Composite Stock Cutting through Simulated Annealing†
نویسندگان
چکیده
This paper explores the use of Simulated Annealing as an optimization technique for the problem Composite Material Stock Cutting. The shapes are not constrained to be convex polygons or even regular shapes. However, due to the composite nature of the material, the orientation of the shapes on the stock is restricted. For placements of various shapes, we show how to determine a cost function, annealing parameters, and performance.
منابع مشابه
Solving an one-dimensional cutting stock problem by simulated annealing and tabu search
A cutting stock problem is one of the main and classical problems in operations research that is modeled as Lp < /div> problem. Because of its NP-hard nature, finding an optimal solution in reasonable time is extremely difficult and at least non-economical. In this paper, two meta-heuristic algorithms, namely simulated annealing (SA) and tabu search (TS), are proposed and deve...
متن کاملThe trim loss concentration in one-dimensional cutting stock problem (1D-CSP) by defining a virtual cost
Nowadays, One-Dimensional Cutting Stock Problem (1D-CSP) is used in many industrial processes and re-cently has been considered as one of the most important research topic. In this paper, a metaheuristic algo-rithm based on the Simulated Annealing (SA) method is represented to minimize the trim loss and also to fo-cus the trim loss on the minimum number of large objects. In this method, the 1D-...
متن کاملA Distributed Hybrid Algorithm for Composite Stock Cutting
The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we introduce a new hybrid algorithm called Mean Field Genetic Algorithm (MGA) which combines the benefit of rapid convergence property of Mean Field Annealing (MFA) and the...
متن کاملApplying Simulated Annealing and the No Fit Polygon to the Nesting Problem
This paper presents a new method to pack convex polygons into bins (the nesting problem). To do this polygons are placed in rows within bins using a metaheuristic algorithm (simulated annealing) and by utilising the No Fit Polygon. We show that simulated annealing out performs hill climbing. The nesting algorithm is described in detail, along with various aspects that have been incorporated in ...
متن کاملComparison of Meta-Heuristic Algorithms for Clustering Rectangles
In this paper we consider a simplified version of the stock cutting (two-dimensional bin packing) problem. We compare three meta-heuristic algorithms (genetic algorithm (GA), tabu search (TS) and simulated annealing (SA)) when applied to this problem. The results show that tabu search and simulated annealing produce good quality results. This is not the case with the genetic algorithm. The prob...
متن کامل