Saeid Amiri, Danial Dervovic, Parisa Zehtabi, Michael Cashmore

Surrogate-Assisted Monte-Carlo Tree Search in Facility Location and Beyond (Extended Abstract)

Introduction

Surrogate-assisted monte-carlo tree search in facility location and beyond (extended abstract). Explore Surrogate-Assisted Monte-Carlo Tree Search (MCTS) for complex combinatorial problems like facility location. Achieve faster, consistent solutions by leveraging surrogate models for expensive evaluations.

Abstract

Combinatorial problems abound in industry. A persistent issue encountered using search-based solutions is that evaluating particular nodes may be expensive. As an example, organisations frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits, which may be formulated as a combinatorial search problem. In this extended abstract, we examine a particular class of facility location problems, where the objective is to minimize the loss of sales resulting from the removal of several retail stores. However, estimating sales accurately is expensive and time-consuming. To overcome this challenge, we leverage Monte-Carlo Tree Search assisted by a surrogate model that computes evaluations faster. Initial results suggest that MCTS supported by a fast surrogate function can generate solutions faster while maintaining a solution consistent with non-assisted MCTS.

Review

This extended abstract presents a compelling approach to tackling computationally expensive combinatorial optimization problems, specifically focusing on a class of facility location challenges. The core contribution lies in integrating a fast surrogate model with Monte-Carlo Tree Search (MCTS) to accelerate the evaluation of nodes within the search tree. By addressing the bottleneck of costly objective function evaluations, the authors aim to provide a more efficient mechanism for generating solutions in real-world scenarios, such as optimizing retail networks by minimizing sales loss from store closures. This framework holds significant promise for improving the practical applicability of search-based solutions in industrial settings. The problem addressed is highly relevant, as many practical optimization tasks, including facility location, demand rapid decision-making but involve complex, time-consuming simulations or data analyses to assess the quality of potential solutions. The proposed method of leveraging surrogate models to assist MCTS is a theoretically sound and intuitive strategy, combining the strengths of intelligent search with the efficiency of predictive modeling. The preliminary results, suggesting that the surrogate-assisted MCTS can "generate solutions faster while maintaining a solution consistent with non-assisted MCTS," are highly encouraging and indicate a valuable step towards practical deployment, potentially extending to other domains "beyond" facility location as hinted by the title. As an extended abstract, the details provided are understandably concise. For a full paper, it would be crucial to elaborate on several key aspects. This includes a more detailed description of the surrogate model's architecture, training methodology, and its performance characteristics (e.g., accuracy, training time). A thorough exposition of the MCTS implementation, including choice of policies and rollout strategies, would also be beneficial. Most importantly, a comprehensive experimental section would be required, detailing the benchmark problems used, the quantitative metrics for "faster" and "consistent" solutions, statistical significance, and a discussion of the computational resources involved. Further exploration into the broader applicability of this approach to other combinatorial problems and the trade-offs involved in surrogate model fidelity versus computational speed would significantly strengthen future contributions.

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