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227. Robotic Assembly Line Balancing Problem Considering Uncertain Demand by Q-learning method
Invited abstract in session TA-26: Applications to Logistics and Transportation, stream Combinatorial Optimization.
Tuesday, 8:30-10:00Room: 012 (building: 208)
Authors (first author is the speaker)
| 1. | Yuchen Li
|
| Economics and Management, Beijing University of Technology |
Abstract
Coordinated decision making in manufacturing sector is crucial for business development. In this paper, a multi-objective robotic assembly line balancing problem is considered, which has the following features:1) it involves a two-stage decision making process where the first stage is to design the robotic assembly line in terms of the task and robot assignment, and the second stage is to determine how much to produce for each product model; 2) the demand for each product is uncertain; 3) two objectives are considered including the number of workstations and carbon emissions. A reinforcement learning (Q-learning) method is devised to solve such problem. Computational studies are performed to validate the performance of the proposed algorithm.
Keywords
- Combinatorial Optimization
- Production and Inventory Systems
- Programming, Stochastic
Status: accepted
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