Integrated problem of soaking pit heating and hot rolling scheduling in steel plants

Li, Feng; Zhang, Yanyan; Wang, Han; Lai, Xiaofan

doi:10.1016/j.cor.2019.04.016

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…They proposed a model knowledge representation and intelligent matching mechanism. Li et al [17] researched the integrated problem of soaking pit heating and hot rolling scheduling in steel plants. They provided a fast heuristic algorithm with worst-case bound to generate nearoptimal solutions, and a branch and bound algorithm to solve the problem to optimality.…”

Section: Integration Optimization Problemmentioning

confidence: 99%

Improved Differential Evolution Algorithm for Slab Allocation and Hot-Rolling Scheduling Integration Problem

et al. 2023

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To reduce logistics scheduling costs and energy consumption, this paper studies the slab allocation and hot-rolling scheduling integrated optimization problem that arises in practical iron and steel enterprises. In this problem, slabs are first allocated to orders and then sent to heating furnaces for heating; then, they are sent to a hot-rolling mill for rolling. A 0–1 integer programming model is established to minimize the attribute difference in the allocation cost between slabs and orders, the switching cost of hot-rolling processing, and waiting times after slabs reach rolling mills. Given the problem’s characteristics, an improved differential evolution algorithm using a real-number coding method is designed to solve it. Three different heuristic algorithms are proposed to improve the quality of solutions in the initial population. Multiple parent individuals participate in the mutation operation, which increases the population diversity and prevents the algorithm from falling into the local optimum prematurely. Experiments on 14 sets of real production data from a large domestic iron and steel plant show that our improved differential evolution algorithm generates significantly better solutions in a reasonable amount of time compared with CPLEX, the simulated artificial method, and the classical differential evolution algorithm, and it can be used by practitioners.

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Section: Integration Optimization Problemmentioning

confidence: 99%

Improved Differential Evolution Algorithm for Slab Allocation and Hot-Rolling Scheduling Integration Problem

et al. 2023

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“…Xia et al [21] addressed the production scheduling problem of furnace hot rolling section in the iron and steel industry with minimizing temperature deviation of slab, energy consumption and oxidation loss. Li and Tian [22] and Li et al [23] formulated the production scheduling problem of the furnace hot rolling section in the iron and steel industry as a mixed integer linear programming model. Tan et al [24] established nonlinear mathematical models for a continuous casting furnace hot rolling production scheduling problem in the iron and steel industry with the objectives of minimizing energy waste and energy requirement.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(1) To the best of our knowledge, the existing studies focus on the production scheduling of furnace hot rolling section in the iron and steel industry [10,[15][16][17][18][19][20][21][22][23][24][25][26][27]. A few scholars such as De Ladurantaye et al [28] discussed the production scheduling of furnace hot rolling section in the aluminum industry, but this research field has received rather limited attention in the scientific literature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results come from the following aspects. Firstly, to tackle the scheduling problem in the furnace hot rolling section, most research concentrates on the iron and steel industry [10,[15][16][17][18][19][20][21][22][23][24][25][26][27], and the aluminum industry is hardly considered in the literature [28]. Even if the aluminum and steel industries have similar production processes, which also have different characteristics, the two types of production cannot be treated as the same.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

et al. 2021

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In order to reduce the energy consumption of furnaces and save costs in the product delivery time, the focus of this paper is to discuss the uncertainty of demand in the rolling horizon and to globally optimize the sustainability of the production in the aluminum furnace hot rolling section in environmental and economic dimensions. First, the triples α/β/γ are used to describe the production scheduling in the aluminum furnace hot rolling section as the scheduling of flexible flow shop, satisfied to constraints of demand uncertainty, operation logic, operation time, capacity and demand, objectives of minimizing the residence time of the ingot in the furnace and minimizing the makespan. Second, on the basis of describing the uncertainty of demand in rolling horizon with the scenario tree, a multi-objective mixed integer linear programming (MILP) optimization model for sustainable production in the aluminum furnace hot rolling section is formulated. Finally, an aluminum alloy manufacturer is taken as an example to illustrate the proposed model. The computational results show that when the objective weight combination takes the value of α=0.7, β=0.3, the sustainability indicators of the environmental and economic dimensions can be optimized to the maximum extent possible at the same time. Increasingly, managerial suggestions associated with the trade-off between environmental and economic dimensions are presented. Scheduling in the rolling horizon can optimize the production process of the aluminum furnace hot rolling section globally, indicating that it is more conducive to the sustainable development of the environment and economic dimensions than scheduling in a single decision time period.

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“…A preliminary study of the specific industrial setup which motivated our research was done in Ackermann et al (2020+). For studies from different areas of application we refer, for instance, to Li et al (2015), Zhou et al (2016), Tan et al (2018), Chen et al (2019), Li et al (2019). The problems typically stem from various industries, e.g.…”

Section: Literaturementioning

confidence: 99%

Scheduling a proportionate flow shop of batching machines

Hertrich

Weiß

Ackermann

et al. 2020

J Sched

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In this paper we study a proportionate flow shop of batching machines with release dates and a fixed number $$m \ge 2$$ m ≥ 2 of machines. The scheduling problem has so far barely received any attention in the literature, but recently its importance has increased significantly, due to applications in the industrial scaling of modern bio-medicine production processes. We show that for any fixed number of machines, the makespan and the sum of completion times can be minimized in polynomial time. Furthermore, we show that the obtained algorithm can also be used to minimize the weighted total completion time, maximum lateness, total tardiness and (weighted) number of late jobs in polynomial time if all release dates are 0. Previously, polynomial time algorithms have only been known for two machines.

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Integrated problem of soaking pit heating and hot rolling scheduling in steel plants

Cited by 13 publications

References 26 publications

Improved Differential Evolution Algorithm for Slab Allocation and Hot-Rolling Scheduling Integration Problem

Improved Differential Evolution Algorithm for Slab Allocation and Hot-Rolling Scheduling Integration Problem

Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

Scheduling a proportionate flow shop of batching machines

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