17 records found
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Crowd-Sourced Delivery Systems (CDS) depend on occasional drivers to deliver parcels directly to online customers. These freelance drivers have the flexibility to accept or reject orders from the platform, leading to a stochastic and often unstable matching process for delivery a
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Vehicle proactive guidance strategies are used by ride-hailing platforms to mitigate supply–demand imbalance across regions by directing idle vehicles to high-demand regions before the demands are realized. This article presents a data-driven stochastic optimization framework for
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Crowd-Sourced Delivery Services (CDS) use in-store customers, as crowd-shippers, to deliver online orders directly to other customers. As independent contractors, the crowd-shippers are free to decide whether to accept or reject the online orders assigned by the retailer. High or
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Freelance drivers in ride-hailing systems may strategically accept or reject ride requests based on their projection of the profitability of the assigned rides. This driver acceptance uncertainty is mainly caused by the flat rate payment and the blind ride acceptance rule adopted
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This paper proposes an integrated dispatching framework for matching drivers with riders in ride-hailing systems. The goal is to compute matching solutions that maximize social welfare and benefit both sides of the market, such that the sustainable growth of the ride-hailing syst
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Providing high-quality matching between drivers and riders is imperative for sustaining the growth of ride-sharing platforms. A user-focused matching mechanism design plays a key role in terms of ensuring user satisfaction. In this paper, we consider the matching problem in the c
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To reduce the vehicle relocation rate considering relieving disequilibrium of the supply-demand ratios across regions for car-sharing systems, in this paper, we propose a data-driven optimization framework by integrating the non-parametric learning algorithm and two-stage stochas
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The unprecedented growth of demand for charging electric vehicles (EVs) calls for novel expansion solutions to today’s charging networks. Riding on the wave of the proliferation of sharing economy, Airbnb-like charger sharing markets open the opportunity to expand the existing ch
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In ride-sharing services, travel time uncertainty significantly impacts the quality of matching solutions for both the drivers and the riders. This paper studies a one-to-many ride-sharing matching problem where travel time between locations is uncertain. The goal is to generate
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In this paper, we propose a data-driven robust optimization model to reduce total travel cost in ride-sharing systems under travel time uncertainty. Instead of using a pre-defined uncertainty set, we study a data-driven robust optimization approach that integrates gated recurrent
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As an alternative to traditional taxi services, Transportation Network Companies (TNCs) such as Uber and Lyft are playing an increasingly important role in the paradigm shifting from car ownership to mobility as a service. We consider an electric vehicle fleet charging scheduling
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Order dispatching in ride-sharing platform under travel time uncertainty
A data-driven robust optimization approach
In this paper, we study a one-to-one matching ride-sharing problem to save the travellers' total travel time considering travel time uncertainty. Unlike the existing work where the uncertainty set is assumed to be known or roughly estimated, in this work, we propose a learning-ba
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We propose a data-driven optimization model to reduce riders' wait time for vehicle guidance and rebalancing operations, considering the rider demands are under uncertainty. Instead of assuming a pre-defined rider demand distribution, we propose a data-driven framework that integ
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We propose a learning-based approach for open driver guidance and rebalancing in ride-hailing platforms. The objective is to further enhance the wait time reduction benefit of batched matching by incorporating learning-based open driver guidance and rebalancing. By leveraging the
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Consider a decentralized electric vehicle (EV) charging scheduling problem where the chargers and vehicles are modeled as utility maximizing agents. To schedule chargers to vehicles in a day-ahead market, we propose a double auction mechanism, where chargers report their time ava
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Coordinating patient preferences through automated negotiation
A multiagent systems model for diagnostic services scheduling
This paper presents a multiagent systems model for patient diagnostic services scheduling. We assume a decentralized environment in which patients are modeled as self-interested agents who behave strategically to advance their own benefits rather than the system wide performance.
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This paper proposes a negotiation protocol for outpatient diagnostic services scheduling in a collaborative setting where each patient has private preference values over available service time slots and the overall schedule is achieved through negotiation among patients. With the
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