When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms

Mofidi, Seyed Shahab; Pazour, Jennifer A.

doi:10.1016/j.trb.2019.05.008

Cited by 32 publications

(2 citation statements)

References 60 publications

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“…This leads to a challenging optimization problem considering the interplay between assignments to crowdsourced drivers and routing and repositioning of the company fleet. Another way to hedge against driver rejections was recently introduced by [63]. The authors suggest offering drivers menus of jobs to select from.…”

Section: Crowdsourced Transportationmentioning

confidence: 99%

Directions for future research on urban mobility and city logistics

2022

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This survey article provides an overview on future directions for research in urban mobility and city logistics. It sets a focus on three particularly serious changes in the business models: vehicle autonomy, crowdsourced logistics, and urban micro‐consolidation centers. In the future, service fleets might fully or partially be autonomous which brings new operational opportunities and challenges. In many business models, crowdsourcing jobs are already common. While this might save costs, it also leads to uncertainty in the available workforce and their behavior. Finally, micro‐consolidation centers enable the use of smaller, cheaper, and emission‐friendlier vehicles, but lead to more complex planning and operations. For each topic, the article presents an overview on the relevant literature as well as important and open research challenges.

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Section: Crowdsourced Transportationmentioning

confidence: 99%

Directions for future research on urban mobility and city logistics

2022

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“…However, one-to-many matching cannot recommend multiple vehicle and cargo sources for both drivers and shippers simultaneously. Clearly, many-to-many matching would be beneficial for expanding the scope of matching selection in real logistics activities, increasing the percentage of successful transactions between vehicles drivers and cargo owners, and maximizing the utilization of transport resources [8].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Parameter Controlled Ant Colony Optimization Approach for Peer-to-Peer Vehicle and Cargo Matching

Ling

Mao

et al. 2021

IEEE Access

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With the development of the sharing economy, a lot of vehicle-cargo matching service platforms have been built to reduce supply-request information asymmetry and enhance the use of idle vehicles. It is a critical but challenging task for a logistics service platform to create matches automatically between vehicles and cargos and achieve high vehicle utilization. Conventional vehicle-cargo matching such as one-to-one matching and one-to-many matching cannot recommend multiple vehicle and cargo sources for both drivers and shippers simultaneously. In this article, we investigate an intelligent approach to generate many-to-many vehicle-cargo matches and recommend them to the corresponding cargo owners and vehicle drivers. To this end, we develop a mathematical model for maximizing the platform's matching rate and matching profit and propose an innovative ant colony optimization based on parameter tuning to solve the matching problem. A clustering technology combining bacterial foraging chemotaxis with the k-means algorithm is used to judge the state of the ant colony, and the parameters are adjusted adaptively to make the algorithm converge rapidly to the neighborhood of the global optimal solution. We then apply the randomicity and ergodicity of chaos to adjust the parameters, aiming to jump out of local optimum. Numerical results show that the proposed algorithm can achieve recommendation results that are accurate and stable compared with those of some other search methods and can provide satisfactory solutions for vehicle drivers and cargo owners.INDEX TERMS Ant colony optimization, bacterial foraging clustering algorithm, parameter tuning, vehicle-cargo matching, chaos.

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Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platforms

Horner,

Pazour,

Mitchell

2024

Naval Research Logistics

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This article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted.

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When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms

Cited by 32 publications

References 60 publications

Directions for future research on urban mobility and city logistics

Directions for future research on urban mobility and city logistics

An Adaptive Parameter Controlled Ant Colony Optimization Approach for Peer-to-Peer Vehicle and Cargo Matching

Increasing driver flexibility through personalized menus and incentives in ridesharing and crowdsourced delivery platforms

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