Coil Batching to Improve Productivity and Energy Utilization in Steel Production

Lixin Tang (), Ying Meng (), Zhi-Long Chen () and Jiyin Liu ()
Additional contact information
Lixin Tang: Liaoning Key Laboratory of Manufacturing System and Logistics, Institute of Industrial Engineering and Logistics Optimization, Northeastern University, Shenyang 110819, China
Ying Meng: Liaoning Key Laboratory of Manufacturing System and Logistics, Institute of Industrial Engineering and Logistics Optimization, Northeastern University, Shenyang 110819, China
Zhi-Long Chen: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Jiyin Liu: School of Business and Economics, Loughborough University, Leicestershire LE11 3TU, United Kingdom

Manufacturing & Service Operations Management, 2016, vol. 18, issue 2, 262-279

Abstract: 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, we show that our algorithms are superior to the 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 U.S. dollars and a large reduction of standard coal consumption and carbon dioxide emissions.

Keywords: steel production; batch annealing; batching decisions; integer programming; dynamic programming; branch-and-price-and-cut; tabu search (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2015.0558 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:18:y:2016:i:2:p:262-279

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().