Demand control model with combinatorial incentives and surcharges for one-way carsharing operation

Wang, Lei; Ma, Wanjing; Wang, Meng; Qu, Xiaobo

doi:10.1016/j.trc.2021.102999

Cited by 16 publications

(8 citation statements)

References 31 publications

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“…A learning algorithm is developed for both estimating the expected future demands and determining the best discounts. Wang et al (2021a) propose a method to determine both the incentives and the surcharges according to the demand patterns, in OW CSSs, maximizing the total revenue due to the users satisfied and the losing incentives or gaining surcharges. The proposed approach is tested by using the database of EVCARD (a Chinese CS company in Shanghai).…”

Section: User-based Vehicle Relocationmentioning

confidence: 99%

A survey on emergent trends in the optimization of car‐sharing systems

Bruglieri

Pisacane

2023

Int Trans Operational Res

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This paper reviews the most recent literature on the optimization of car‐sharing systems. Unlike other surveys, we do not focus on a single aspect of car‐sharing systems, but we consider a wide range of optimization problems with a global view. Our aim is threefold. First, we revise the classic decision problems arising in car‐sharing systems with conventional and electric vehicles, such as the fleet size, the station location, and the vehicle relocation. Then, we discuss some of the most recent decision problems arising in more innovative car‐sharing systems, like those with autonomous vehicles, with mixed fleets, and the multi‐modal and the time‐dependent ones. Finally, we focus on new perspectives arising in the optimization of some innovative car‐sharing services that are not yet (or little) investigated in the literature and that are worthy of attention from the operations research point of view. For each of them, we also highlight new and challenging open decision problems that deserve academic attention.

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Section: User-based Vehicle Relocationmentioning

confidence: 99%

A survey on emergent trends in the optimization of car‐sharing systems

Bruglieri

Pisacane

2023

Int Trans Operational Res

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“…Through the incentivization of customers and a predictive model for the state of the system, Stokkink and Geroliminis (2021) developed a user-based vehicle relocation approach to determine the optimal incentive as a trade-off between the cost of an incentive and the expected omitted demand loss. To mitigate the demand and supply imbalance problem and increase profits by means of combinatorial monetary incentives and surcharges, Wang et al (2021a) As summarized in Table 5 , in total, only six studies factored the user-based vehicle relocation problem for CSSs. This may indicate that there is a lot of room for future research on this problem.…”

Section: User-based Vehicle Relocationmentioning

confidence: 99%

Modeling and optimization for carsharing services: A literature review

2022

Multimodal Transportation

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“…For instance, Pfrommer, Warrington, Schildbach, and Morari (2014) consider shared mobility systems and propose a combined strategy for routing staff-based vehicles and for fixing real-time price incentives for customers, based on MPC principles. In Wang, Ma, Wang, and Qu (2021), the problem of relocation of vehicles in one-way car sharing systems is addressed by proposing an optimisation algorithm to define monetary incentives and surcharges at different stations and times of day.…”

Section: Road Pricing and Incentives For Sustainable Mobilitymentioning

confidence: 99%