RGV dynamic scheduling optimization model based on greedy algorithm
DOI: 10.23977/acss.2018.21002 | Downloads: 12 | Views: 912
Wang Haoze 1, Dong Chen 2, Xu Yuan 3
1 School of computer and information technology, Beijing Jiaotong University, Beijing, 264401
2 School of electronic information engineering, Beijing Jiaotong University, Beijing, 264401
3 School of economics and management, Beijing Jiaotong University, Beijing, 264401
Corresponding AuthorWang Haoze
For one intelligent processing system which inclusionRGV, how to effectively use various resources to rationally and dynamically perform dynamic scheduling to improve production efficiency is the key. This paper studies only the material processing operations of a single process. According to the processing process of a given material, we need to focus on analyzing its dynamic scheduling strategy. In a material processing system with an established 8-hour working time, maximizing the amount of material processing is the primary goal. However, the increased amount of material processing is obtained by continuously completing the accumulation of work tasks. Therefore, the core is to convert the material processing quantity maximization model in the system into a task selection planning model based on time loss minimization, and seek each with a greedy algorithm. A task selects the optimal solution locally, and approximates each local optimal combination as a global optimal.
KEYWORDSGreedy algorithm, RGV dynamic scheduling, Global optimal
CITE THIS PAPER
Haoze, W., Dong, C., Yuan, X., Yuqiang, S., Ling Y., RGV dynamic scheduling optimization model based on greedy algorithm. Advances in Computer, Signals and Systems (2018) 2: 8-10.
 Qiao Fei, Wu Qidi. Real-time Scheduling and Fault Scheduling of RGV in SJ-FMS[J]. Combined Machine Tool & Automatic Processing Technology, 1995(3): 39-43.
 Li Cunchang, Li Haolin. Overall Design and Dynamic Real-time Scheduling of Automatic Guide Vehicle (AGV)[J]. Combined Machine Tool & Automatic Processing Technology, 1996(4): 32-36.