NIPPON STEEL & SUMITOMO METAL TECHNICAL REPORT - No. 121 - Production Planning and Scheduling Technology for Steel Manufacturing Process

Sep 11, 2026

Steel manufacturing relies on a V-type production process that decouples products from natural raw materials to meet each order’s quality and delivery targets while accommodating large-lot variety arising from small-lot requests. Because the overall process is large-scale and complex, production planning and scheduling impose heavy burdens and require strong system-level support. This report outlines the development status and future prospects of optimization algorithms for decision support in production planning and scheduling tasks.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages17

Reading Time6 min

AccessFree to download

1. Introduction

V-type flow shop characteristics

Planning and scheduling challenges

System-based decision support demand

2. Production and logistics optimization cases

3. Scheduler balancing productivity and cost

4. Explicit solution for hot rolling constraints

5. Machine-learning estimation of standard production periods

6. Weekly schedule planning toward just-in-time production

7. Discrete event simulation for logistics regulation

NIPPON STEEL & SUMITOMO METAL TECHNICAL REPORT No. 121 MARCH 2019 UDC 658 . 51 : 669 . 1 Production Planning and Scheduling Technology for Steel Manufacturing Process Kuniharu ITO* Masanori SHIOYA Masatoshi AGO Tetsuaki KUROKAWA Hirokazu KOBAYASHI Junichi MORI Abstract The steel manufacturing process is a V-type production process that separates products from natural raw materials so as to satisfy product quality and delivery times of each order while orienting large lot variety of small lot orders, making large lots from each process. Because of the large-scale and complicated production process, the burden of production planning and scheduling work is high, and support needs by system technology are strong. In this paper, we describe the development status and future prospects of optimization algorithms to support decision making in our production planning and scheduling tasks. 1. Introduction In steel manufacturing processes, various types of steel products are manufactured from iron ore, coal, and other raw materials imported from overseas based on requests by customers in various industries (e.g., automobiles, shipbuilding, bridges, and home appliances) through processes from blast furnace to converter, continuous casting, rolling, annealing, and surface treatment. The production pattern is V-type (branching) flow shop. Product specifications include various requirements based on the application of products such as quality of materials (e.g., strength and toughness), grades of the inside and surface of slabs, and size (e.g., thickness and width) . Specifications range in number from several thousands to tens of thousands although this varies depending on the product type. In addition, their manufacturing conditions consist of a combination of various factors such as molten steel components, rolling size, annealing temperature, and plating type. The variety of manufacturing conditions is equivalent to that of product specifications. Production planning and scheduling determine processing timing and sequence in each manufacturing process for each order while manufacturing conditions and delivery date in each process are satisfied. Comprehensive judgment in consideration of quality, costs, delivery dates, and other various performance indexes is required. In the steel manufacturing processes, in particular, large lot production in which products with the same manufacturing conditions are continuously manufactured is advantageous from the aspects of quality and costs (including profitability) and thereby such production has been targeted. However, as explained above, conditions for manufacturing products vary from process to process and delivery dates are also different. Therefore, it is difficult to plan a single production schedule throughout all manufacturing processes while balancing the quality, costs, and delivery dates, so such work depends on the expertise of experienced workers. Meanwhile, technologies for supporting production planning and scheduling from the aspect of systems are being increasingly demanded because one generation of experienced planners has been giving way to another recently, manufacturing conditions are becoming more difficult due to a shift to high-grade steel, and workloads need to be reduced toward work-style reform. In addition, regarding computer technologies, in addition to the enhanced performance of computers themselves, various algorithms represented by mathematical optimization solvers have been advanced and they have enabled support by systems in domains for which practical use used tobe difficult. Against such a background, Nippon Steel & Sumitomo Metal Corporation has been developing technologies for supporting production planning and scheduling in various manufacturing processes such as raw materials, steelmaking, hot rolling, and logistics. Chapter 2 introduces cases in which Nippon Steel & Sumitomo Metal worked to optimize production and logistics simulta

FAQs

What makes production planning and scheduling difficult in steel manufacturing?

The process is large-scale and complex, with many product specifications and manufacturing conditions that vary by process. Quality, cost, and delivery dates must be balanced, making comprehensive scheduling hard to unify.

What process structure is emphasized for steel production in the report?

The report describes a V-type (branching) flow shop, where products must be produced to satisfy order-specific quality and delivery-time requirements.

How does the report propose system support for planners?

It focuses on developing optimization algorithms and decision-support systems, including machine learning and mathematical programming, to assist planning and scheduling across processes.

Sep 11, 2026

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