Heuristic Search for a Real-World 3D Stock Cutting Problem

Katerina Klimova () and Una Benlic ()
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Katerina Klimova: Satalia
Una Benlic: East China Jiaotong University

A chapter in Operations Research Proceedings 2019, 2020, pp 63-69 from Springer

Abstract: Abstract Stock cutting is an important optimisation problem which can be found in many industries. The aim of the problem is to minimize the cutting waste, while cutting standard-sized pieces from sheets or rolls of a given material. We consider an application of this problem arising from the packing industry, where the problem is extended from the standard one or two dimensional definition into the three dimensional problem. The purpose of this work is to help businesses determine the sizes of boxes to purchase so as to minimize the volume of empty space of their packages. Given the size of a real-world problem instances, we present an effective Adaptive Large Neighbourhood Search heuristic that is able to decrease the volume of empty space by an average of 22% compared to the previous approach used by the business.

Keywords: Cutting and packing; Adaptive neighborhood search; Heuristics (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-48439-2_8

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DOI: 10.1007/978-3-030-48439-2_8

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