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.
Highlights
Along with the advance of manufacturing technology and information technology at home and abroad, a series of works about “Intelligent Manufacturing” are being carried out in succession [1,2,3,4].As having an important role in manufacturing industries, the iron and steel industry proceeds in some explorations of intelligent manufacturing from the perspective of equipment technique to planning and scheduling in order to keep competitiveness in metallurgical industry [5,6,7,8]
The due date differences of the five solutions obtained from TR-Multi-objective evolutionary algorithm (MOEA) were all remarkably lower than those of GA and modified partheno-genetic algorithm (MPGA), and the improved degrees of the worst solution of 1971.645 respectively achieved 13.3% and 8.3% in contrast to the ones of two other algorithms
Focused on the charge planning problem in special steel plants of long products, a mathematical model is established aiming at the optimization of both due date difference and open order amount, model is established aiming at the optimization of both due date difference and open order amount, and a modified multi-objective evolutionary algorithm based on target ranking is presented to solve this model
Summary
Along with the advance of manufacturing technology and information technology at home and abroad, a series of works about “Intelligent Manufacturing” are being carried out in succession [1,2,3,4]. The steelmaking-continuous casting process is the core section in steel production [9]. According to the characteristics of billet continuous casting process of spring steel, Wang et al [11] built a new charge plan model by taking into account the constraints of steel grades, dimensions, and due dates; a modified partheno-genetic algorithm (MPGA) was established to search near-optimum solutions. Lin et al [15] introduced a new concept of order-set to investigate the integrated production planning (IPP) problem for steelmaking continuous casting-hot rolling (SCC-HR) process and proposed a modified interval multi-objective optimization evolutionary algorithm (MI-MOEA). This work is to offer a practical charge planning scheme for special steel plants through building a multi-objective optimization model and developing a modified multi-objective evolutionary algorithm.
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