Using genetic algorithms in solving the one-dimensional cutting stock problem in the construction industry

2004 ◽  
Vol 31 (2) ◽  
pp. 321-332 ◽  
Author(s):  
Adham A Shahin ◽  
Ossama M Salem
Keyword(s):  
United States ◽  
Genetic Algorithm ◽  
The United States ◽  
Optimization Techniques ◽  
Cutting Stock Problem ◽  
Waste Reduction ◽  
Cutting Stock ◽  
Construction Waste ◽  
One Dimensional ◽  
The One

In the United States, vast amounts of construction waste are produced every year. Construction waste accounts for a significant portion of the municipal waste stream of the United States. One-dimensional stocks are one of the major contributors to construction waste. Cutting one-dimensional stocks to suit needed project lengths results in trim losses, which are the main causes of one-dimensional stock waste. Although part of such waste is recyclable such as steel waste, reduction in the generation of waste can enhance the stock material usage and thereby increase the profit potential of the company. The traditional optimization techniques (i.e., linear programming and integer programming) suffer some drawbacks when they are used to solve the one-dimensional cutting stock problem (CSP). In this paper, a genetic algorithm (GA) model for solving the one-dimensional CSP (GA1D) is presented. Three real life case studies from a local steel workshop in Fargo, North Dakota have been studied, and their solutions (cutting schedules) using the GA approach are presented and compared with the actual workshop cutting schedules. The comparison shows a high potential of savings that could be achieved.Key words: construction waste management, waste reduction, genetic algorithm, GA, cutting stock problem, CSP, optimization, reinforcement steel optimization, rebar optimization.

scholarly journals A GENETIC ALGORITHM FOR THE ONE-DIMENSIONAL CUTTING STOCK PROBLEM WITH SETUPS

2014 ◽  
Vol 34 (2) ◽  
pp. 165-187 ◽  
Author(s):  
Silvio Alexandre de Araujo ◽  
Kelly Cristina Poldi ◽  
Jim Smith
Keyword(s):  
Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
One Dimensional ◽  
The One

Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem

2013 ◽  
pp. 32-45
Author(s):  
Julliany Sales Brandão ◽  
Alessandra Martins Coelho ◽  
João Flávio V. Vasconcellos ◽  
Luiz Leduíno de Salles Neto ◽  
André Vieira Pinto
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Model Problem ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Computational Results ◽  
New Approach ◽  
One Dimensional ◽  
The One

This paper presents the application of the one new approach using Genetic Algorithm in solving One-Dimensional Cutting Stock Problems in order to minimize two objectives, usually conflicting, i.e., the number of processed objects and setup while simultaneously treating them as a single goal. The model problem, the objective function, the method denominated SingleGA10 and the steps used to solve the problem are also presented. The obtained results of the SingleGA10 are compared to the following methods: SHP, Kombi234, ANLCP300 and Symbio10, found in literature, verifying its capacity to find feasible and competitive solutions. The computational results show that the proposed method, which only uses a genetic algorithm to solve these two objectives inversely related, provides good results.

Application of Genetic Algorithm to Minimize the Number of Objects Processed and Setup in a One-Dimensional Cutting Stock Problem

2011 ◽  
Vol 2 (1) ◽  
pp. 34-48
Author(s):  
Julliany Sales Brandão ◽  
Alessandra Martins Coelho ◽  
João Flávio V. Vasconcellos ◽  
Luiz Leduíno de Salles Neto ◽  
André Vieira Pinto
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Model Problem ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Computational Results ◽  
New Approach ◽  
One Dimensional ◽  
The One

This paper presents the application of the one new approach using Genetic Algorithm in solving One-Dimensional Cutting Stock Problems in order to minimize two objectives, usually conflicting, i.e., the number of processed objects and setup while simultaneously treating them as a single goal. The model problem, the objective function, the method denominated SingleGA10 and the steps used to solve the problem are also presented. The obtained results of the SingleGA10 are compared to the following methods: SHP, Kombi234, ANLCP300 and Symbio10, found in literature, verifying its capacity to find feasible and competitive solutions. The computational results show that the proposed method, which only uses a genetic algorithm to solve these two objectives inversely related, provides good results.

Effect of Applying Advanced Optimization Techniques for the One-Dimensional Cutting Stock Problem

2014 ◽  
Author(s):  
Vimal Savsani ◽  
Poonam Savsani ◽  
Prashant Arya
Keyword(s):  
Linear Optimization ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Np Hard ◽  
One Dimensional ◽  
Teaching Learning Based Optimization ◽  
The One ◽  
Teaching Learning

The one-dimensional cutting stock problem is a linear optimization problem that is categorized as NP-Hard. This problem has a large number of applications in a number of different industries. Though a number of traditional methods have been applied to solve this problem, these methods are not as effective as advanced optimization techniques to find the global optimum of NP-Hard problems. In this paper, a combination of three such advanced methods has been used to solve the Cutting Stock Problem: the firefly algorithm (FFA), the bat algorithm (BA) and the teaching-learning based optimization (TLBO). The results of provided by these algorithms are compared on the basis of the optimality of the solution and for three individual case studies as well as by the convergence of the algorithms. It was found that the teaching-learning based optimization technique performed well in both the optimality as well as the convergence.

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