO2 emissions.

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Finding least fuel emission paths in a network with time‐varying speeds

Qian

Eglese

2013

Networks

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This article considers the problem of finding a route and schedule for a vehicle starting from a depot, visiting a set of customers, and returning to the depot, in a time-dependent network where the objective is to minimize the greenhouse gas emissions. In this formulation, the speeds of the vehicle as well as the routes chosen are decision variables subject to limits determined by the level of congestion on the roads at the time. Two methods are proposed to find the optimal strategy for a single route. One is a time-increment-based dynamic programming method, and the other is a new heuristic approach. In addition, a case study is carried out, which compares the performances of these methods, as well as the least polluting routes with the shortest time routes between two customer nodes.

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Vehicle routing and scheduling with time-varying data: A case study

Cited by 172 publications

References 11 publications

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

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

Una Solución al Enrutamiento de Vehículos en Ciudades Montañosas Considerando Aspectos Ambientales y Económicos

Finding least fuel emission paths in a network with time‐varying speeds

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