Integrating stochastic time-dependent travel speed in solution methods for the dynamic dial-a-ride problem

Schilde, Michael; Doerner, Karl F.; Hartl, Richard F.

doi:10.1016/j.ejor.2014.03.005

Cited by 117 publications

(49 citation statements)

References 38 publications

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“…For example, Figliozzi [13], [14] expressed the travel time on links as a function of the departure time which is calculated by constant or time dependent travel speed. Schilde et al [37] explored a dynamic dial-a-ride problem with stochastic time dependent travel speed. From the history data, they estimated the distribution of transportation requests by Poisson distribution, and the time between the request and the latest vehicle arrival time by Gamma distribution derived from the distribution of travel speeds and travel times.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

Kim

Ong

Cheong

et al. 2016

IEEE Trans. Intell. Transport. Syst.

102

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This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model to solve this problem and adopt a rollout-based approach to the solution, using approximate dynamic programming to avoid the curse of dimensionality. We also investigate how to estimate the probability distribution of travel times of arcs which, reflecting reality, are considered to consist of multiple road segments. Experiments are conducted using a real-world problem faced by Singapore logistics/delivery company and authentic road traffic information.Index Terms-Dynamic vehicle routing problem, approximate dynamic programming, uncertain travel times, rollout algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

Kim

Ong

Cheong

et al. 2016

IEEE Trans. Intell. Transport. Syst.

102

View full text Add to dashboard Cite

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“…Schilde et al . presented metaheuristic based solution approaches for dynamic dial‐a‐ride problem in a road network with stochastic and time dependent travel speeds.…”

Section: Related Literaturementioning

confidence: 99%

“…Xiang et al [14] developed an approach for scheduling dial-a-ride system in a city road network with time dependent and stochastic delays. Schilde et al [15] presented metaheuristic based solution approaches for dynamic dial-a-ride problem in a road network with stochastic and time dependent travel speeds.…”

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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“…The numerical results showed that the deterministic annealing algorithm is capable of obtaining better solutions for large scale instances under the acceptable computational time. Schilde et al [35] proposed four meta-heuristic methods including the dynamic VNS, the dynamic stochastic VNS, the multiple plan approach and the multiple scenario approach for dynamic DARP with consideration of the time-dependent travel speed. The block scheduling algorithm was applied to deal with time-dependent travel speeds.…”

Section: Heuristic Approachmentioning

confidence: 99%

“…Let A i,k ∈ {1, …, K}, i ∈ N be a notation which represents that request i has k alternatives (vehicle routes) that it can be inserted in, and satisfies the condition Δf i;A i;k ≤ Δf i;A i;k ' ; k ≤ k ′ ; i ∈ N . Compared with the basic greedy insertion heuristic, the Regret-K insertion heuristic selects the request to insert into the vehicle route according to Equation (35).…”

Section: Variablesmentioning

confidence: 99%

A revised branch‐and‐price algorithm for dial‐a‐ride problems with the consideration of time‐dependent travel cost

Chang

2014

J of Advced Transportation

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SUMMARYDeveloping demand responsive transit systems are important with regard to meeting the travel needs for elderly people. Although Dial-a-ride Problems (DARP) have been discussed for several decades, most researchers have worked to develop algorithms with low computational cost under the minimal total travel costs, and fewer studies have considered how changes in travel time might affect the vehicle routes and service sequences. Ignoring such variations in travel time when design vehicle routes and schedules might lead to the production of inefficient vehicle routes, as well as incorrect actual vehicle arrival times at the related nodes. The purpose of this paper is to construct a DARP formulation with consideration of time-dependent travel times and utilizes the traffic simulation software, DynaTAIWAN, to simulate the real traffic conditions in order to obtain the time-dependent travel time matrices. The branch-and-price approach is introduced for the time-dependent DARP and tested by examining the sub-network of Kaohsiung City, Taiwan. The numerical results reveal that the length of the time window can significantly affect the vehicle routes and quantitative measurements. As the length of the time window increases, the objective value and the number of vehicles will reduce significantly. However, the CPU time, the average pickup delay time, the average delivery delay time and the average actual ride time (ART)/direct ride time (DRT) will increase significantly as the length of the time window increases. Designing the vehicle routes to reduce operating costs and satisfy the requirements of customers is a difficult task, and a trade-off must be made between these goals.

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Integrating stochastic time-dependent travel speed in solution methods for the dynamic dial-a-ride problem

Cited by 117 publications

References 38 publications

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

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

A revised branch‐and‐price algorithm for dial‐a‐ride problems with the consideration of time‐dependent travel cost

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