Solving shortest path problems with a weight constraint and replenishment arcs

Smith, Olivia J.; Boland, Natashia; Waterer, Hamish

doi:10.1016/j.cor.2011.07.017

Cited by 67 publications

(58 citation statements)

References 30 publications

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“…We adapt an approach from the SPP with a weight constraint and replenishment arcs studied by [27]. However unlike the initial problem we considered the CS at a limited number of nodes and not in the arcs.…”

Section: B Proposed Methodologymentioning

confidence: 99%

Electric Vehicle green routing with possible en-route recharging

Baouche

Billot

Trigui³

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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The deployment of Electric Vehicles (EVs) is constrained mainly by the restricted autonomy, the lack of charging stations, the battery recharge time and recuperation capability (e.g. braking phases or downhill). In this study we aim to develop a green routing tool to encourage the use of EV. To overcome the autonomy limitation we propose a methodology for the EV that includes a charging stations location model, an energy graph construction for EV routing and a resolution scheme for the routing problem with recharging stage(s) along the way on the available charging stations.

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Section: B Proposed Methodologymentioning

confidence: 99%

Electric Vehicle green routing with possible en-route recharging

Baouche

Billot

Trigui³

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…Then, the results achieved by the proposed system are compared with the corresponding ones for three other systems. The first system is a traditional route algorithm for WSN that having no optimization [24], the second system uses an analytical optimization for WSNs having obstacles [25]. The third one uses particle swarm optimization technique [26].…”

Section: Applicability Of the Proposed System And Its Resultsmentioning

confidence: 99%

Optimizing the Routing of Wireless Sensor Networks for Obstacles-avoidance

Mohamed¹,

Nassar²

2017

IJCA

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In the recent years, wireless sensor networks (WSNs), have become essential part in a huge number of the modern applications. Researchers have developed a lot of work to improve their performance. But, practically WSNs still face with different kinds of obstacles those cause main challenges for their reliability. Therefore, finding an optimum obstacleavoiding route path for the WSNs is considered an important research problem. The present work introduces a new optimum routing algorithm based on the cluster-based method for the WSNs with obstacles. The proposed system uses the cluster-based method and the mobile sink to decrease the power consumptions and increase the lifetime of the WSNs. Besides, it uses the genetic algorithm to optimize the avoiding-obstacles routing path. Suggested system has been applied for a WSN used to communicate between a discoveryradiation robot and its operating system as a case of study. Simulation results for the tested WSN and their comparison with three other route algorithms have proved the effectiveness of the proposed novel method.

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“…prune ←true 5: end if 6: end for 7: return prune This idea is an extension of the infeasibility pruning strategy presented by Lozano and Medaglia [13] for the CSP. Other authors have proposed similar preprocessing procedures in the context of DP to solve other shortest path variants [5,14,17,23,24].…”

Section: Algorithmmentioning

confidence: 99%

An exact method for a class of robust shortest path problems with scenarios

Duque

Medaglia

2019

Networks

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In this variant of the robust shortest path problem, the cost of traversing an arc is given by a discrete set of scenarios. The problem is then to find a (robust) path that takes into account the information arising from the multiple cost realizations of the possible scenarios. To account for a robust path, we adopt the bw‐robustness criterion, which ameliorates the dramatic role played by worst‐case approaches. Under this criterion, the parameter b represents a desirable upper bound for the cost that the decision maker wants for most of the scenarios; while parameter w strictly bounds the cost and represents a value that the decision maker is not willing to exceed in any scenario. To solve the problem, we extend the pulse algorithm, a general‐purpose solution strategy that has been used on shortest path problems with side constraints. The proposed algorithm compares favorably against an integer programming approach both in terms of speed and scalability on networks with up to 39 377 nodes and 192 094 arcs.

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Solving shortest path problems with a weight constraint and replenishment arcs

Cited by 67 publications

References 30 publications

Electric Vehicle green routing with possible en-route recharging

Electric Vehicle green routing with possible en-route recharging

Optimizing the Routing of Wireless Sensor Networks for Obstacles-avoidance

An exact method for a class of robust shortest path problems with scenarios

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