An exact method for the biobjective shortest path problem for large-scale road networks

Duque, Daniel; Lozano, Leonardo; Medaglia, Andrés L.

doi:10.1016/j.ejor.2014.11.003

Cited by 68 publications

(61 citation statements)

References 22 publications

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“…Generally, there are two types of solutions to it: dynamic programming (DP) [8,9] and ranking [10]. Pulse algorithm [11] is a precise algorithm to solve BSP. Although an idea of recursively traversing all paths in a network is adopted, it is still efficient by using a four-step pruning mechanism to exclude dominated paths effectively.…”

Section: Problem-solving Methodsmentioning

confidence: 99%

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Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load

Chai

et al. 2017

Discrete Dynamics in Nature and Society

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Mathematical models for path planning and vehicle scheduling for logistic distribution of hazardous materials in full container load (FCL) are established, with their problem-solving methods proposed. First, a two-stage multiobjective optimization algorithm is designed for path planning. In the first stage, pulse algorithm is used to obtain the Pareto paths from the distribution center to each destination. In the second stage, a multiobjective optimization method based on Nondominated Sorting Genetic Algorithm II (NSGA-II) is designed to obtain candidate transport paths. Second, with analysis on the operating process of vehicles with hazardous materials in FCL, the vehicle scheduling problem is converted to Vehicle Routing Problem with Time Windows (VRPTW). A problem-solving method based on estimation of distribution is adopted. A transport timetable for all vehicles based on their transport paths is calculated, with participation of the decision-makers. A visual vehicle scheduling plan is presented for the decision-makers. Last, two examples are used to test the method proposed in this study: distribution of hazardous materials in a small-scale test network and distribution of oil products for sixteen gas stations in the main districts of Lanzhou city. In both examples, our method is used to obtain the path selection and vehicle scheduling plan, proving that validity of our method is verified.

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Section: Problem-solving Methodsmentioning

confidence: 99%

“…(2) It exceeds either one or both upper bounds [11] defined by the nadir point before reaching the end node. (3) It is dominated by any solution which is in current efficient set before reaching the end node.…”

Section: Problem-solving Methodsmentioning

confidence: 99%

Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load

Chai

et al. 2017

Discrete Dynamics in Nature and Society

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“… Duque et al . developed an exact recursive approach for simultaneously minimizing two conflicting objectives while finding the shortest path in a network. A shortest time path need not be the fuel efficient path assuming that the travel time through the network is a function of vehicle speed and there are no speed restrictions.…”

Section: Related Literaturementioning

confidence: 99%

“…Azaron and Kianfar [20] and Peer and Sharma [21] developed solutions for the shortest path problem in a an evolutionary algorithm that identified single most dominant solution as well as achieved a pre-defined number of non-dominant solutions for multi-objective shortest path problem. An exact label-setting algorithm that identifies a set of pareto optimal paths based on lexicographic goals for multi-objective shortest path problem was developed by Pulido et al [56] Duque et al [57] developed an exact recursive approach for simultaneously minimizing two conflicting objectives while finding the shortest path in a network. A shortest time path need not be the fuel efficient path assuming that the travel time through the network is a function of vehicle speed and there are no speed restrictions.…”

Section: Related Literaturementioning

confidence: 99%

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Godwin

Sajeev

George

2016

J of Advced Transportation

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Summary Intra‐city commuting is being revolutionized by call‐taxi services in many developing countries such as India. A customer requests a taxi via phone, and it arrives at the right time and at the right location for the pick‐up. This mode of intra‐city travel has become one of the most reliable and convenient modes of transportation for customers traveling for business and non‐business purposes. The increased number of vehicles on city roads and raising fuel costs has prompted a new type of transportation logistics problem of finding a fuel‐efficient and quickest path for a call‐taxi through a city road network, where the travel times are stochastic. The stochastic travel time of the road network is induced by obstacles such as the traffic signals and intersections. The delay and additional fuel consumption at each of these obstacles are calculated that are later imputed to the total travel time and fuel consumption of a path. A Monte‐Carlo simulation‐based approach is proposed to identify unique fuel‐efficient paths between two locations in a city road network where each obstacle has a delay distribution. A multi‐criteria score is then assigned to each unique path based on the probability that the path is fuel efficient, the average travel time of the path and the coefficient of variation of the travel times of the path. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Recently, (Duque et al, 2015) proposed a new exact method, called Pulse algorithm, for the BSP and large-scale road networks. Pulse algorithm is based on recursive method using pruning strategies that accelerate the graph exploration.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Label-setting Algorithm for the Bi-objective Shortest Path Problem

Giret

Kergosien

Sauvanet

et al. 2016

Proceedings of 5th the International Conference on Operations Research and Enterprise Systems

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In this paper, we consider a classical Bi-objective Shortest Path problem (BSP) that takes into account both distance and insecurity criteria. This work is in collaboration with an enterprise who provide a web platform called Géovélo that aims to propose routes for cycling. We propose a new exact method to solve a BSP, called Label Setting algorithm with Dynamic update of Pareto Front (LSDPF), which aims to find all nondominated solutions of the problem. Different exploration strategies have been proposed and tested. Numerical experiments on real data sets and on instances of the literature were conducted. Comparison with recent benchmarks algorithms solving BSP-the bounded Label Setting algorithm by (Raith, 2010) and the pulse algorithm by (Duque et al., 2015)-shows that our method outperform these benchmarks algorithms.

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An exact method for the biobjective shortest path problem for large-scale road networks

Cited by 68 publications

References 22 publications

Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load

Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

An Efficient Label-setting Algorithm for the Bi-objective Shortest Path Problem

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