Optimizing Cut Order Planning in the Garment Industry using Mixed-Integer Nonlinear Programming with Open-Source Solvers.

Abstract

The aim of this thesis is to develop and implement a mathematical model to optimize the fabric usage in garment manufacturing through Cut Order Planning (COP). The proposed model is based on a Mixed-Integer Nonlinear Programming (MINLP) formulation that integrates discrete decisions such as pile count and size assignment with nonlinear constraints related to fabric consumption. This model is inspired by the work of Ünal and Yüksel (2020) and reimplemented in an open-source environment using Pyomo and the SCIP solver. The approach allows for minimizing fabric waste while satisfying production constraints across multiple product types. To evaluate the efficiency and practic- ality of the solution, results were obtained for shirts, trousers, sweatshirts, and coats. A comparison with the original LINGO-based model was conducted, focusing on iteration count and constraint satisfaction, while taking hardware differences into account. Keywords: Cut Order Planning, Mixed Integer Nonlinear Programming, Fabric Optimization, Garment Industry, SCIP, Open-Source Optimization.