Algorithm Development in Materials Science and Engineering: Poster Session
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee, TMS: Integrated Computational Materials Engineering Committee, TMS: Phase Transformations Committee, TMS: Solidification Committee
Program Organizers: Mohsen Asle Zaeem, Colorado School of Mines; Mikhail Mendelev, NASA ARC; Bryan Wong, University of California, Riverside; Ebrahim Asadi, University of Memphis; Garritt Tucker, Colorado School of Mines; Charudatta Phatak, Argonne National Laboratory; Bryce Meredig, Travertine Labs LLC
Wednesday 5:30 PM
March 17, 2021
Room: RM 36
Location: TMS2021 Virtual
Session Chair: Mohsen Asle Zaeem, Colorado School of Mines
Model and Improved Dynamic Programming Algorithm for Optimization of Unplanned Slab Allocation in the Steel Plant: Yongzhou Wang1; Zhong Zheng1; Cheng Wang1; Xiaoqiang Gao1; 1Chongqing University
The unplanned slab is the surplus slab produced by the steelmaking-continuous casting process, which will increase the inventory cost of enterprises. The unplanned slab allocation problem is to reasonably assign the unplanned slabs to the hot rolling supplementary orders, steelmaking supplementary orders, or customer orders in a given period. It can be considered an extension of the multiple knapsack problem. Therefore, a 0-1 integer programming model is established to minimize the cost of unplanned slab and order specification variances and inventory cost of the unplanned slab. Due to its NP-hardness, the adaptive method of contract assignment priority measurement in different scenarios and the improved NSGA-II algorithm considering the local search strategy are proposed to solve the problem. Using unplanned slab data from a steel company for testing, the algorithm proposed in this paper is superior to the manual algorithm in terms of solution quality and calculation time.