Solution of the Rectangular Strip Packing Problem Considering a 3-Stage Guillotine Cutting Constraint with Finite Slitter Blades

In this study, we propose a new algorithm to solve the rectangular strip packing problem (RSPP), a variant of the cutting stock problem in which the mother materials have a common fixed width and infinite length. Based on the column-generation technique with three improvements, the proposed algorithm can solve large-scale problems involving tens of thousands of materials within a reasonable time, considering practical cutting constraints, i.e., the three-stage guillotine cutting constraint and the limitations of slitter blades. The proposed algorithm is evaluated in terms of its packing efficiency and calculation time.

Linear Integer Optimization Model for Two-Stage Guillotine Cutting Stock Problem Using Branch and Bound Method in the Garment Industry

This paper discusses the Two-Stage Guillotine Cutting Stock Problem (2GCSP) in the garment industry, namely how to determine the two-stage guillotine pattern that is used to cut fabric stocks into several certain size t-shirt materials that are produced based on the demand for each size of the shirt. 2GCSP is modeled in the form of Linear Integer Optimization and finding solutions using the Branch and Bound method. In this paper also presented a Graphical User Interface with Maple software as an interactive tool to find the best fabric stock cutting patterns. The results show that the optimal solution can be determined by solving numerically using the Branch and Bound method and Maple optimization packages. The solution is shown with an illustration of the pattern and the amount of fabric cut based on the pattern.