Online Proactive Multi-Task Assignment with Resource Availability Anticipation
D\'eborah Conforto Nedelmann, J\'er\^ome Lacan, Caroline Chanel

TL;DR
This paper introduces a proactive, receding-horizon approach for online multi-task assignment that anticipates resource availability without assuming future request locations, improving efficiency and task allocation.
Contribution
It presents a novel proactive multi-task assignment method using a genetic algorithm that does not rely on future request location assumptions, enhancing online optimization performance.
Findings
Outperforms reactive methods in cost and task allocation
Reduces resource idle time in benchmark tests
Effective in synthetic and real-world scenarios
Abstract
With the emergence of services and online applications as taxi dispatching, crowdsourcing, package or food delivery, industrials and researchers are paying attention to the online multi-task assignment optimization field to quickly and efficiently met demands. In this context, this paper is interested in the multi-task assignment problem where multiple requests (e.g. tasks) arrive over time and must be dynamically matched to (mobile) agents. This optimization problem is known to be NP-hard. In order to treat this problem with a proactive mindset, we propose to use a receding-horizon approach to determine which resources (e.g. taxis, mobile agents, drones, robots) would be available within this (possibly dynamic) receding-horizon to meet the current set of requests (i.e. tasks) as good as possible. Contrarily to several works in this domain, we have chosen to make no assumption…
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