Steelmaking And Continuous Casting Research Articles

A Distributionally Robust Scheduling Approach for Uncertain Steelmaking and Continuous Casting Processes

This article presents a new model to handle the cast break problem caused by small daily disruptions in the processing time of the steelmaking and continuous casting (SCC) production process. In this model, the exact distribution of the uncertain parameters is unknown, and support set, mean, and covariance information is used to describe the uncertain processing time. The problem aims to determine the assignments, sequences, and time points of the charges to be processed on corresponding machines. The main goal is to minimize the expected value of the production objective while reducing the number of cast break occurrences. The problem is solved in two steps. First, a subproblem is developed by fixing the sequences and the assignments of the charges. This subproblem is formulated as a distributionally robust chance-constrained (DRCC) model, in which the constraints are established with certain probabilities even when the uncertain processing times are in their worst cases. A dual approximation method is proposed to convert the model into a semidefinite programming problem so that it can be solved by standard solvers. Additionally, a linear programming approximation method is used to accelerate the solving procedure. A Tabu search algorithm incorporated with a speed-up strategy is also designed to determine the assignments and sequences of the charges. Both simulated data generated from different distributions and actual production data are used to test the efficacy of our model. Results of the numerical experiments show that the schedule obtained from the DRCC model is more robust, i.e., it causes fewer cast breaks than the nominal schedule obtained from a deterministic model.

An Efficient Augmented Lagrange Multiplier Method for Steelmaking and Continuous Casting Production Scheduling

The steelmaking and continuous casting (SCC) process is one of the vital processes in iron and steel plants since it determines chemical compositions of slabs and is often a bottleneck for iron and steel manufacturing. In this paper, we first develop a discrete-time mixed-integer linear programming (MILP) formulation for a new SCC scheduling problem where different processing routes are used to produce diversified and personalized slab products. To solve the proposed MILP formulation efficiently, we then propose a novel efficient solution algorithm using Augmented Lagrange multiplier method (e-ALM) through relaxation of the coupling constraints and incorporation of penalty components. A heuristic-based list scheduling approach as well as adjacent pairwise swap operations is used to construct a high-quality feasible schedule. A fine-adjusting strategy based on the subgradient direction is proposed to update Lagrangian multipliers dynamically to speed up the convergence. It is shown that the proposed e-ALM is able to generate optimal or near-optimal solutions with 5% optimality for 150 industrial-scale instances and outperform the commercial solver GAMS/CPLEX and the existing Lagrangian relaxation algorithms with better feasible solutions and smaller duality gap within the specified computational time.

Proactive and reactive scheduling of the steelmaking and continuous casting process through adaptive robust optimization

The schedule of a steelmaking and continuous casting (SCC) process defines the unit assignments of casts and ladles, and the processing sequence and timing in each unit. These decisions have to meet strict production requirements such as limited storage duration and continuous operation under various disruptions. Uncertain processing time is a common factor that may invalidate the initial schedule. Traditional deterministic reactive scheduling implements corrective measures for every disruption event by either repairing the deterministic schedule or rescheduling from scratch. In this work, we present a novel adaptive robust optimization method for SCC scheduling under uncertainty. Compared to the deterministic scheduling method, adaptive robust proactive scheduling generates an initial schedule immune to most major disruptions eliminating the need for most changes. Adaptive robust hybrid scheduling presents an attractive trade-off between solution conservatism and excessive scheduling modifications.

A robust optimization approach for integrated steel production and batch delivery scheduling with uncertain rolling times and deterioration effect

Efficient collaboration between various sub-processes of steel production is of considerable significance, which directly affects a product’s production cycle and energy consumption. However, current collaborative optimisation models and methods in steel production are still limited: (1) Most of the current collaborative manufacturing problems in steel production focus on obtaining joint schedule between steel-making and continuous casting (SCC), and the works considering continuous casting and hot rolling (CCHR) are very few. (2) The processing time is assumed as a constant in most of the existing SCC scheduling models. However, the rolling time of a product in hot rolling operation is actually uncertain and deteriorating. (3) Exact algorithms cannot be applied to solve the complicated collaborative optimisation problems because of their high complexities. To address these problems, we propose an integrated CCHR and batch delivery scheduling model where interval rolling time and linear deterioration effect are considered. With the concept of min–max regret value, we formulate the collaborative optimisation problem as a robust optimisation problem. Instead of using the exact algorithm, we develop an Improved Variable Neighborhood Search (IVNS) algorithm incorporated a novel population update mechanism and neighbourhood structures to solve the robust optimisation problem. Moreover, we develop an exact algorithm that combines CPLEX solver and two dynamic programming algorithms to obtain the maximum regret value of a given rolling sequence. The results of computational experiments show the excellent performance of the proposed algorithms. Abbreviations: IVNS: improved variable neighbourhood search; TOPSIS: technique for order of preference by similarity to ideal solution; PUM-TOPSIS: population update mechanism based on TOPSIS; DP: dynamic programming; NSs-PUC: neighbourhood structures based on the parameterised uniform crossover; SNRT: shortest normal rolling time; SNRT-DP: DP algorithm based on SNRT rule; BRKGA: biased random-key genetic algorithm; SCC: steelmaking and continuous casting; MINP: mixed integer nonlinear programme; CCHR: continuous casting and hot rolling; PSO: particle swarm optimisation; GA: genetic algorithm; VNS-HS: variable neighbourhood search and harmony search; HPSO + GA: hybrid PSO and GA; SA: simulated annealing; B&B: branch-and-bound; TPSO: two-phase soft optimisation; TSAUN: tabued simulated annealing with united-scenario neighbourhood; VNS: variable neighbourhood search; ABC: artificial bee colony; PRVNS: population-based reduced variable neighbourhood search; NS1: neighbourhood structure 1; NS2: neighbourhood structure 2; DE: differential evolution; WSR: Wilcoxon signed-rank test; ENS: exchange neighbourhood structure; IVNS-ENS: IVNS with ENS; RPI: relative percentage increase; ARPI: average RPI; SD: standard deviation.

A Metaheuristic Method for the Task Assignment Problem in Continuous-Casting Production

The steelmaking and continuous-casting (SCC) process in integrated iron and steel enterprises can be described as two stages: the upstream stage and downstream stage. Raw materials are transformed into molten steel in the upstream stage, while the downstream stage is responsible for transforming molten steel which is released at regular intervals and has a limited time for being turned into slabs. This article focuses on the task assignment problem in the downstream stage within the given information resulting from the upstream stage. This problem is formulated as a nonlinear mixed-integer programming model aimed at minimizing total tardiness within the resource constraints and time windows constraints for the tasks. An improved solution algorithm based on particle swam optimization is developed to efficiently solve the proposed model. Finally, computational experiments are implemented to evaluate the performance of the solution algorithm in terms of solution quality and computational time.

An Effective Heuristic Rescheduling Method for Steelmaking and Continuous Casting Production Process With Multirefining Modes

Job start-time delay often occurs in the steelmaking and continuous casting (SCC) production that changes constraints or assumptions on which the initial scheduling plan is based. The development of effective rescheduling method that allows the system to promptly react to such disruption becomes essential to improve productivity, reduce production costs, and enable efficient material and energy utilization. This paper tackles this challenge and presents a comprehensive analysis of start-time delay disturbance, its consequences, and strategies to resolve conflicts. A heuristic rescheduling algorithm is then proposed to allow the system to remain alert to this type of disruption in real SCC production, and to quickly react it with an optimal rescheduling plan that has the minimum total waiting time. The proposed methodology and algorithm are applied to and illustrated through both a simulated prototypical SCC system and Shanghai BaoSteel plant, a real industrial setting.

An Effective Approach for the Scheduling of Refining Process with uncertain iterations in Steel-making and Continuous Casting Process

Refining is a high temperature, high energy expenditure and large scale logistics machining process in the steel-making and continuous casting (SCC) production. The refining process is mainly characterized by the time-critical and component-critical design. However, the designed activities are often interlinked and quite uncertain; some of the stages may have to be iterated several times to meet the design criteria if the components of the molten steel do not meet the designed criteria for the casting process and the do not meet the established target dates. So, how to generate a robust and optimal schedule to handle the uncertainties for the refining process in a limited time by a computationally efficient manner is becoming critical for the production of iron and steel. After the seperable problem formulation is created, The stochastic dynamic programming method is adopt to the obtained subproblems which are relaxed by Lagrangian relaxation multipliers, the actual schedule is dynamically constructed based on the dual solution. The method has been tested by using practical data from the Shanghai Bashan steel plant in China and could get near optimal solutions in a limited time; the uncertain number of iterations is effectively handled for the production.

Near Optimal Scheduling of Steel-making and Continuous Casting Process Based on Charge Splitting Policy

The process of steel-making and continuous casting (SCC) is the bottleneck in the iron and steel production, how to take a quick response and make a good scheduling in a computationally efficient manner are the key factors for iron and steel production manufacturing productivity. In this paper, based on the time-index variables, we established a mixed integer-programming model for the scheduling problem considering the four performance indexes. Then, the charge splitting policy based on the relaxation of the no confliction and no break cast are proposed. The polynomial dynamic programming algorithm is presented to solve the corresponding relaxed problems. Based on the above approaches, an approximate optimization method with surrogate subgradient algorithm is proposed to solve the relaxed problem. The method has been tested by using practical data from the Shanghai Baoshan steel plant in China. Computational experiments demonstrate that the model and the algorithms developed can generate the satisfactory solutions and realizes rapidly response for the dynamic change.

A Prediction Method for Abnormal Condition of Scheduling Plan with Operation Time Delay in Steelmaking and Continuous Casting Production Process

In the steelmaking and continuous casting (SMCC) production process, the operation time delay often occurs which may lead to planned casting break or processing conflict so that the initial scheduling plan becomes unrealizable. Existing rescheduling methods with disturbances firstly classify the disturbances according to the disturbance type and disturbance quantity only by artificial experience or rules, and then directly adjust initial scheduling plan with corresponding rescheduling method. Those methods don’t analyze the influence degree of disturbances to the initial scheduling plan in detail, so the adjustment degree of initial scheduling plan is always too greater, which leads to the poor continuity and stability of initial scheduling plan. In this paper, the relation among operation time delay, planned casting break and processing conflict is deeply analyzed. Then a novel prediction method for abnormal condition of scheduling plan with operation time delay disturbance in SMCC production process is proposed including disturbance identification of operating time delay based on event-driven mechanism, analysis on charges based on reachability matrix, analysis on influence degree of disturbance and abnormal condition decision of initial scheduling plan. As a result, the real-time application shows that the proposed prediction method can timely and accurately predict the abnormal condition of the scheduling plan with operation time delay disturbance in SMCC production process, which can only adjust the affected charges that must to be rescheduled in the initial scheduling plan and reduce the frequency of complete rescheduling. The initial scheduling plan can also maintain the good continuity and stability.

A Rescheduling Method for Operation Time Delay Disturbance in Steelmaking and Continuous Casting Production Process

In the steelmaking and continuous casting (SMCC) production process, operation time delay may lead to casting break or processing conflict so that the initial scheduling plan becomes unrealizable. Existing research methods are difficult to guarantee the accuracy of the model and successful application to actual applications. The rescheduling problem in response to operation time delay is firstly analyzed. This is then followed by the establishment of a novel multi-objective nonlinear programming model (MONPM). In specifications, a three-stage rescheduling method is proposed including the batches splitting (BS), forward scheduling method (FSM) and backward scheduling method (BSM). As a result, the real-time application shows that the proposed rescheduling method efficiently ensures the continuous casting and dramatically shortens the redundant waiting time for molten steel in very short rescheduling time.

Dynamic Optimal Scheduling Method and Its Application for Converter Fault in Steelmaking and Continuous Casting Production Process

AbstractIn steelmaking and continuous casting (SMCC) production process, converter fault can lead to unexpected changes to the pre-specified converter-continuous caster production mode so that the original scheduling plan becomes unrealizable. In this paper, the dynamic scheduling problem in response to converter fault is firstly analyzed. This is then followed by the establishment of a novel multi-objective nonlinear programming model (MONPM) by introducing the production mode parameter α, production schedule parameter β and χ. The proposed method considers changes in production mode, production schedule of charge, the interval characteristics of processing time. In specific, a two-stage dynamic optimal scheduling method is proposed including the production path planning of charges (PPP) and the production time scheduling (PTS). As a result, a dynamic optimal scheduling software system (DOSSS) is developed and is successfully applied to the scheduling of the largest iron and steel company (BaoSteel) in China. The real-time application shows that the proposed method can efficiently reduce scheduling time, significantly increase the outputs of converters and dramatically shorten the redundant waiting time for molten steel.

Intelligent Optimization-Based Production Planning and Simulation Analysis for Steelmaking and Continuous Casting Process

Aiming at the limitations of the traditional mathematical model for production planning, a novel optimization model is proposed to improve the efficiency and performance for production planning in steelmaking and continuous casting (SCC) process. The optimization model combined with parallel-backward inferring algorithm and genetic algorithm is described. To analyze and evaluate the production plans, a simulation model based on cellular automata is presented. And then, the integrated system including the production plan optimization model and the simulation model is introduced to evaluate and adjust the production plan on-line. The test with production data in a steel plant shows that the optimization model demonstrates ability to deal with time uncertainty in production planning and to set up a conflict-free production plan, and the integrated system provides a useful tool for dynamically drawing and adjusting a production plan on-line. The average staying time of the production plan is about 5% shorter than that in a practical process.

Decision support system for the batching problems of steelmaking and continuous-casting production

This paper investigates two batching problems for steelmaking and continuous-casting (SCC) production in an integrated iron and steel enterprise. The tasks of the problems are to make the decisions as how to consolidate ordered slabs into charges, and then how to group charges into casts. The effective decisions on these batching problems can help to balance the requirements of materials in downstream production lines, improve the customer satisfaction levels, and reduce production costs (including reduction of open ordered slabs, less slabs quality upgrading, reduction of steel-grade changeovers, and reduction of inefficient utilization of tundishes lives). We first formulate the problems as integer-programming models by consider practical constraints and requirements, and then develop the two heuristic algorithms for the corresponding batching problems. By embedding above models and algorithms, we develop decision support system (DSS) software with interactive planning editor. The DSS has been tested by using practical data set collected from the steelmaking plant in Baosteel which is one of the most advanced iron and steel enterprises in China. Computational experiments demonstrate that the models and algorithms developed can generate the satisfactory solutions when they work together with the planning editor in the DSS.