Raw Material Purchasing Optimization Using Column Generation
Source abstract
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 and bound algorithm while 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.
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Research context
OpenAlex topics: Advanced Manufacturing and Logistics Optimization · Optimization and Packing Problems · Scheduling and Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, China
The Artificial Intelligence and Big Data Science Center, Northeastern University, Shenyang 110819, China
Funding
the Major Program of the National Natural Science Foundation of China · the 111 Project
References (37)
Show source-provided references
1. A multi-objective model for purchasing of bulk raw materials of a large-scale integrated steel plant10.1016/S0925-5273(02)00373-0 ↗
2. Zhang, X.F., Boutat, D., and Liu, D.Y. (2023). Applications of fractional operator in image processing and stability of control systems. Fractal Fract., 7.10.3390/fractalfract7050359 ↗
3. Zhang, X.F., Liu, R., Wang, Z., and Gui, Q. (2022). Adaptive fractional image enhancement algorithm based on rough set and particle swarm optimization. Fractal Fract., 6.10.3390/fractalfract6020100 ↗
4. Adaptive sliding mode consensus control based on neural network for singular fractional order multi-agent systems
5. A model for an optimal procurement strategy
6. Global output-feedback prescribed performance control of nonlinear systems with unknown virtual control coefficients10.1109/TAC.2021.3137103 ↗
7. Data analytics and optimization for smart industry10.1007/s42524-020-0126-0 ↗
8. A dynamic version of the economic lot size model10.1287/mnsc.5.1.89 ↗
9. Deterministic production planning: Algorithms and complexity
10. Heuristic procedures for the single operation multi-item loading problem10.1080/05695557908974478 ↗
11. An integrated approach for production lot sizing and raw material purchasing10.1016/j.ejor.2018.02.042 ↗
12. Raw material procurement with fluctuating prices10.1016/j.ijpe.2007.02.038 ↗
13. Fuzzy axiomatic design approach based green supplier selection: A case study from Singapore10.1016/j.jclepro.2013.12.076 ↗
14. Optimal purchasing of raw materials: A data-driven approach10.1002/aic.11494 ↗
15. Fuzzy adaptive output feedback control of uncertain nonlinear systems with prescribed per formance10.1109/TCYB.2017.2692767 ↗
16. Heuristic lot-sizing performance in a rolling-schedule environment10.1111/j.1540-5915.1980.tb01170.x ↗
17. An economic lot-sizing technique I: The part-period algorithm10.1147/sj.71.0030 ↗
18. Cyclic ordering policies from capacitated suppliers under limited cycle time10.1016/j.cie.2018.12.048 ↗
19. Simple heuristic for dynamic order sizing and supplier selection with time-varying data10.1111/j.1937-5956.2002.tb00474.x ↗
20. An efficient optimal algorithm for the quantity discount problem in material require ment planning10.1016/j.cor.2008.05.003 ↗
21. Integration of lot sizing and safety strategy placement using interactive multiobjective optimization10.1016/j.cie.2022.108731 ↗
22. An optimization model for aggregate production planning and control: A genetic algorithm approach
23. Integrated resource leveling and material procurement with variable execution intensities10.1016/j.cie.2020.106673 ↗
24. The capacitated lot sizing problem: A review of models and algorithms10.1016/S0305-0483(03)00059-8 ↗
25. Multi-item single-level capacitated dynamic lot-sizing heuristics: A general review10.1057/jors.1988.169 ↗
26. Column generation based heuristic algorithm for multi-item scheduling10.1080/05695558308974624 ↗
27. Set partition and column generation heuristics for capacitated dynamic lot-sizing10.1016/0377-2217(90)90296-N ↗
28. Solving the CLSP by a tabu search heuristic10.1057/jors.1996.13 ↗
29. An efficient algorithm for multi-item scheduling10.1287/opre.19.4.946 ↗
30. Programming of economic lot sizes10.1287/mnsc.4.2.115 ↗
31. A new optimization algorithm for the vehicle routing problem with time windows10.1287/opre.40.2.342 ↗
32. A column generation approach to the urban transit crew scheduling problem10.1287/trsc.23.1.1 ↗
33. A branch-and-price algorithm for the generalized assignment problem10.1287/opre.45.6.831 ↗
34. Decomposition principle for linear programming10.1287/opre.8.1.101 ↗
35. On the integer properties of scheduling set partitioning models10.1016/0377-2217(88)90233-0 ↗
36. A heuristic solution procedure for the multi-item, single-level, limited-capacity, lot-sizing problem10.1016/0272-6963(81)90033-4 ↗
37. A simple heuristic for the multi-item single level capacitated lot sizing problem10.1016/0167-6377(86)90027-1 ↗