Production scheduling optimization algorithm for the hot rolling processes

Chen, A. l.; Yang, Genke; Wu, Zhongxi

doi:10.1080/00207540600988048

Cited by 47 publications

(18 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been applied to continuous nonlinear optimization problems (Kennedy and Eberthart 1995), training of artificial neural networks (Eberhart and Shi 2001), the job-shop scheduling problem (Zhang et al 2009), steel hot rolling process scheduling (Chen et al 2008), the traveling salesman problem (Pang et al 2004), supply chain network design problem (Banks et al 2008), part sequencing and operation sequencing optimization for a CNC machine (Kumar et al 2009), vision inspection on printed circuit boards (Wu et al 2009), and others. More diverse application areas are described in Eberhart and Shi (2001), and Banks et al (2007Banks et al ( , 2008.…”

Section: Introductionmentioning

confidence: 99%

A probability matrix based particle swarm optimization for the capacitated vehicle routing problem

Kim

Son

2010

J Intell Manuf

View full text Add to dashboard Cite

Particle swam optimization (PSO) is a relatively new metaheuristic that has recently drawn much attention from researchers in various optimization areas. However, application of PSO for the capacitated vehicle routing problem (CVRP) is very limited. This paper proposes a simple PSO approach for solving the CVRP. The proposed PSO approach uses a probability matrix as the main device for particle encoding and decoding. While existing research used the PSO solely for assignment of customers to routes and used other algorithms to sequence customers within the routes, the proposed approach applies the PSO approach to both simultaneously. The computational results show the effectiveness of the proposed PSO approach compared to the previous approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

A probability matrix based particle swarm optimization for the capacitated vehicle routing problem

Kim

Son

2010

J Intell Manuf

View full text Add to dashboard Cite

show abstract

“…Tang et al (2000) proposed a multiple travelling salesman problem model, and solved it using a modified genetic algorithm, which could simultaneously form m rolling units in a parallel strategy. Chen et al (2008a) proposed a vehicle routing problem (VRP) model and developed a hybrid approach QPSO-SA to solve the problem. Chen et al (1998) suggested a vehicle routing problem with time windows (VRPTW) model to describe the rolling batch planning problem, in which the position limitation of slabs was quantified as the time constraint.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective optimisation algorithm for the hot rolling batch scheduling problem

Jia

Yang

et al. 2013

International Journal of Production Research

View full text Add to dashboard Cite

The hot rolling batch scheduling problem is a hard problem in the steel industry. In this paper, the problem is formulated as a multi-objective prize collecting vehicle routing problem (PCVRP) model. In order to avoid the selection of weight coefficients encountered in single objective optimisation, a multi-objective optimisation algorithm based on Pareto-dominance is used to solve this model. Firstly, the Pareto MAX -MI N Ant System (P-MMAS), which is a brand new multi-objective ant colony optimisation algorithm, is proposed to minimise the penalties caused by jumps between adjacent slabs, and simultaneously maximise the prizes collected. Then a multi-objective decision-making approach based on TOPSIS is used to select a final rolling batch from the Pareto-optimal solutions provided by P-MMAS. The experimental results using practical production data from Shanghai Baoshan Iron & Steel Co., Ltd. have indicated that the proposed model and algorithm are effective and efficient.

show abstract

“…Tang and Wang [8] modeled the hot rolling production scheduling problem as a prize collecting vehicle routing problem (PCVRP), for which an iterated local search algorithm (ILS) was proposed on the basis of very large-scale neighborhood (VLSN) using cyclic transfer. Due to the complexity of the scheduling models and the inefficiency of mathematical programming methods, various intelligent methods, including local search and greedy algorithm [9][10][11], genetic algorithm [12][13][14], tabu search [15] and particle swarm optimization [29] have been widely applied in past decades to solve the HSM scheduling problem. Furthermore, scheduling systems with different features have also been developed.…”

Section: Introductionmentioning

confidence: 99%

Development of hybrid evolutionary algorithms for production scheduling of hot strip mill

Chen

Ge³

et al. 2012

Computers & Operations Research

View full text Add to dashboard Cite

a b s t r a c tA hot strip mill (HSM) produces hot rolled products from steel slabs, and is one of the most important production lines in a steel plant. The aim of HSM scheduling is to construct a rolling sequence that optimizes a set of given criteria under constraints. Due to the complexity in modeling the production process and optimizing the rolling sequence, the HSM scheduling is a challenging task for hot rolling production schedulers. This paper first introduces the HSM production process and requirements, and then reviews previous research on the modeling and optimization of the HSM scheduling problem. According to the practical requirements of hot rolling production, a mathematical model is formulated to describe two important scheduling sub-tasks: (1) selecting a subset of manufacturing orders and (2) generating an optimal rolling sequence from the selected manufacturing orders. Further, hybrid evolutionary algorithms with integration of genetic algorithm (GA) and extremal optimization (EO) are proposed to solve the HSM scheduling problem. Computational results on industrial data show that the proposed HSM scheduling solution can be applied in practice to provide satisfactory performance.

show abstract

Production scheduling optimization algorithm for the hot rolling processes

Cited by 47 publications

References 22 publications

A probability matrix based particle swarm optimization for the capacitated vehicle routing problem

A probability matrix based particle swarm optimization for the capacitated vehicle routing problem

A multi-objective optimisation algorithm for the hot rolling batch scheduling problem

Development of hybrid evolutionary algorithms for production scheduling of hot strip mill

Contact Info

Product

Resources

About