One Dimensional Cutting Stock Problem (1D-CSP) A New approach for Sustainable Trim Loss

2018 ◽  
Vol 6 (10) ◽  
pp. 265-271
Author(s):  
P. L. Powar ◽  
Siby Samuel
Keyword(s):  
Cutting Stock Problem ◽  
Cutting Stock ◽  
New Approach ◽  
One Dimensional ◽  
Trim Loss

A Least-Loss Algorithm for a Bi-Objective One-Dimensional Cutting-Stock Problem

2019 ◽  
Vol 6 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Hesham K. Alfares ◽  
Omar G. Alsawafy
Keyword(s):  
Real Life ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Benchmark Problems ◽  
Standard Size ◽  
One Dimensional ◽  
Small Item ◽  
Proposed Model ◽  
Trim Loss ◽  
Industrial Problem

This article presents a new model and an efficient solution algorithm for a bi-objective one-dimensional cutting-stock problem. In the cutting-stock—or trim-loss—problem, customer orders of different smaller item sizes are satisfied by cutting a number of larger standard-size objects. After cutting larger objects to satisfy orders for smaller items, the remaining parts are considered as useless or wasted material, which is called “trim-loss.” The two objectives of the proposed model, in the order of priority, are to minimize the total trim loss, and the number of partially cut large objects. To produce near-optimum solutions, a two-stage least-loss algorithm (LLA) is used to determine the combinations of small item sizes that minimize the trim loss quantity. Solving a real-life industrial problem as well as several benchmark problems from the literature, the algorithm demonstrated considerable effectiveness in terms of both objectives, in addition to high computational efficiency.

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.

Application of the Two-Stage One-Dimensional Cutting Stock Problem in the Steel Industry

2018 ◽  
Author(s):  
Jose Luis Santos ◽  
Joni Santos ◽  
Manuel Joao Ferreira ◽  
Nelson Alves ◽  
Miguel Guevara
Keyword(s):  
Steel Industry ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Stage ◽  
One Dimensional
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