An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots
This study addresses the multi-robot task allocation (MRTA) problem for logistics robots operating in zone-picking warehouse environments. With the rapid growth of e-commerce and the Fourth Industrial Revolution, logistics robots are increasingly deployed to manage high-volume order fulfillment. How...
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/11/1770 |
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| author | Byoungho Choi Minkyu Kim Heungseob Kim |
| author_facet | Byoungho Choi Minkyu Kim Heungseob Kim |
| author_sort | Byoungho Choi |
| collection | DOAJ |
| description | This study addresses the multi-robot task allocation (MRTA) problem for logistics robots operating in zone-picking warehouse environments. With the rapid growth of e-commerce and the Fourth Industrial Revolution, logistics robots are increasingly deployed to manage high-volume order fulfillment. However, efficiently assigning tasks to multiple robots is a complex and computationally intensive problem. To address this, we propose a five-step optimization framework that reduces computation time while maintaining practical applicability. The first step calculates and stores distances and paths between product locations using the A* algorithm, enabling reuse in subsequent computations. The second step performs hierarchical clustering of orders based on spatial similarity and capacity constraints to reduce the problem size. In the third step, the traveling salesman problem (TSP) is formulated to determine the optimal execution sequence within each cluster. The fourth step uses a mixed integer linear programming (MILP) model to allocate clusters to robots while minimizing the overall makespan. Finally, the fifth step incorporates battery constraints by optimizing the task sequence and partial charging schedule for each robot. Numerical experiments were conducted using up to 1000 orders and 100 robots, and the results confirmed that the proposed method is scalable and effective for large-scale scenarios. |
| format | Article |
| id | doaj-art-25cea7cfd000483b9518aa78596a5dab |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-25cea7cfd000483b9518aa78596a5dab2025-08-20T03:46:50ZengMDPI AGMathematics2227-73902025-05-011311177010.3390/math13111770An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics RobotsByoungho Choi0Minkyu Kim1Heungseob Kim2Department of Smart Manufacturing Engineering, Changwon National University, Changwon-si 51140, Republic of KoreaDepartment of Smart Manufacturing Engineering, Changwon National University, Changwon-si 51140, Republic of KoreaDepartment of Smart Manufacturing Engineering, Changwon National University, Changwon-si 51140, Republic of KoreaThis study addresses the multi-robot task allocation (MRTA) problem for logistics robots operating in zone-picking warehouse environments. With the rapid growth of e-commerce and the Fourth Industrial Revolution, logistics robots are increasingly deployed to manage high-volume order fulfillment. However, efficiently assigning tasks to multiple robots is a complex and computationally intensive problem. To address this, we propose a five-step optimization framework that reduces computation time while maintaining practical applicability. The first step calculates and stores distances and paths between product locations using the A* algorithm, enabling reuse in subsequent computations. The second step performs hierarchical clustering of orders based on spatial similarity and capacity constraints to reduce the problem size. In the third step, the traveling salesman problem (TSP) is formulated to determine the optimal execution sequence within each cluster. The fourth step uses a mixed integer linear programming (MILP) model to allocate clusters to robots while minimizing the overall makespan. Finally, the fifth step incorporates battery constraints by optimizing the task sequence and partial charging schedule for each robot. Numerical experiments were conducted using up to 1000 orders and 100 robots, and the results confirmed that the proposed method is scalable and effective for large-scale scenarios.https://www.mdpi.com/2227-7390/13/11/1770logistics robotsmulti-robot task allocation (MRTA)zone-picking warehousetask allocation and scheduling |
| spellingShingle | Byoungho Choi Minkyu Kim Heungseob Kim An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots Mathematics logistics robots multi-robot task allocation (MRTA) zone-picking warehouse task allocation and scheduling |
| title | An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots |
| title_full | An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots |
| title_fullStr | An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots |
| title_full_unstemmed | An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots |
| title_short | An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots |
| title_sort | optimization framework for allocating and scheduling multiple tasks of multiple logistics robots |
| topic | logistics robots multi-robot task allocation (MRTA) zone-picking warehouse task allocation and scheduling |
| url | https://www.mdpi.com/2227-7390/13/11/1770 |
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