A mixed-integer linear programming model for the continuous casting planning

Bellabdaoui, Adil; Teghem, Jacques

doi:10.1016/j.ijpe.2004.10.016

Cited by 103 publications

(54 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A reasonable scheduling plan can effectively reduce production cost and energy consumption, promote quality and efficiency and reduce the emission of carbon and other pollutants. Bellabdaoui and Teghem identify a mixed integer linear programming model to find the solution to the continuous casting problem [1]. However, the optimization algorithm cannot arrive at the optimal solution to scheduling in steelmaking casting production in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

Integrated Batch Planning Optimization Based on Fuzzy Genetic and Constraint Satisfaction for Steel Production

Su¹,

Yue²,

Jin³

2016

Int. j. simul. model.

View full text Add to dashboard Cite

This paper stablishes the model of the integrated batch planning process, and proposes an improved algorithm for this problem. The simulation results of a computerized scheduling system are given to prove the fitness of the model. The steel making casting production process scheduling problem is very difficult to get a good performance solution in practice. The scheduling of steelmaking casting production is a complicated problem of combinational optimization in the hybrid flow shop, which is an NP-Hard problem, and determining the polynomial time algorithm to arrive at the accurate optimal solution has proved to be a difficult task. An improved fuzzy genetic optimization and improved algorithm strategy are proposed. The results show that the method is very efficient in solving the steel casting production scheduling problem.

show abstract

Section: Introductionmentioning

confidence: 99%

Integrated Batch Planning Optimization Based on Fuzzy Genetic and Constraint Satisfaction for Steel Production

Su¹,

Yue²,

Jin³

2016

Int. j. simul. model.

View full text Add to dashboard Cite

show abstract

“…However, it is still not very satisfactory when solving the large-scale SCC problems in actual iron & steel companies. The most commonly used methods for solving the SCC scheduling problems are and mixed-integer linear optimization (MILP) [3][4][5], artificial intelligence, linear optimization (LP), heuristics [6,7] and simulation methods. The literatures [8][9][10] illustrate the detailed reviews on these methods.…”

Section: Introductionmentioning

confidence: 99%

Simulating Schedule Optimization Problem in Steelmaking Continuous Casting Process

Yang¹,

Qiu²,

Luo³

et al. 2015

Int. j. simul. model.

View full text Add to dashboard Cite

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.

show abstract

“…But these two algorithms have some weakness. For example, GA has lower local searching ability, not enough uses the feedback information in system, and prone to "premature" convergence in practical application [9]. While for PSO, it has lower searching precision, prone to fall into local optimal, the global optimal solution may not easy to be searched as the result [10].…”

Section: Introductionmentioning

confidence: 99%

Flowshop with Multiple Processors Dynamic Scheduling Problem based on Hybrid Genetic-Particle Swarm Optimization

Li¹,

Chuanyong²,

Li³

2015

TOCSJ

View full text Add to dashboard Cite

Abstract:Flowshop scheduling problem is a kind of typical scheduling problem, and has wide application background. While common in the flowshop environment, the objective of the flowshop with multiple processors dynamic scheduling problem is minimize the total cost of earliness/tardiness and deviation punishment. In this paper, a mathematical model of the problem is presented and a hybrid genetic-particle swarm optimization algorithm is constructed. The simulation results show that this is a better solving method for complex flowshop multiple processors dynamic scheduling problem.

show abstract

A mixed-integer linear programming model for the continuous casting planning

Cited by 103 publications

References 10 publications

Integrated Batch Planning Optimization Based on Fuzzy Genetic and Constraint Satisfaction for Steel Production

Integrated Batch Planning Optimization Based on Fuzzy Genetic and Constraint Satisfaction for Steel Production

Simulating Schedule Optimization Problem in Steelmaking Continuous Casting Process

Flowshop with Multiple Processors Dynamic Scheduling Problem based on Hybrid Genetic-Particle Swarm Optimization

Contact Info

Product

Resources

About