The time-dependent vehicle routing problem with soft time windows and stochastic travel times

Taş, Duygu; Dellaert, Nico; Woensel, Tom Van; Kok, A.G. de

doi:10.1016/j.trc.2014.08.007

Cited by 107 publications

(38 citation statements)

References 33 publications

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“…Traffic time can be affected by different accidents such as raining, car accident and other reasons (Duan et al, 2015). There are few studies considering both stochastic and time dependency (Lecluyse et al, 2009;Taş et al, 2014;Sun et al, 2015;Zhang et al, 2016). The green objective is directly associated with the elapsed time in traffic, thus considering this objective together makes sense.…”

Section: Introductionmentioning

confidence: 99%

A stochastic time-dependent green capacitated vehicle routing and scheduling problem with time window, resiliency and reliability: a case study

Rabbani¹,

Bosjin²,

Yazdanparast³

et al. 2018

10.5267/j.dsl

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This paper presents a new multi-objective model for a vehicle routing problem under a stochastic uncertainty. It considers traffic point as an inflection point to deal with the arrival time of vehicles. It aims to minimize the total transportation cost, traffic pollution, customer dissatisfaction and maximizes the reliability of vehicles. Moreover, resiliency factors are included in the model to increase the flexibility of the system and decrease the possible losses that may impose on the system. Due to the NP-hardness of the presented model, a metaheuristic algorithm, namely Simulated Annealing (SA) is developed. Furthermore, a number of sensitivity analyses are provided to validate the effectiveness of the proposed model. Lastly, the foregoing meta-heuristic is compared with GAMS, in which the computational results demonstrate an acceptable performance of the proposed SA. .

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Section: Introductionmentioning

confidence: 99%

A stochastic time-dependent green capacitated vehicle routing and scheduling problem with time window, resiliency and reliability: a case study

Rabbani¹,

Bosjin²,

Yazdanparast³

et al. 2018

10.5267/j.dsl

View full text Add to dashboard Cite

show abstract

“…Research dedicated to the stochastic time-dependent vehicle routing problem with time windows or STD-VRPTW can be found in [88,128,130]. In Lecluyse et al [88] the time-dependent travel times are the result of a stochastic process due to traffic congestion.…”

Section: Literature Surveymentioning

confidence: 99%

“…The proposed queueing models are solved for problem instances with up to 80 vertices by an algorithm based on a TS method. In the problem formulation discussed by Taş et al [130] vertices have soft time windows. The authors propose TS and an adaptive large neighbourhood search to solve this problem.…”

Section: Literature Surveymentioning

confidence: 99%

“…The distributions of the travel times most commonly applied in the field of vehicle routing problems to solve road logistic problems are uniform, normal, lognormal, shifted gamma and gamma distributions ( [21,43,71,88,91,106,116,[129][130][131]134]). The gamma distribution is the most commonly used ( [21,43,88,106,116,[129][130][131]).…”

Section: -15mentioning

confidence: 99%

“…The gamma distribution is the most commonly used ( [21,43,88,106,116,[129][130][131]). These distributions can be estimated using different data sources such as floating car data, automatic vehicle identification and inductive loop detectors ( [95,110,157,160]).…”

Section: -15mentioning

confidence: 99%

See 2 more Smart Citations

Optimizing practical orienteering problems with stochastic time-dependent travel times: towards congestion free routes

Verbeeck¹

2016

4OR-Q J Oper Res

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Reasoning and querying bounds on differences with layered preferences

et al. 2021

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Bounds on differences are widely used in AI to model binary constraints regarding different dimensions, such as time, space, costs, calories, etc. Representing and reasoning with them is an important task in several areas such as knowledge representation, scheduling and planning. Researchers are increasingly focusing on the treatment of fuzzy or probabilistic constraints, to deal with preferences and/or uncertainty. Current approaches to constraints with preferences focus on the evaluation of the optimal (i.e., with highest preference) solutions for the set of constraints and propose a wide range of alternative operators to combine preferences within constraint propagation. However, in decision support tasks, finding a specific (though optimal) solution is not the main goal, but rather it is more important to identify the "space of solutions" (i.e., the minimal network) with their preferences, and to provide users with query answering mechanisms to explore it. We propose the first approach that addresses such a need by (i) supporting user-defined layered scales of preferences (e.g., Low, Medium, High, Very High), (ii) proposing a family of extensions of bounds on differences constraints to deal with such layered preferences, (iii) defining a family of reasoning algorithms to evaluate the minimal network, which is parametric with respect to the basic operations to combine preferences (and the scale of preferences), and (iv) providing suitable query-answering facilities. The properties of the family of approaches are also analyzed.

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The time-dependent vehicle routing problem with soft time windows and stochastic travel times

Cited by 107 publications

References 33 publications

A stochastic time-dependent green capacitated vehicle routing and scheduling problem with time window, resiliency and reliability: a case study

A stochastic time-dependent green capacitated vehicle routing and scheduling problem with time window, resiliency and reliability: a case study

Optimizing practical orienteering problems with stochastic time-dependent travel times: towards congestion free routes

Reasoning and querying bounds on differences with layered preferences

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