Conflict-Free Routing of Twin Reclaimers in the Stockyard Based on a Time-Space Network Model

Abstract

In the stockyard of dry bulk materials such as coal and iron ore, multiple stacker-reclaimers operate collaboratively to enhance stockyard productivity. However, effectively planning the routes for these interconnected machines presents a significant challenge. We propose a new modeling framework for this conflict-free routing problem in which the reclaiming process is modeled in a so-called time-space network (TSN) framework. The formulated optimization problem is mixed integer programming (MIP), which has been proven NP-hard. To address its computational efficiency, we develop a two-level metaheuristic algorithm to simplify the encoding complexity of the original problem. In the developed two-level algorithm, a task priority ordering candidate is listed at the top level, while the detailed conflict-free routes are constructed at the bottom level using an insertion-based conflict-free reclaiming and customized route improvement. The developed metaheuristic is tested on a large number of instances in comparison with the Gurobi solver and three commonly used methods for such a routing problem. The experimental results show that the proposed algorithm outperforms the other four methods regarding the solution quality and the computation time. Note to Practitioners—This study is motivated by the need for efficient route planning of interconnected reclaimers in the stockyard of dry bulk materials such as coal and iron ore. To address this problem, we present a novel modeling framework for addressing the conflict-free routing problem, wherein the reclaiming process is intricately modeled within a so-called time-space network framework. Solving the resulting mixed-integer programming is of high computational complexity; therefore, we have engineered a two-level metaheuristic framework to reduce its complexity. The developed methodology has been extensively tested on instances featuring real-world stockyard operations and contributes to terminal operators by improving productivity and resulting in more economic benefits. Future research will integrate real-time data and dynamic optimization techniques to facilitate routing decisions that can adapt to evolving operational conditions and stockyard configurations.