Adaptive Large Neighborhood Search for Scheduling of Mobile Robots

DANG, Vinh Quang, Hana RUDOVÁ and Cong Thanh NGUYEN. Adaptive Large Neighborhood Search for Scheduling of Mobile Robots. Online. In The Genetic and Evolutionary Computation Conference (GECCO). New York, NY, USA: ACM, 2019. p. 224-232. ISBN 978-1-4503-6111-8. Available from: https://dx.doi.org/10.1145/3321707.3321764. [citováno 2024-04-23]

Basic information

• Original name: Adaptive Large Neighborhood Search for Scheduling of Mobile Robots
• Authors: DANG, Vinh Quang (704 Viet Nam, belonging to the institution), Hana RUDOVÁ (203 Czech Republic, guarantor, belonging to the institution) and Cong Thanh NGUYEN (704 Viet Nam)
• Edition: New York, NY, USA, The Genetic and Evolutionary Computation Conference (GECCO), p. 224-232, 9 pp. 2019.
• Publisher: ACM

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: DOI
• RIV identification code: RIV/00216224:14330/19:00109329
• Organization unit: Faculty of Informatics
• ISBN: 978-1-4503-6111-8
• Doi: http://dx.doi.org/10.1145/3321707.3321764
• UT WoS: 000523218400029
• Keywords in English: Scheduling; Mobile robots; Adaptive Large Neighborhood Search; Flexible Manufacturing Systems
• Tags: core_A, firank_A
• Tags: International impact, Reviewed

Abstract

Our work addresses the scheduling of mobile robots for transportation and processing of operations on machines in a flexible manufacturing system. Both mobile robots and automated guided vehicles (AGVs) can transport components among machines in the working space. Nevertheless, the difference is that mobile robots considered in this work can process specific value-added operations, which is not possible for AGVs. This new feature increases complexity as well as computational demands. To summarize, we need to compute a sequence of operations on machines, the robot assignments for transportation, and the robot assignments for processing. The main contribution is the proposal of an adaptive large neighborhood search algorithm with the sets of exploration and exploitation heuristics to solve the problem considering makespan minimization. Experimental evaluation is presented on the existing benchmarks. The quality of our solutions is compared to a heuristic based on genetic algorithm and mixed- integer programming proposed recently. The comparison shows that our approach can achieve comparable results in real time which is in order of magnitude faster than the earlier heuristic.