Resilient Fleet Management Strategy for Energy-Aware Intra-Factory Logistics

Основные понятия

Utilizing prior knowledge of the search space enhances fleet management resilience in real-time operations.

Аннотация

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.

Статистика

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.

Цитаты

"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."

Ключевые выводы из

Resilient Fleet Management for Energy-Aware Intra-Factory Logistics

by Mithun Gouth... в arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11034.pdf

Дополнительные вопросы

How can the proposed algorithm handle larger fleets and more complex combinatorial problems

The proposed algorithm can handle larger fleets and more complex combinatorial problems by leveraging the prior knowledge stored in the search tree. As the fleet size increases, traditional exact methods become computationally expensive due to the NP-hard nature of the problem. However, by utilizing a Monte Carlo Tree Search (MCTS) algorithm offline to generate a nominal search tree with cost estimates for task assignment decisions, the algorithm can efficiently explore a vast solution space. When perturbations occur in real-time operations, this prior knowledge is crucial for rapidly adapting to changes without having to solve an entirely new problem from scratch. The re-utilization of the search tree topology and cost estimates allows for quick updates and feasible solutions even in scenarios involving larger fleets or more intricate combinatorial problems.

What are the limitations of using metaheuristic algorithms compared to exact methods in fleet management optimization

While metaheuristic algorithms like simulated annealing, genetic algorithms, and tabu search offer computational efficiency and speed in solving fleet management optimization problems compared to exact methods, they come with certain limitations. One key limitation is that metaheuristics do not provide guarantees on convergence or optimality of solutions obtained. This lack of certainty may lead to suboptimal solutions when dealing with complex fleet management scenarios where precision is critical. Additionally, metaheuristic algorithms rely heavily on parameter tuning which can be time-consuming and require domain expertise. In contrast, exact methods ensure optimal solutions but are often computationally intensive and impractical for real-time decision-making processes in dynamic environments.

How can transfer learning be further integrated into the algorithm to adapt to disruptions not seen during training

To further integrate transfer learning into the algorithm for handling disruptions not seen during training, additional mechanisms can be implemented within the online adaptation process. One approach could involve incorporating reinforcement learning techniques that allow the system to learn from new disruptions encountered during operation and update its strategies accordingly. By continuously updating cost estimates based on real-world perturbations experienced by the fleet over time, the algorithm can adapt dynamically without requiring extensive retraining each time a novel disruption occurs.