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 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 Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem

2012 ◽  
pp. 56-71
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfill the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

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.

Manufacturing-oriented silicon steel coil lengthwise cutting stock problem with useable leftover

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fengjie Li ◽  
Yan Chen ◽  
Xiaochun Hu
Keyword(s):  
Computation Time ◽  
Silicon Steel ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Content Type ◽  
Total Cost ◽  
Steel Coil ◽  
Steel Coils ◽  
Cost Of Materials ◽  
Material Utilization

PurposeThis paper propose an algorithm for the multiple silicon steel coils multiperiod two-dimensional lengthwise cutting stock problem (m2DLCSP), so as to minimize the total cost of materials and production.Design/methodology/approachThe authors propose a sequential leftovers utilization correction (SLUC) algorithm for the m2DLCSP. The algorithm primarily considers three optimization strategies. First, it considers usable leftovers to simplify the cutting process and improve material utilization. The total quantity and types of leftovers should be limited in order to avoid leftover overstock. Second, it uses a splice method of items to improve the generated cutting plan. Third, it takes into account operational restrictions in the cutting operations. Operational restrictions include imposing maximum and minimum lengths on the cutting patterns, and the limitation of cutting knives at the slitting machines.FindingsSeveral sets of benchmark with real-world and randomly generated instances are provided to evaluate the algorithm. Compared with literature algorithm and current procedure applied in enterprises, the computational results indicate that proposed algorithm can effectively reduce the total cost, and the computation time is reasonable for practical use.Social implicationsThis algorithm can effectively reduce the total cost.Originality/valueThe proposed method can effectively applied to solve the m2DLCSP and improve the economic efficiency of enterprises.

Sign in / Sign up

Export Citation Format

Share Document