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.

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 Rough set and teaching learning based optimization technique for optimal features selection

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Suresh Satapathy ◽  
Anima Naik ◽  
K. Parvathi
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Optimization Technique ◽  
Feature Selection Method ◽  
Optimization Techniques ◽  
Features Selection ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

AbstractRough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough Set theory with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.

A balance approach for the one-dimensional multiple stock size cutting stock problem with setup cost

2016 ◽  
Vol 230 (12) ◽  
pp. 2182-2189 ◽  
Author(s):  
Dianjian Wu ◽  
Chunping Yan
Keyword(s):  
Pattern Generation ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Setup Cost ◽  
Stock Size ◽  
One Dimensional ◽  
Setup Costs ◽  
Balance Approach ◽  
The One ◽  
Cutting Plan

A balance approach is presented to solve one-dimensional multiple stock size cutting stock problem with setup cost. The approach first utilizes a sequential pattern generation algorithm to generate a series of cutting plans based on each stock size, respectively. Then, a measure standard of cost balance utilization is used to select a current optimized cutting pattern from a cutting plan corresponding to each stock size. All item demands are dealt by the previous two steps to obtain many optimized cutting plans, and an ideal cutting plan is extracted according to the minimum sum of stock and setup costs at last. The approach is applied to two tests, and the computational results demonstrate that it possesses good cost adaptability and optimization performance.

scholarly journals Minimal Proper Non-IRUP Instances of the One-Dimensional Cutting Stock Problem

2014 ◽  
Author(s):  
Vadim M. Kartak ◽  
Artem V. Ripatti ◽  
Guntram Scheithauer ◽  
Sascha Kurz
Keyword(s):  
Cutting Stock Problem ◽  
Cutting Stock ◽  
One Dimensional ◽  
The One
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