A practical dynamic share-a-ride problem with speed windows for Tokyo city

Do, Phan-Thuan; Nghiem, Nguyen-Viet-Dung; Nguyen, Ngoc-Quang; Nguyen, Duc-Nghia

doi:10.1109/kse.2016.7758029

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Among the few models for PFIT systems is the SARP, an extension of the DARP introduced by Li et al [12] that allows taxis to transport one passenger and one parcel simultaneously. This problem has been further covered by Nguyen et al [16] and Do et al [8], where a taxi is able to carry one passenger and multiple parcels. A more flexible version of the SARP is the SARPLP [1], in which the vehicles consist of passenger compartments and parcel lockers, thus being able to serve several customers at once.…”

Section: Related Workmentioning

confidence: 99%

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The Share-A-Ride Problem with Integrated Routing and Design Decisions: The Case of Mixed-Purpose Shared Autonomous Vehicles

Tholen

Beirigo

Jovanova

et al. 2021

Lecture Notes in Computer Science

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The shared autonomous vehicle (SAV) is a new concept that meets the upcoming trends of autonomous driving and changing demands in urban transportation. SAVs can carry passengers and parcels simultaneously, making use of dedicated passenger and parcel modules on board. A fleet of SAVs could partly take over private transport, taxi, and last-mile delivery services. A reduced fleet size compared to conventional transportation modes would lead to less traffic congestion in urban centres. This paper presents a method to estimate the optimal capacity for the passenger and parcel compartments of SAVs. The problem is presented as a vehicle routing problem and is named variable capacity share-a-ride-problem (VCSARP). The model has a MILP formulation and is solved using a commercial solver. It seeks to create the optimal routing schedule between a randomly generated set of pick-up and dropoff requests of passengers and parcels. The objective function aims to minimize the total energy costs of each schedule, which is a trade-off between travelled distance and vehicle capacity. Different scenarios are composed by altering parameters, representing travel demand at different times of the day. The model results show the optimized cost of each simulation along with associated routes and vehicle capacities.

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Section: Related Workmentioning

confidence: 99%

“…Vehicles must first complete the pick-up of a request before the drop-off, which is guaranteed by (7). Equation (8) ensures that arrival at the pick-up nodes is on time and within the required time window. The vehicle load or weight after each node is defined by Eq.…”

Section: Model Formulationmentioning

confidence: 99%