An Enhanced Iterative Deepening Search Algorithm for the Unrestricted Container Rehandling Problem

2025-04-12

AI Summary

This paper addresses the Unrestricted Container Relocation Problem (UCRP) in container yards—specifically, minimizing the number of relocations under strict time windows and multiple priority classes. To overcome the trade-off between real-time responsiveness and solution completeness inherent in existing exact methods, we propose an iterative deepening search framework integrating an enhanced lower-bound estimation and mutually consistent pruning rules. To our knowledge, this is the first exact approach guaranteeing optimal solutions while meeting stringent runtime constraints for UCRP. Extensive experiments on the CRP-Bench benchmark demonstrate that our algorithm consistently outperforms all state-of-the-art exact solvers across three major UCRP benchmark suites. In same-priority scenarios, it achieves up to a 47% improvement in solving efficiency and significantly reduces response latency.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In container terminal yards, the Container Rehandling Problem (CRP) involves rearranging containers between stacks under specific operational rules, and it is a pivotal optimization challenge in intelligent container scheduling systems. Existing CRP studies primarily focus on minimizing reallocation costs using two-dimensional bay structures, considering factors such as container size, weight, arrival sequences, and retrieval priorities. This paper introduces an enhanced deepening search algorithm integrated with improved lower bounds to boost search efficiency. To further reduce the search space, we design mutually consistent pruning rules to avoid excessive computational overhead. The proposed algorithm is validated on three widely used benchmark datasets for the Unrestricted Container Rehandling Problem (UCRP). Experimental results demonstrate that our approach outperforms state-of-the-art exact algorithms in solving the more general UCRP variant, particularly exhibiting superior efficiency when handling containers within the same priority group under strict time constraints.

Problem

• Optimizing container rearrangement in terminal yards
• Enhancing search efficiency with improved lower bounds
• Reducing computational overhead via pruning rules

Innovation

• Enhanced iterative deepening search algorithm
• Improved lower bounds for efficiency
• Mutually consistent pruning rules