A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry

Lin, Jian-hua; Liu, Min; Hao, Jinghua; Jiang, Sheng-Long

doi:10.1016/j.cor.2016.03.002

Cited by 60 publications

(24 citation statements)

References 20 publications

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“…To obtain a diverse Pareto front, Limbourg and Aponte provided the definitions of the hyper-volume and crowding distance based on intervals [24]. Following the dominance relation and the crowding distance based on intervals, they proposed the following strategy for sorting solutions to (1). The dominance relation is first utilized to assign each solution with a unique rank.…”

Section: B Dominance Relation and Crowding Distance Based On Intervalsmentioning

confidence: 99%

“…x=3, cp= [1,3] = [0.5, 1.5]. If only the left endpoints are utilized to measure the difference between ∆ 1 and ∆ 2 , we will conclude that there is no difference according to the results of [23] and [58].…”

Section: Dividing Decision Variables Based On Interval Similaritymentioning

confidence: 99%

“…According to the above theorem, we propose the following approach of dividing all the decision variables in (1). For x k and c p (t), we first calculate…”

Section: Dividing Decision Variables Based On Interval Similaritymentioning

confidence: 99%

“…Each of these optimization problems generally contains more than one objective conflicting with each other, and has the interval characteristic in at least one objective and (or) constraint. One representative instance is production planning in a steel-making continuous casting-hot rolling (SCC-HR) process [1]. The process can be formulated as an MOP with the interval characteristic.…”

Section: Introductionmentioning

confidence: 99%

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A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Gong

Zhang

et al. 2020

IEEE Trans. Evol. Computat.

148

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Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.

show abstract

Section: B Dominance Relation and Crowding Distance Based On Intervalsmentioning

confidence: 99%

Section: Dividing Decision Variables Based On Interval Similaritymentioning

confidence: 99%

“…According to the above theorem, we propose the following approach of dividing all the decision variables in (1). For x k and c p (t), we first calculate…”

Section: Dividing Decision Variables Based On Interval Similaritymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Gong

Zhang

et al. 2020

IEEE Trans. Evol. Computat.

148

View full text Add to dashboard Cite

show abstract

“…As a typical one-dimensional bin-packing problem, charge planning problem can be solved by some classic methods, such as constructive heuristics and intelligent optimization algorithms [12,15]. The latter attains the optimal solutions of the problems mainly through the simulations of actual system without further exploration the essential characteristics of the problem itself.…”

Section: Solving Strategymentioning

confidence: 99%

Optimal Charge Planning Model of Steelmaking Based on Multi-Objective Evolutionary Algorithm

Yang

Wang

Zou

et al. 2018

Metals

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Abstract:As having an important part of coordination control in steelmaking process, traditional production planning and scheduling technologies are developed with little consideration of the metallurgy mechanism, leading to lower feasibility for actual production. Based on current situation and requirements of steel plants, this paper focuses on the investigation of the charge plan from the view of metallurgy and establishes a charge planning model concerning the minimization of both the open order amount and the difference in due dates of the orders in each charge. A modified multi-objective evolutionary algorithm is proposed to solve the charge planning model of steelmaking process. By presenting a new fitness function, based on the rule of target ranking and introducing the Elitism strategy to construct the non-inferior solution set, the quality of solutions is improved effectively and the convergence of the algorithm is enhanced remarkably. Simulation experiments are carried out on the orders from actual production, and the proposed algorithm produces a group of optimized charge plans in a short time. The quality of the solutions is better than those produced by a genetic algorithm, modified partheno-genetic algorithm, and those produced manually to some extent. The simulation results demonstrate the feasibility and effectiveness of the proposed model and the algorithm.

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Energy Efficient Network Manufacturing System Using Controlled Elitist Non-dominated Sorting Genetic Algorithm

Ramakurthi

Manupati

Varela

et al. 2020

Proceedings of the International Conference of Mechatronics and Cyber- MixMechatronics - 2020

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A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry

Cited by 60 publications

References 20 publications

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Optimal Charge Planning Model of Steelmaking Based on Multi-Objective Evolutionary Algorithm

Energy Efficient Network Manufacturing System Using Controlled Elitist Non-dominated Sorting Genetic Algorithm

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