Deep reinforcement learning for optimal resource allocation (II)

As vending machines become increasingly intelligent, enabling real-time updates of stock levels, the manual task of restocking remains a logistical challenge. This paper addresses the efficient restocking of vending machines via the Capacitated Vehicle Routing Problem (CVRP), focusing on optimiz...

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Bibliographic Details
Main Author: Uday, Nihal Arya
Other Authors: Zhang Jie
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/174967
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Institution: Nanyang Technological University
Language: English
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Summary:As vending machines become increasingly intelligent, enabling real-time updates of stock levels, the manual task of restocking remains a logistical challenge. This paper addresses the efficient restocking of vending machines via the Capacitated Vehicle Routing Problem (CVRP), focusing on optimizing routes for limited-capacity vehicles to meet demand without exceeding capacity, while minimizing costs. Traditional heuristics such as LKH-3 have shown robust performance in CVRP but face limitations in scalability and adaptability. This study compares two advanced learning-based approaches—L2D, employing deep reinforcement learning, and NCO, with its innovative light encoder and heavy decoder architecture—against the LKH-3 algorithm. Through detailed experimentation, we evaluate their scalability, computational efficiency, and solution quality. Our findings reveal that while L2D and NCO exhibit superior generalization capabilities and demonstrate promising scalability to large-scale problem instances, nuances in performance and efficiency metrics highlight their respective strengths and areas for improvement. The comparative analysis not only underscores the potential of learning-based models in overcoming the limitations of traditional heuristics but also delineates the path for future research in integrating the computational intelligence of machine learning with the intuitive problem-solving prowess of heuristic algorithms. This synthesis aims to pave the way for innovative solutions to CVRP and other combinatorial optimization challenges, marking a significant stride toward leveraging artificial intelligence in operational research.