Title: Inventory optimisation based on NSGA-III algorithm

Authors: Yaxue Li; Hongzhi Xie; Xiaolin Deng; Jin Zhang; Shuhui Liu; Li Wang

Addresses: National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China ' Spare Parts Center, China Nuclear Power Operations Co.,Ltd, Shenzhen, 518000, China ' Spare Parts Center, China Nuclear Power Operations Co.,Ltd, Shenzhen, 518000, China ' National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China ' Spare Parts Center, China Nuclear Power Operations Co.,Ltd, Shenzhen, 518000, China ' National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

Abstract: Inventory management is essential to any enterprise, and correctly setting inventory parameters can reduce costs while ensuring adequate inventory levels. To meet the availability requirements of different categories of spare parts for nuclear power plants, this paper combines the NSGA3 algorithm with a single-objective algorithm to solve the inventory parameter setting problem. Based on the historical usage data of nuclear power plant spare parts, we establish both availability rate and spare part cost objective functions and optimise these functions using the NSGA3 algorithm. We then use a single-objective optimisation function to obtain the optimal solution as the parameter setting for nuclear power plant spare parts. By combining these two methods, we can better meet spare parts requirements for different categories, while fully considering the inventory management needs of nuclear power companies and improving management efficiency.

Keywords: NSGA3; single-objective optimisation; inventory manage; spare parts classification.

DOI: 10.1504/IJSSC.2023.133247

International Journal of Space-Based and Situated Computing, 2023 Vol.9 No.3, pp.158 - 164

Received: 23 May 2023
Accepted: 27 May 2023
Published online: 03 Sep 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article