Steel-making process scheduling using Lagrangian relaxation

Tang, Lixin; Luh, Peter B.; Liu, Jiyin; Fang, Lian-Hua

doi:10.1080/00207540110073000

Cited by 196 publications

(87 citation statements)

References 18 publications

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“…HFSs have important applications in flexible manufacturing systems (FMS), such as electronics and furniture manufacturing, process industries such as chemical, textile, metallurgical, semiconductors, printed circuit board assembly lines, pharmaceutical, oil, food, automobile manufactures, steel making systems, etc. -see, e.g., Tang et al (2002), Jin et al (2002), Mathirajan & Sivakumar (2006) and Yaurima et al (2009)-. Therefore, HFS scheduling has attracted interest by scientific community, being reflected in literature contributions dealing with this particular problem; however the issue of lot processing in this type of shops has not gained sufficient attention.…”

Section: Particularities Of Hfs With Lot Processingmentioning

confidence: 99%

Lot Processing in Hybrid Flow Shop Scheduling Problem

Burtseva¹,

Romero²,

Ramirez³

et al. 2012

Production Scheduling

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Section: Particularities Of Hfs With Lot Processingmentioning

confidence: 99%

Lot Processing in Hybrid Flow Shop Scheduling Problem

Burtseva¹,

Romero²,

Ramirez³

et al. 2012

Production Scheduling

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“…In this process, we need to decide the sequence, the beginning time, as well as the molten steel machine and scheduling of the molten steel in the iron & steel production stages. It is very difficult to get a good performance solution in practice in iron & steel production [1]. Optimization of the scheduling SCC processes can reduce the energy consumption and improve the profit of the company, so many iron & steel companies have studied the SCC production process for continuous improvement.…”

Section: Introductionmentioning

confidence: 99%

Simulating Schedule Optimization Problem in Steelmaking Continuous Casting Process

Yang¹,

Qiu²,

Luo³

et al. 2015

Int. j. simul. model.

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This paper establishes the models of the steelmaking continuous casting (SCC) process, and proposed the improved algorithms for this problem. The simulation results of a computerized scheduling system are also given to prove the model. The SCC process scheduling problem is very difficult to get a good performance solution in practice. The scheduling of the SCC process requires that each cast plan is processed on time, and the charges should be processed continuously for the same caster in the same cast, as well as the waiting time of the charges cannot be conflicted mutually in the same converters. We propose a quantum-behaved particle swarm optimization (QPSO) and improved algorithm strategy. The results show that the QPSO is very efficient for solving the SCC production scheduling problem, especially for large scale problem.

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“…Tang et al [10] proposed an SCC scheduling that eliminates machine conflicts, via a non-linear programming model. In another work, Tang et al [11] developed an integer-programming model and obtained the optimal solution by combining Lagrangian relaxation and a heuristic method. Pacciarelli and Pranzo [12] completed the SCC schedule by using a generalisation of the disjunctive graph of Roy and Sussman [13] and Beam search.…”

Section: Introductionmentioning

confidence: 99%

Optimising Steel Production Schedules via a Hierarchical Genetic Algorithm

Worapradya¹

2014

The South African Journal of Industrial Engineering

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This paper presents an effective scheduling in a steel-making continuous casting (SCC) plant. The main contribution of this paper is the formulation of a new optimisation model that more closely represents real-world situations, and a hierarchical genetic algorithm (HGA) tailored particularly for searching for an optimal SCC schedule. The optimisation model is developed by integrating two main planning phases of traditional scheduling: (1) planning cast sequence, and (2) scheduling of steel-making and timing of all jobs. A novel procedure is given for genetic algorithm (GA) chromosome coding that maps Gantt chart and hierarchical chromosomes. The performance of the proposed methodology is illustrated and compared with a two-phase traditional scheduling and a standard GA toolbox. Both qualitative and quantitative performance measures are investigated. OPSOMMINGEffektiewe skedulering van 'n kontinue-giet staalaanleg word voorgehou. Die hoof bydrae van hierdie navorsing is die formulering van 'n nuwe optimiseringsmodel wat regte wêreld situasies beter verteenwoordig, asook 'n hiërargiese genetiese algoritme wat spesifiek aangepas word vir die soek van 'n optimale kontinue-giet skedule. Die optimiseringsmodel is onwikkel deur twee hoof beplanningsfases van tradisionele skedulering te integreer, naamlik die beplan van die giet volgorde en die skedulering van staal vervaardiging en die tydsberekening van alle werkstukke. 'n Nuwe prosedure word voorgestel vir genetiese algoritme chromosoom kodering wat Gantt-kaart en hiërargiese chromosome kombineer. Die voorgestelde metode word geïllustreer en vergelyk met 'n twee fase tradisionele skedulering en 'n standaard genetiese algoritme. Beide die kwalitatiewe en kwantitatiewe prestasiemaatstawwe is ondersoek.

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Steel-making process scheduling using Lagrangian relaxation

Cited by 196 publications

References 18 publications

Lot Processing in Hybrid Flow Shop Scheduling Problem

Lot Processing in Hybrid Flow Shop Scheduling Problem

Simulating Schedule Optimization Problem in Steelmaking Continuous Casting Process

Optimising Steel Production Schedules via a Hierarchical Genetic Algorithm

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