A genetic algorithm for optimizing production in a cold rolled steel slitting line

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A genetic algorithm for optimizing production in a cold rolled steel slitting line

Biswajit Mahanty, Prabal Rakshit

Department of Industrial Engineering and Management, Indian Institute of Technology Kharagpur, Kharagpur, WB-721302, India.

DOI:

https://doi.org/10.7494/cmms.2013.1.0426

Abstract:

This paper presents a genetic algorithm for optimizing production in a cold rolled steel slitting line in a steel service center in India. The steel service center needs to generate a sequence of jobs for the slitter that involves the generation of a cutting pattern for each mother coil according to the customer order widths. A cutting pattern is an arrangement of the slitter knives for each mother coil under consideration. The following objectives are considered: 1) minimization of weight deviation for each customer order, 2) minimization of the slitter head setup time, and 3) minimization of the trim loss. The constraints include the following: 1) The sum of all customer order widths of a pattern should not exceed the width of the mother coil considered, 2) A customer order can be in excess or in deficit but not both, 3) A mother coil can have only one pattern associated with it, and 4) The customer order weight deviation should be within acceptable ranges. For the problem under consideration, a mother coil having the highest width is chosen and all the patterns possible from the given set of customer orders are generated using the Pierce algorithm. Each pattern is assigned a pattern number which is used for encoding in the genetic algorithm. The genetic algorithm selects those cutting patterns that generate trims under a specified limit, penalizes both over-production and under-production and penalizes each additional setup. The genetic algorithm is validated with a number of test problems. The application of the algorithm resulted in yield improvement to the tune of 5% and reduction of weight deviation for the customers to the tune of 15-20%. The genetic algorithm also generates a number of scheduling options for the steel service center.

Cite as:

Mahanty, B., & Rakshit, P. (2013). A genetic algorithm for optimizing production in a cold rolled steel slitting line. Computer Methods in Materials Science, 13(1), 160 – 165. https://doi.org/10.7494/cmms.2013.1.0426

Article (PDF):

Keywords:

Genetic algorithm, Optimization, Cutting pattern, Cold rolled steel slitting

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