An iterative sequential heuristic procedure to a real-life 1.5-dimensional cutting stock problem

2006 ◽  
Vol 175 (3) ◽  
pp. 1870-1889 ◽  
Author(s):  
X. Song ◽  
C.B. Chu ◽  
Y.Y. Nie ◽  
J.A. Bennell
Keyword(s):  
Real Life ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Heuristic Procedure

scholarly journals An analytics-based heuristic decomposition of a bilevel multiple-follower cutting stock problem

OR Spectrum ◽  
2021 ◽  
Author(s):  
Adejuyigbe O. Fajemisin ◽  
Laura Climent ◽  
Steven D. Prestwich
Keyword(s):  
Real Life ◽  
Forest Harvesting ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Decomposition Approach ◽  
New Class ◽  
Bilevel Problem ◽  
Programming Techniques ◽  
Bilevel Problems ◽  
Better Than

AbstractThis paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k-medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k-medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approach.

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.

An Improved Heuristic Procedure for a Nonlinear Cutting Stock Problem

1976 ◽  
Vol 23 (1) ◽  
pp. 78-86 ◽  
Author(s):  
I. Coverdale ◽  
F. Wharton
Keyword(s):  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Heuristic Procedure

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

2014 ◽  
Author(s):  
Maged R. Rostom ◽  
Ashraf O. Nassef ◽  
Sayed M. Metwalli
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Sheet Material ◽  
Real Life ◽  
Setup Time ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Grouping Genetic Algorithm ◽  
Material Utilization

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.

scholarly journals Modification of Haessler’s sequential heuristic procedure for the one-dimensional cutting stock problem with setup cost

Production ◽  
2018 ◽  
Vol 28 (0) ◽  
Author(s):  
Mateus Martin ◽  
Antonio Moretti ◽  
Marcia Gomes-Ruggiero ◽  
Luiz Salles Neto
Keyword(s):  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Heuristic Procedure ◽  
Setup Cost ◽  
One Dimensional ◽  
The One

scholarly journals Minimizing waste (off-cuts) using cutting stock model: The case of one dimensional cutting stock problem in wood working industry

2016 ◽  
Vol 9 (3) ◽  
pp. 834 ◽  
Author(s):  
Gbemileke A. Ogunranti ◽  
Ayodeji E. Oluleye
Keyword(s):  
Linear Programming ◽  
Real Life ◽  
Cost Savings ◽  
Pattern Generation ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Generation Algorithm ◽  
One Dimensional ◽  
Stock Model ◽  
Total Stock

Purpose: The main objective of this study is to develop a model for solving the one dimensional cutting stock problem in the wood working industry, and develop a program for its implementation.Design/methodology/approach: This study adopts the pattern oriented approach in the formulation of the cutting stock model. A pattern generation algorithm was developed and coded using Visual basic.NET language. The cutting stock model developed is a Linear Programming (LP) Model constrained by numerous feasible patterns. A LP solver was integrated with the pattern generation algorithm program to develop a one - dimensional cutting stock model application named GB Cutting Stock Program.Findings and Originality/value: Applying the model to a real life optimization problem significantly reduces material waste (off-cuts) and minimizes the total stock used. The result yielded about 30.7% cost savings for company-I when the total stock materials used is compared with the former cutting plan. Also, to evaluate the efficiency of the application, Case I problem was solved using two top commercial 1D-cutting stock software.  The results show that the GB program performs better when related results were compared.Research limitations/implications: This study round up the linear programming solution for the number of pattern to cut.Practical implications: From Managerial perspective, implementing optimized cutting plans increases productivity by eliminating calculating errors and drastically reducing operator mistakes. Also, financial benefits that can annually amount to millions in cost savings can be achieved through significant material waste reduction.Originality/value: This paper developed a linear programming one dimensional cutting stock model based on a pattern generation algorithm to minimize waste in the wood working industry. To implement the model, the algorithm was coded using VisualBasic.net and linear programming solver called lpsolvedll (dynamic link library) was integrated to develop a one dimensional cutting stock Program.

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