Problem In Steel Industry Research Articles

An adaptive differential evolution algorithm with DBSCAN for the integrated slab allocation problem in steel industry

In this study, an integrated slab allocation problem (ISAP) is investigated, aiming to optimise inventory management in the steel production process of hot rolling. Different from previous studies, three sets of decisions are made simultaneously. The first is to allocate open-order slabs to orders to reduce inventory slabs and improve slab utilisation. The second is to reallocate customer-order slabs to orders to shorten the order completion time and improve customer satisfaction. The third is to group the remaining open-order slabs into virtual orders to ensure continuous production and reduce inventory pressure. The problem is formulated as an original integer programming mathematical model. A novel improved differential evolution algorithm with self-adaptive mutation and parameter selection strategies is proposed to solve the ISAP. In particular, a special encoding and decoding method is designed to address the issue of transformation between the slab allocation scheme and algorithm individual. A density-based spatial clustering of applications with noise algorithm and random swap local search are embedded into the framework of the differential evolution algorithm. Finally, experiments are conducted to evaluate the proposed algorithm, and the numerical results demonstrate its superiority in solving the ISAP.

Logistics optimisation of slab pre-marshalling problem in steel industry

We study the slab pre-marshalling problem to re-position slabs in a way that the slabs are stored in the least number of stacks and each stack contains only the slabs of the same group, which can be utilised interchangeably. In this way, when a slab of any group is required, the topmost slab can always be picked up without shuffling. During pre-marshalling, however, at most two slabs can be moved by one operation. In this paper, we present a network model with three valid inequalities to solve this problem. With a small amount of labelled data from the model approach, a self-training technique is applied to train a function for predicting the best next move. Then, a new hybrid algorithm is developed to solve the practical problems by combining the self-training technique, heuristics, and the branch-and-bound algorithm with five dominance rules. The experimental results demonstrate the effectiveness of this network model and valid inequalities, and the performances of different components of this algorithm. The new algorithm produces high-quality solutions within seconds.

Modelling and discrete differential evolution algorithm for order rescheduling problem in steel industry

Order management is a critical and complicated issue in the production process of iron and steel industry, since orders are the bridge between customers and semi-finished/final products in different units. Usually, the scheduling of orders is arranged by skilled planners. However, the initial scheduling may be infeasible during the production process due to the dynamic and frequent variation of production environment. This paper investigates a practical order rescheduling problem to adapt various changes that affect the normal production. The problem is formulated as a mixed integer programming mathematical model considering the original objective, the deviation from the initial scheduling and the equilibrium of production capacity. A discrete differential evolution algorithm with new mutation and crossover operators is proposed to find near-optimal solutions of this problem. Computational experiments are presented on both randomly generated instances and the instances from the practical production. Experimental results illustrate that the proposed algorithm could obtain better solutions compared with four standard differential evolution algorithms and the manual method. Furthermore, a production data based practical decision support system embedding the model and algorithm is developed to monitor the production process, diagnose whether there are high-impact changes of the orders and units, and make rescheduling decisions if necessary.

Data imputation for gas flow data in steel industry based on non-equal-length granules correlation coefficient

In the field of data-driven based modeling and optimization, the completeness and the accuracy of data samples are the foundations for further research tasks. Since the byproduct gas system of steel industry is rather complicated and its data-acquisition process might be frequently affected by the unexpected operational factors, the data-missing phenomenon usually occurs, which might lead to the failure of model establishment or inaccurate information discovery. In this study, a data imputation method based on the manufacturing characteristics is proposed for resolving the data-missing problem in steel industry. A novel correlation analysis, named by non-equal-length granules correlation coefficient (NGCC), is reported, and the corresponding model based on Estimation of Distribution Algorithm (EDA) is established to study the correlation of the similar procedures. To verify the performance of the proposed method, this study considers three typical features of the gas flow data with different missing ratios. The experiment results indicate that it is greatly effective for the missing data imputation of byproduct gas, and exhibits better performance on the accuracy compared to the other methods.

Scenario-based Modeling Approach and Scatter Search Algorithm for the Stochastic Slab Allocation Problem in Steel Industry

A common problem encountered in steel companies is that of allocating the surplus slabs to customer orders so as to minimize the total cost of production and inventory. Due to many unpredictable events arising in practical manufacture environment, slab yields and customer demands are full of uncertainties. This paper focuses on such uncertainties and studies the stochastic version of the slab allocation problem that has received little attention in the literature. Using a scenario-based approach, we formulate the problem as a mixed integer linear programming (MILP) model. To make the MILP model more concision, we reformulate it with less variables and constraints by using a scenarios aggregation approach. The commercial optimization software such as IBM ILOG CPLEX can solve the model to optimality for small and medium scale instances, but fail to solve large scale instances to optimality. Thus, a scatter search algorithm with directed local search based on follow-up technique is proposed to solve the problem approximately. Moreover, we introduce a random perturbation strategy to avoid search process being tapped in local optimum. Computational results on randomly generated instances show that the proposed algorithm is effective.

A fuzzy goal programming approach with priority for channel allocation problem in steel industry

A model is presented to address a steel supplier's channel allocation problem that includes decisions of channel mix and capacity allocation for each distribution channel. The problem has been formulated as a fuzzy mixed integer multiple goal programming problem that includes business competitive advantages such as maximizing net profits, minimizing the rate of end user claims, and minimizing the rate of late lading, and is subject to constraints regarding manufacturing capacity, customer's demand, channel capacity, channel quota flexibility, budget limitations, and so on. Realistic data from Taiwan's largest steel company is implemented for the effectiveness of the model. The proposed model can also be applied to the allocation problem in other industries with preemptive priority and desired achievement level in a fuzzy environment.

A New ILS Algorithm for Cast Planning Problem in Steel Industry

Cast planning is a practical problem frequently encountered in steel industry. Its task is to group charges into batches (casts) with respect to the similarities of steel-grade and dimensions between charges, taking account of the practical technique constraints on life-span of tundish. Effective cast planning can reduce the changeover cost of charges and enhance the productivity of continuous casters. The objective under our consideration is to minimize the total dissimilarity costs between the charges in the same casts, to minimize the number of casts and the number of unselected charges. A quadratic integer programming model with multiple objectives for this problem is formulated. It is NP-complete, and so an iterated local search (ILS) algorithm is developed for the problem. In this algorithm, cyclic transfer neighborhood is adopted, in which several charges are transferred among casts simultaneously as a manner of cycle. A new kick strategy is developed with the idea of assigning charges to different casts according to the dissimilarity costs between them and the casts’ central charges identified by the current solution. Computational results using real data from an advanced iron & steel company in China indicate that the ILS algorithm provides optimal solutions for small instances, and better near-optimal solutions for larger instances compared with a linear solver, Lingo 8.0, used to solve the equivalent linear integer programming obtained by transforming the original model. Totally, 92.3% of the instances are solved to global optima by the ILS algorithm while it is possible that the other 9.7% instances also are solved to optimal, which indicates the efficiency of the algorithm. At the same time, the algorithm also provides better solutions than the ones obtained by the current system used in the company.