Title: Earthwork allocation optimisation based on cut-fill matching and transportation path planning

Authors: Jing Yu; You Huang; Lining Xing; Zizhou Zhao; Mingshun Li

Addresses: School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China; National Engineering Research Center of Highway Maintenance Technology, Changsha University of Science and Technology Changsha, China ' Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road and Traffic Safety, Ministry of Education, Changsha University of Science and Technology, Changsha, 410114, Hunan, China; School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China ' Key Laboratory of Collaborative Intelligence Systems, Xidian University, Xi'an, China ' Guangdong Xiangfei Road Engineering Supervision Co., Ltd., Guangzhou,, China ' School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China

Abstract: Earthwork allocation is a critical component of engineering construction projects, with the objective of reducing costs and shortening the construction period. While previous research has focused on solving the cut-fill matching problem, there is a lack of study on mechanical transportation path planning. This study introduces a matching model for cut-fill that minimises construction costs and mechanical transfer distance. Moreover, a hybrid ant colony-greedy model and algorithm are proposed to address the transportation path planning problem. To demonstrate the effectiveness of the model and algorithm, an earthwork allocation project is examined using the earthwork allocation model and its solution algorithm. Experimental results show that the two-stage allocation model and algorithm successfully address earthwork allocation challenges. Additionally, the AC-GA algorithm provides a superior earthwork allocation scheme.

Keywords: earthwork allocation; cut-fill matching; path optimisation; linear programming; ant colony-greedy algorithm.

DOI: 10.1504/IJAAC.2024.140538

International Journal of Automation and Control, 2024 Vol.18 No.5, pp.588 - 603

Received: 16 Aug 2023
Accepted: 07 Nov 2023
Published online: 22 Aug 2024 *

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