Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm

Li, Xiangyong; Tian, Peng; Leung, Stephen C. H.

doi:10.1016/j.ijpe.2010.01.013

Cited by 243 publications

(138 citation statements)

References 20 publications

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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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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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“…Due to the recognized practical importance of incorporating uncertainty, the uncertain version of routing problems has also attracted increasing attention. Researchers have formulated various problems depending on the uncertainty under consideration; for example, uncertainty in customer presence (see for instance, Jaillet, 1988;Jaillet and Odoni, 1988;Campbell and Thomas, 2008), uncertainty in demand (see for instance, Bertsimas, 1992;Bertsimas and Simchi-Levi, 1996;Sungur et al, 2008;Gounaris et al, 2013), and uncertainty in travel time (see for instance, Russell and Urban, 2008;Chang et al, 2009;Li et al, 2010). A comprehensive overview can be found in Cordeau et al (2007), Häme and Hakula (2013).…”

Section: Related Workmentioning

confidence: 99%

“…A chance constrained programming model minimizes the transportation cost while guaranteeing that the arrival times are within the time windows with a pre-specified probability (Jula et al, 2006;Chang et al, 2009;Mazmanyan and Trietsch, 2009;Li et al, 2010). This approach is insensitive to the extent of time window violations and may rule out desirable solutions.…”

Section: Related Workmentioning

confidence: 99%

Routing Optimization Under Uncertainty

Jaillet

Sim

2016

Operations Research

111

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We consider a class of routing optimization problems under uncertainty in which all decisions are made before the uncertainty is realized. The objective is to obtain optimal routing solutions that would, as much as possible, adhere to a set of specified requirements after the uncertainty is realized. These problems include finding an optimal routing solution to meet the soft time window requirements at a subset of nodes when the travel time is uncertain, and sending multiple capacitated vehicles to different nodes to meet the customers' uncertain demands. We introduce a precise mathematical framework for defining and solving such routing problems. In particular, we propose a new decision criterion, called the Requirements Violation (RV) Index, which quantifies the risk associated with the violation of requirements taking into account both the frequency of violations and their magnitudes whenever they occur. The criterion can handle instances when probability distributions are known, and ambiguity, when distributions are partially characterized through descriptive statistics such as moments information. We develop practically efficient algorithms involving Benders decomposition to find the exact optimal routing solution in which the RV Index criterion is minimized, and give numerical results from several computational studies that show the attractive performance of the solutions.

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“…They discussed several models with different recourses and proposed a heuristic to solve them. A more general case when time windows are imposed were considered in some studies (e.g., Russell and Urban (2008), Li et al (2010), Sungur et al (2010) and Taş et al (2013)). Since these problems are highly complicated, these studies limit themselves to the development of heuristics.…”

Section: Introductionmentioning

confidence: 99%

Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines

Adulyasak

Jaillet

2016

Transportation Science

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We consider the vehicle routing problem with deadlines under travel time uncertainty in the contexts of stochastic and robust optimization. The problem is defined on a directed graph where a fleet of vehicles is required to visit a given set of nodes and deadlines are imposed at a subset of nodes. In the stochastic vehicle routing problem with deadlines (SVRP-D), the probability distribution of the travel times is assumed to be known and the problem is solved to minimize the sum of probability of deadline violations. In the robust vehicle routing problem with deadlines (RVRP-D), however, the exact probability distribution is unknown but it belongs to a certain family of distributions. The objective of the problem is to optimize a performance measure, called lateness index, which represents the risk of violating the deadlines. Although novel mathematical frameworks have been proposed to solve these problems, the size of problem that those approaches can handle is relatively small. Our focus in this paper is the computational aspects of the two solution schemes. We introduce formulations that can be applied for the problems with multiple capacitated vehicles and discuss the extensions to the cases of incorporating service times and soft time windows. Furthermore, we develop an algorithm based on a branch-and-cut framework to solve the problems. The experiments show that these approaches provide substantial improvements in computational efficiency compared to the approaches in the literature.

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Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm

Cited by 243 publications

References 20 publications

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty

Routing Optimization Under Uncertainty

Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines

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