Online In-Route Task Selection in Spatial Crowdsourcing

Costa, Camila F.; Nascimento, Mário A.

doi:10.1145/3397536.3422215

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Task assignment is typically modeled as a bipartite matching problem, where vertices from two sides represented tasks and workers, respectively [20]. Various optimization goals have been investigated, including maximizing the matching edge weights [21], minimizing the detour distance [22] and load balancing [23], [24]. Alternatively, task assignment can be viewed as a selection problem where the objective is to maximize the number of fulfilled tasks [17], [25].…”

Section: B Task Assignment and Planning In Spatial Crowdsourcingmentioning

confidence: 99%

“…Although these formulations are aligned with different spatial crowdsourcing applications such as ride hailing [21], [24], geographical data generation [17], [22], [25] and workload distribution [23], [24], they cannot be trivially extended into our problem setting. This is because spatial crowdsourcing confines the assignment decision into a batch or assume an expiration of each task.…”

Section: B Task Assignment and Planning In Spatial Crowdsourcingmentioning

confidence: 99%

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Adaptive Task Planning for Large-Scale Robotized Warehouses

Shi¹,

Tong²,

Zhou³

et al. 2022

Preprint

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Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire item distribution pipeline, known as a fulfilment cycle. Direct extensions from state-of-the-art path finding methods are ineffective to solve the TPRW problem because they fail to adapt to the bottleneck variations of fulfillment cycles. In response, we propose Efficient Adaptive Task Planning, a framework for large-scale robotized warehouses with time-varying item arrivals. It adaptively selects racks to fulfill at each timestamp via reinforcement learning, accounting for the time-varying bottleneck of the fulfillment cycles. Then it finds paths for robots to transport the selected racks. The framework adopts a series of efficient optimizations on both time and memory to handle large-scale item throughput. Evaluations on both synthesized and real data show an improvement of 37.1% in effectiveness and 75.5% in efficiency over the state-of-the-arts.

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Section: B Task Assignment and Planning In Spatial Crowdsourcingmentioning

confidence: 99%

Section: B Task Assignment and Planning In Spatial Crowdsourcingmentioning

confidence: 99%

Adaptive Task Planning for Large-Scale Robotized Warehouses

Shi¹,

Tong²,

Zhou³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…They used the Lagrangian relaxation method. Costa and Nascimento (2020) proposed two heuristic approaches, one is based on local optimizations, and the other one is based on incremental solutions to solve the the Online In-Route Task Selection (Online-IRTS) problem. Yuen et al (2015) introduced a new framework for crowdsourcing activities to enable employees to continue working on long-term crowdsourcing projects.…”

Section: Spatial Crowdsourcing Platformmentioning

confidence: 99%

GRASP-Tabu Search Algorithms for the Route Planning Problem in Spatial Crowdsourcing

Bouatouche

Belkadi

2022

International Journal of Applied Metaheuristic Computing

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With the speedy progress of mobile devices, a lot of commercial enterprises have exploited crowdsourcing as a useful approach to gather information to develop their services. Thus, spatial crowdsourcing has appeared as a new platform in e-commerce and which implies procedures of requesters and workers. A requester submits spatial tasks request to the workers who choose and achieve them during a limited time. Thereafter, the requester pays only the worker for the well accomplished the task. In spatial crowdsourcing, each worker is required to physically move to the place to accomplish the spatial task and each task is linked with location and time. The objective of this article is to find an optimal route to the worker through maximizing her rewards with respecting some constraint, using an approach based on GRASP with Tabu. The proposed algorithm is used in the literature for benchmark instances. Computational results indicate that the proposed and the developed algorithm is competitive with other solution approaches.

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