- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | MA Liang, HU Chenhan, JIN Fucai, DONG Wei. Double-Layer and Multi-objective Constraint Optimization Model for Transportation Scheduling of Molten Iron[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 357-366, 397. doi: 10.3969/j.issn.0258-2724.20220008
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In order to realize the collaborative optimization of operation scheduling and resource allocation in molten iron transportation, based on the theory of the cumulative scheduling with constraint programming and lexicographic multi-objective optimization, a double-layer and multi-objective constraint optimization method is explored for the transportation scheduling of molten iron. Firstly, setting the highest turnover rate of molten iron tanks and the highest operation efficiency as two lexicographic objectives, the upper-level constraint optimization model is built for molten iron transportation operation. In the model, the constraints are involved, such as operation sequence, operation implementation logic, time limit of molten iron cooling, limited operation times of molten iron tank, resource capacity limit, and resource pool of the molten iron tanks. Secondly, with the highest resource utilization balance, the lower-level constrained optimization model is established for resource allocation in molten iron transportation, in which the uniqueness of operation implementation and resource capacity are taken as constraints. Finally, the hybrid algorithm of constraint propagation and multi-point constructive search is developed to solve the whole model iteratively. The case study shows that, the turnover rate target and transportation efficiency target obtained by the hybrid algorithm are 14.29% and 60.53% higher than those obtained by the basic depth first backtracking algorithm respectively. Compared with weighted and single objective models, lexicographical multi-objective model improves the efficiency and quality of solution by 20.3% and 11.11%, respectively.
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