Core Concepts
Utilizing prior knowledge of the search space enhances fleet management resilience in real-time operations.
Abstract
The paper introduces a novel fleet management strategy for battery-powered robot fleets engaged in intra-factory logistics. It addresses uncertainties like blocked passages and equipment malfunctions by proposing a two-step methodology. The first step involves solving the nominal problem using a Monte Carlo Tree Search algorithm, creating a nominal search tree. When disruptions occur, this tree is updated with costs to generate feasible solutions in real-time. Centralized fleet management considers all robots for task reassignment, unlike decentralized approaches that focus on affected robots only. Metaheuristic algorithms are used for quick improvements, but they lack guarantees on optimality. The proposed method reuses the search tree topology and cost estimates when recomputing policies for perturbed problems, enabling rapid adaptation to changes without solving entirely new problems each time.
Stats
Computational experiments required 370 hours of processing time each.
Battery capacity changed from 20 kJ to 16 kJ.
Payload capacities varied from 10 to 6 commodities.
Quotes
"The NP-hard nature of the task assignment problem has led to numerous decentralized approaches focusing on reassigning tasks only to affected robots."
"Centralized fleet management harnesses collective resilience by considering all available robots for task reassignment."
"The proposed method utilizes prior knowledge of the search space when there is a change in the nominal task definition."