2D Cutting Stock Problem Using Hybrid 3-D Overlapped Grouping Genetic Algorithm

Abstract

The cutting stock problem (CSP) is a business problem that arises in many areas, particularly in manufacturing industries where a given stock material must be cut into a smaller set of shapes. It has gained a lot of attention for increasing efficiency in industrial engineering, logistics and manufacturing. This paper presents a hybrid new 3-D overlapped grouping Genetic Algorithm (GA) that solves two-dimensional cutting stock problems for nesting the rectangular shapes. The objective is the minimization of the wastage of the sheet material which leads to maximizing material utilization and the minimization of the setup time. The model and its results are compared with real life case study from a steel workshop in a bus manufacturing factory. The effectiveness of the proposed approach is shown by comparing and shop testing of the optimized cutting schedules. The results reveal its superiority in terms of waste minimization comparing to the current cutting schedules and show that our approach outperforms existing heuristic algorithms. The whole procedure can be completed in a reasonable amount of time by the developed optimization program.