Optimizing road network daily maintenance operations with stochastic service and travel times

Chen, Lu; Hà, Minh Hoàng; Gendreau, Michel

doi:10.1016/j.tre.2014.02.002

Cited by 40 publications

(33 citation statements)

References 31 publications

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“…A similar approach has been independently developed and used by Chen, Hoàng Hà, Langevin, and Gendreau (2014), to linearize chance constraints for a model that optimizes road network daily maintenance operations with stochastic service and travel times. We are now ready to state the complete formulation for the SBRP.…”

Section: Problem Definitionmentioning

confidence: 99%

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

Erdoğan

Battarra

Calvo

2015

European Journal of Operational Research

171

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a b s t r a c tBicycle sharing systems can significantly reduce traffic, pollution, and the need for parking spaces in city centers. One of the keys to success for a bicycle sharing system is the efficiency of rebalancing operations, where the number of bicycles in each station has to be restored to its target value by a truck through pickup and delivery operations. The Static Bicycle Rebalancing Problem aims to determine a minimum cost sequence of stations to be visited by a single vehicle as well as the amount of bicycles to be collected or delivered at each station. Multiple visits to a station are allowed, as well as using stations as temporary storage. This paper presents an exact algorithm for the problem and results of computational tests on benchmark instances from the literature. The computational experiments show that instances with up to 60 stations can be solved to optimality within 2 hours of computing time.

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Section: Problem Definitionmentioning

confidence: 99%

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

Erdoğan

Battarra

Calvo

2015

European Journal of Operational Research

171

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“…For instance, in their pioneering VRPSTT work Laporte, Louveaux, and Mercure (1992) assume that the travel times are independent and normally distributed random variables and exploit the fact that the convolution of normal random variables is again normally distributed. A similar assumption is made by Kenyon and Morton (2003), Figliozzi (2010), and Chen et al (2014). Taş et al (2013Taş et al ( , 2014 model travel times using the family of gamma distributions, which is also an additive family.…”

Section: Introductionmentioning

confidence: 94%

On Modeling Stochastic Travel and Service Times in Vehicle Routing

Gómez

Mariño

Akhavan-Tabatabaei

et al. 2016

Transportation Science

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“…It covers a variety of approaches that are used to solve logistic problems under uncertainty, including a number of papers in which the servicing time is uncertain. These include Li et al [19], Lei et al [20], and Chen et al [21] who all assumed normal distribution service time. The aforementioned papers by Tas et al [16] and Russell and Urban [17] use gamma and Erlang distributions for travel times, respectively, while Gomez [22] modelled variable travel times using a wide range of distributions, which included Erlang, Burr, and lognormal distributions.…”

Section: Calculating the Probability Of Carrying Out Successful Maintmentioning

confidence: 99%

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

2018

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This paper for the first time captures the impact of uncertain maintenance action times on vessel routing for realistic offshore wind farm problems. A novel methodology is presented to incorporate uncertainties, e.g., on the expected maintenance duration, into the decision-making process. Users specify the extent to which these unknown elements impact the suggested vessel routing strategy. If uncertainties are present, the tool outputs multiple vessel routing policies with varying likelihoods of success. To demonstrate the tool's capabilities, two case studies were presented. Firstly, simulations based on synthetic data illustrate that in a scenario with uncertainties, the cost-optimal solution is not necessarily the best choice for operators. Including uncertainties when calculating the vessel routing policy led to a 14% increase in the number of wind turbines maintained at the end of the day. Secondly, the tool was applied to a real-life scenario based on an offshore wind farm in collaboration with a United Kingdom (UK) operator. The results showed that the assignment of vessels to turbines generated by the tool matched the policy chosen by wind farm operators. By producing a range of policies for consideration, this tool provided operators with a structured and transparent method to assess trade-offs and justify decisions.

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Optimizing road network daily maintenance operations with stochastic service and travel times

Cited by 40 publications

References 31 publications

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

An exact algorithm for the static rebalancing problem arising in bicycle sharing systems

On Modeling Stochastic Travel and Service Times in Vehicle Routing

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

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