Product sequencing and blending of raw materials to feed arc furnaces : a decision support system for a mining-metallurgical industry.
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Date
2022
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Abstract
A large amount of data available today and the complex situations present in the industry make decision support systems
increasingly necessary. This work deals with a problem of a mining-metallurgical industry in which the production of products
used to feed arc furnaces must be sequenced in work shifts. There is a due date and a quality specification for each product.
These products are generated from raw materials available in a set of silos and must satisfy the required quality specifications.
The aim is to minimize the total production time and the total tardiness. To solve it, we developed a decision support system that
applies a matheuristic algorithm to do the product schedule and determine the amount of raw material to produce each product.
In the proposed algorithm, the products generated in each work shift are chosen through a dispatch heuristic rule based on the
shortest production time. In turn, the amount of raw material to be used is calculated by solving a goal linear programming
formulation of a blending problem. We generate instances that simulate real cases to evaluate the developed algorithm. The
results show a good performance of the proposed algorithm, validating its use as a tool to support decision-making.
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Keywords
Heuristic, Operations research in industry
Citation
BACHAREL, R. de F.; SOUZA, M. J. F.; COTA, L. P. Product sequencing and blending of raw materials to feed arc furnaces: a decision support system for a mining-metallurgical industry. Journal of Control, Automation and Electrical Systems, v. 33, p. 1091–1102, 2022. Disponível em: <https://link.springer.com/article/10.1007/s40313-021-00837-3>. Acesso em: 06 jul. 2023.