Integrated Charge Batching and Casting Width Selection at Baosteel

Tang, Lixin; Wang, Gongshu; Chen, Zhilong

doi:10.1287/opre.2014.1278

Cited by 82 publications

(27 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The order (charge) batching problem is to decide how to consolidate orders into charges (casts) considering the compatibility of any two orders (charges) in the same batch. Tang et al (2014) investigate a joint charge batching and casting width selection problem arising in the continuous casting operation at Baosteel. Tang et al (2011) study a batching problem in the melting process to group orders into batches and specify the size and the grade of slabs for each batched order.…”

Section: Related Literature and Problem Complexitymentioning

confidence: 99%

Coil Batching to Improve Productivity and Energy Utilization in Steel Production

Tang

Meng

Chen

et al. 2016

M&SOM

Self Cite

View full text Add to dashboard Cite

This paper investigates a practical batching decision problem that arises in the batch annealing operations in the cold rolling stage of steel production faced by most large iron and steel companies in the world. The problem is to select steel coils from a set of waiting coils to form batches to be annealed in available batch annealing furnaces and choose a median coil for each furnace. The objective is to maximize the total reward of the selected coils less the total coil-coil and coil-furnace mismatching cost. For a special case of the problem that arises frequently in practical settings where the coils are all similar and there is only one type of furnace available, we develop a polynomial-time dynamic programming algorithm to obtain an optimal solution. For the general case of the problem which is strongly NP-hard, an exact branch-and-price-and-cut solution algorithm is developed using a column and row generation framework. A variable reduction strategy is also proposed to accelerate the algorithm. The algorithm is capable of solving medium size instances to optimality within a reasonable computation time. In addition, a tabu search heuristic is proposed for solving larger instances. Three simple search neighborhoods as well as a sophisticated variable depth neighborhood are developed. This heuristic can generate near-optimal solutions for large instances within a short computation time.Using both randomly generated and real-world production data sets, it is shown that our algorithms are superior to a typical rule-based planning approach used by many steel plants. A decision support system that embeds our algorithms was developed and implemented at Baosteel to replace their rule-based planning method. The use of the system brings significant benefits to Baosteel, including an annual net profit increase of at least 1.76 million US Dollars, and a large reduction of standard coal consumption and carbon dioxide emissions.

show abstract

Section: Related Literature and Problem Complexitymentioning

confidence: 99%

Coil Batching to Improve Productivity and Energy Utilization in Steel Production

Tang

Meng

Chen

et al. 2016

M&SOM

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are many algorithms for various optimization problems in steel production, e.g., Tang et al [16] and [17]. However, for sequence-based scheduling problems, TS has been shown to be one of the most effective local search techniques able to avoid being trapped at a local optimum and to find near-optimal solutions, e.g., Nowicki and…”

Section: The Tabu Search Algorithmmentioning

confidence: 99%

“…The loading capacities of the smallest and the biggest ships are about 200 tons and 2000 tons, respectively. The average weight of a coil is about 16 tons. Therefore, the number of coils that one ship can carry ranges from 12 to 120.…”

Section: Introductionmentioning

confidence: 99%

Modeling and solution for the ship stowage planning problem of coils in the steel industry

Tang

Liu

Yang

et al. 2015

Naval Research Logistics

Self Cite

View full text Add to dashboard Cite

We consider a ship stowage planning problem where steel coils with known destination ports are to be loaded onto a ship. The coils are to be stowed on the ship in rows. Due to their heavy weight and cylindrical shape, coils can be stowed in at most two levels. Different from stowage problems in previous studies, in this problem there are no fixed positions on the ship for the coils due to their different sizes. At a destination port, if a coil to be unloaded is not at a top position, those blocking it need to be shuffled. In addition, the stability of ship has to be maintained after unloading at each destination port. The objective for the stowage planning problem is to minimize a combination of ship instability throughout the entire voyage, the shuffles needed for unloading at the destination ports, and the dispersion of coils to be unloaded at the same destination port. We formulate the problem as a novel mixed integer linear programming model. Several valid inequalities are derived to help reducing solution time. A tabu search (TS) algorithm is developed for the problem with the initial solution generated using a construction heuristic. To evaluate the proposed TS algorithm, numerical experiments are carried out on problem instances of three different scales by comparing it with a model‐based decomposition heuristic, the classic TS algorithm, the particle swarm optimization algorithm, and the manual method used in practice. The results show that for small problems, the proposed algorithm can generate optimal solutions. For medium and large practical problems, the proposed algorithm outperforms other methods. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 564–581, 2015

show abstract

“…Traditional research on iron and steel planning and optimization usually focuses on production itself. 1) However, with the increase of energy price, the cost of energy consumption covers 20-35% of the whole iron & steel production cost. And, the comprehensive energy consumption of China is much higher than that of the world with the energy utilization being only 30%.…”

Section: Introductionmentioning

confidence: 99%

On-line Energy Allocation Based on Approximate Dynamic Programming for Iron and Steel Industry

2016

Self Cite

View full text Add to dashboard Cite

Energy allocation in iron and steel industry is the assignment of available energy to various production users. With the increasing price of energy, a perfect allocation plan should ensure that nothing gets wasted and no shortage. This is challenging because the energy demand is dynamic due to the changes of orders, production environment, technological level, etc. This paper try to realize on-line energy resources allocation under the situation of dynamic production plan and environment based on typical energy consumption process of steel enterprises. Without definite analytical model, it is a tough task to make the energy allocation plan tracks the dynamic change of production environment in real time. This paper proposes to deal with dynamic energy allocation problem by interactive learning with time-varying environment using Approximate Dynamic Programming method. The problem is formulated as a dynamic model with variable right-hand items, which is an updated energy demand obtained by on-line learning. Reinforcement learning method is designed to learn the energy consumption principle from the historical data to predict energy consumption level corresponding to current production environment and the production plan in future horizon. Using the prediction results, on-line energy allocation plan is made and its performance is demonstrated by comparison with static allocation method.

show abstract

Integrated Charge Batching and Casting Width Selection at Baosteel

Cited by 82 publications

References 30 publications

Coil Batching to Improve Productivity and Energy Utilization in Steel Production

Coil Batching to Improve Productivity and Energy Utilization in Steel Production

Modeling and solution for the ship stowage planning problem of coils in the steel industry

On-line Energy Allocation Based on Approximate Dynamic Programming for Iron and Steel Industry

Contact Info

Product

Resources

About