Version 1
: Received: 30 April 2024 / Approved: 30 April 2024 / Online: 1 May 2024 (16:46:59 CEST)
How to cite:
Gao, Z.; Li, D.; Wang, D.; Yu, Z. Raw Material Purchasing Optimization Using Column Generation. Preprints2024, 2024050033. https://doi.org/10.20944/preprints202405.0033.v1
Gao, Z.; Li, D.; Wang, D.; Yu, Z. Raw Material Purchasing Optimization Using Column Generation. Preprints 2024, 2024050033. https://doi.org/10.20944/preprints202405.0033.v1
Gao, Z.; Li, D.; Wang, D.; Yu, Z. Raw Material Purchasing Optimization Using Column Generation. Preprints2024, 2024050033. https://doi.org/10.20944/preprints202405.0033.v1
APA Style
Gao, Z., Li, D., Wang, D., & Yu, Z. (2024). Raw Material Purchasing Optimization Using Column Generation. Preprints. https://doi.org/10.20944/preprints202405.0033.v1
Chicago/Turabian Style
Gao, Z., Danni Wang and Zengcai Yu. 2024 "Raw Material Purchasing Optimization Using Column Generation" Preprints. https://doi.org/10.20944/preprints202405.0033.v1
Abstract
The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.
Keywords
Purchasing; optimization; MILP; column generation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
