Dial-a-Ride Problem with time windows, transshipments, and dynamic transfer points

Deleplanque, Samuel; Quilliot, Alain

doi:10.3182/20130619-3-ru-3018.00435

Cited by 9 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mitrović-Minić [1] proved the usefulness of transshipment points in the context of pickup and delivery problems with time windows. Deleplanque [2] introduced transshipment into traditional dial-a-ride problems (a variant of VRP problems) and solved it by an algorithm based on insertion techniques and constraints propagation [3].…”

Section: Literature Reviewsmentioning

confidence: 99%

Study on Customized Shuttle Transit Mode Responding to Spatiotemporal Inhomogeneous Demand in Super-Peak

2021

View full text Add to dashboard Cite

Instantaneous mega-traffic flow has long been one of the major challenges in the management of mega-cities. It is difficult for the public transportation system to cope directly with transient mega-capacity flows, and the uneven spatiotemporal distribution of demand is the main cause. To this end, this paper proposed a customized shuttle bus transportation model based on the “boarding-transfer-alighting” framework, with the goal of minimizing operational costs and maximizing service quality to address mega-transit demand with uneven spatiotemporal distribution. The fleet application is constructed by a pickup and delivery problem with time window and transfer (PDPTWT) model, and a heuristic algorithm based on Tabu Search and ALNS is proposed to solve the large-scale computational problem. Numerical tests show that the proposed algorithm has the same accuracy as the commercial solution software, but has a higher speed. When the demand size is 10, the proposed algorithm can save 24,000 times of time. In addition, 6 reality-based cases are presented, and the results demonstrate that the designed option can save 9.93% of fleet cost, reduce 45.27% of vehicle waiting time, and 33.05% of passenger waiting time relative to other existing customized bus modes when encountering instantaneous passenger flows with time and space imbalance.

show abstract

Section: Literature Reviewsmentioning

confidence: 99%

Study on Customized Shuttle Transit Mode Responding to Spatiotemporal Inhomogeneous Demand in Super-Peak

2021

View full text Add to dashboard Cite

show abstract

“…We allow latest date violation on all time windows. However, vehicles are not allowed: (1) to leave their associated depot, (2) to visit sites or (3) to handle demand, before the earliest date of respective time windows. Thus time windows constraints can be defined as [hard;soft].…”

Section: B Problem Formulationmentioning

confidence: 99%

“…The reader can refer to Masson, Lehuédé and Péton[7] for a general framework on DARP. Deleplanque and Quillot[2] allow the insertion of dynamic transfer points and studied an algorithm based on insertion techniques and constraints propagation.…”

mentioning

confidence: 99%

A greedy based algorithm for a bi-objective Pickup and Delivery Problem with Transfers

Godart

Manier

Bloch

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

View full text Add to dashboard Cite

Optimization of urban transport is an evergrowing research area, especially with the massive success of e-commerce, the substantial demographic increase in urban areas, but also exploring interesting trends such as flexible multimodal itineraries. This work presents an innovative multistage approach where optimization methods such as greedy algorithms are adapted to solve a bi-objective vehicle routing problem with pickup and deliveries in urban areas with transfers. We highlight the performance of our approach on several instances with various sizes and characteristics.

show abstract

“…The results prove savings due to transfers while minimizing the total distance traveled. [8] solve DARP with dynamic transfer points using insertion techniques and constraints propagation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We ensure with constraint (6) that each vehicle has to leave its associated vehicle depot if and only if this vehicle is being used anywhere in the graph, while constraint (7) forces this same vehicle to finish his route at this same vehicle depot. x A vehicle v or a request r on node i must only pass through one edge [i, j] for each visit number a to ensure one unique flow for vehicles (8) and for requests (9) departing from any node. Any given request can only enter once at most in each transfer point (10).…”

Section: E Constraintsmentioning

confidence: 99%

MILP for a Variant of Pickup & Delivery Problem for both Passengers and Goods Transportation

Godart

Manier

Bloch

et al. 2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

View full text Add to dashboard Cite

Pickup and delivery problems have been declined in many variants. In these optimization problems, a fleet of vehicles must satisfy customers' transport demand under specific constraints. This paper studies the feasibility of using a linear model including constraints that are not only related to the goods transport, but also combined with passengers transport in a more flexible way. We explore many characteristics simultaneously like transfers capabilities (with and without storage), time windows on sites, on transfer points and on vehicle depots, heterogeneous capacities and multiple visits. We use exact methods on some instances we created, using commercial solver IBM CPLEX TM. Results obtained show potential savings on two instances but also highlight the complexity and limits in terms of scalability.

show abstract

Dial-a-Ride Problem with time windows, transshipments, and dynamic transfer points

Cited by 9 publications

References 8 publications

Study on Customized Shuttle Transit Mode Responding to Spatiotemporal Inhomogeneous Demand in Super-Peak

Study on Customized Shuttle Transit Mode Responding to Spatiotemporal Inhomogeneous Demand in Super-Peak

A greedy based algorithm for a bi-objective Pickup and Delivery Problem with Transfers

MILP for a Variant of Pickup & Delivery Problem for both Passengers and Goods Transportation

Contact Info

Product

Resources

About