scholarly journals The Use of a Genetic Algorithm for Sorting Warehouse Optimisation

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1197
Author(s):  
Patrik Grznár ◽  
Martin Krajčovič ◽  
Arkadiusz Gola ◽  
Ľuboslav Dulina ◽  
Beáta Furmannová ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Business Processes ◽  
Optimal Number ◽  
Simulation Software ◽  
Heuristic Methods ◽  
Major Purpose ◽  
Processing Times ◽  
Speed Up ◽  
Plant Simulation

In the last decade, simulation software as a tool for managing and controlling business processes has received a lot of attention. Many of the new software features allow businesses to achieve better quality results using optimisation, such as genetic algorithms. This article describes the use of modelling and simulation in shipment and sorting processes that are optimised by a genetic algorithm’s involvement. The designed algorithm and simulation model focuses on optimising the duration of shipment processing times and numbers of workers. The commercially available software Tecnomatix Plant Simulation, paired with a genetic algorithm, was used for optimisation, decreasing time durations, and thus selecting the most suitable solution for defined inputs. This method has produced better results in comparison to the classical heuristic methods and, furthermore, is not as time consuming. This article, at its core, describes the algorithm used to determine the optimal number of workers in sorting warehouses with the results of its application. The final part of this article contains an evaluation of this proposal compared to the original methods, and highlights what benefits result from such changes. The major purpose of this research is to determine the number of workers needed to speed up the departure of shipments and optimise the workload of workers.

scholarly journals Solution of Bottlenecks in the Logistics Flow by Applying the Kanban Module in the Tecnomatix Plant Simulation Software

2021 ◽  
Vol 13 (14) ◽  
pp. 7989
Author(s):  
Miriam Pekarcikova ◽  
Peter Trebuna ◽  
Marek Kliment ◽  
Michal Dic
Keyword(s):  
Lean Manufacturing ◽  
Business Processes ◽  
Software Tool ◽  
Transportation Systems ◽  
Simulation Software ◽  
Simulation Methods ◽  
Software Environment ◽  
Production Logistics ◽  
Pull System ◽  
Plant Simulation

The presented article deals with the issue of solving bottlenecks in the logistics flow of a manufacturing company. The Tx Plant Simulation software tool is used to detect bottlenecks and deficiencies in the company’s production, logistics and transportation systems. Together with the use of simulation methods and lean manufacturing tools, losses in business processes are eliminated and consequently flow throughput is improved. In the TX Plant Simulation software environment, using Bottleneck analyzer, bottlenecks were defined on the created simulation model and a method of optimizing logistics flows was designed and tested by introducing the Kanban pull system. This resulted in an improvement and throughput of the entire logistics flow, a reduction in inter-operational stocks and an increase in the efficiency of the production system as a whole.

scholarly journals Application of the genetic algorithm to the task of compiling the curriculum

2018 ◽  
Author(s):  
V. A. Turchina ◽  
D. O. Tanasienko
Keyword(s):  
Higher Education ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Necessary Conditions ◽  
Fitness Function ◽  
Educational Process ◽  
Heuristic Methods ◽  
Educational Groups ◽  
The Individual ◽  
Negative Property

One of the main tasks in organizing the educational process in higher education is the drawing up of a schedule of classes. It reflects the weekly student and faculty load. At the same time, when compiling, there are a number of necessary conditions and a number of desirable. The paper considers seven required and four desirable conditions. In this paper, one of the well-known approaches that can be used in drawing up a curriculum is consid-ered. The proposed scheme of the genetic algorithm, the result of which is to obtain an approximate solution to the problem of scheduling with the need to further improve it by other heuristic methods. To solve the problem, an island model of the genetic algorithm was selected and its advantages were considered. In the paper, the author's own structure of the individual, which includes chromosomes in the form of educational groups and genes as a lesson at a certain time, is presented and justified. The author presents his own implementations of the genetic algorithms. During the work, many variants of operators were tested, but they were rejected due to their inefficiency. The biggest problem was to maintain the consistency of information encoded in chromosomes. Also, two post-steps were added: to try to reduce the number of teacher conflict conflicts and to normalize the schedule - to remove windows from the schedule. The fitness function is calculated according to the following principles: if some desired or desired property is present in the individual, then a certain number is deducted from the individual's assessment, if there is a negative property, then a certain number is added to the assessment. Each criterion has its weight, so the size of the fine or rewards may be different. In this work, fines were charged for non-fulfillment of mandatory conditions, and rewards for fulfilling the desired

scholarly journals Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm

2016 ◽  
Author(s):  
Tarik Eltaeib ◽  
Julius Dichter
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Genetic Algorithm ◽  
Algorithm Optimization ◽  
Parallel Genetic Algorithms ◽  
Np Completeness ◽  
Linear Polynomial ◽  
Optimization Function ◽  
Speed Up ◽  
Complementary Equation

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

scholarly journals A Master Slave Parallel Genetic Algorithm for Feature Selection in High Dimensional Datasets

2019 ◽  
Vol 8 (3) ◽  
pp. 379-384
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Information Gain ◽  
Optimal Number ◽  
Good Choice ◽  
High Dimensional ◽  
Parallel Genetic Algorithm ◽  
Efficient Manner ◽  
High Dimensional Datasets

Feature Selection in High Dimensional Datasets is a combinatorial problem as it selects the optimal subsets from N dimensional data having 2N possible subsets. Genetic Algorithms are generally a good choice for feature selection in large datasets, though for some high dimensional problems it may take varied amount of time - few seconds, few hours or even few days. Therefore, it is important to use Genetic Algorithms that can give quality results in reasonably acceptable time limit. For this purpose, it is becoming necessary to implement Genetic Algorithms in an efficient manner. In this paper, a Master Slave Parallel Genetic Algorithm is implemented as a Feature Selection procedure to diminish the time intricacies of sequential genetic algorithm. This paper describes the speed gains in parallel Master-Slave Genetic Algorithm and also discusses the theoretical analysis of optimal number of slaves required for an efficient master slave implementation. The experiments are performed on three high-dimensional gene expression data. As Genetic Algorithm is a wrapper technique and takes more time to find the importance of any feature, Information Gain technique is used first as pre-processing task to remove the irrelevant features.

scholarly journals Parameter Setting for a Genetic Algorithm Layout Planner as a Toll of Sustainable Manufacturing

2019 ◽  
Vol 11 (7) ◽  
pp. 2083 ◽  
Author(s):  
Martin Krajčovič ◽  
Viktor Hančinský ◽  
Ľuboslav Dulina ◽  
Patrik Grznár ◽  
Martin Gašo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Manufacturing Systems ◽  
Business Processes ◽  
Sustainable Manufacturing ◽  
Spatial Arrangement ◽  
Production Performance ◽  
Constant Attention ◽  
Logistics Systems ◽  
Evolution Algorithms

The long-term sustainability of the enterprise requires constant attention to the continuous improvement of business processes and systems so that the enterprise is still competitive in a dynamic and turbulent market environment. Improvement of processes must lead to the ability of the enterprise to increase production performance, the quality of provided services on a constantly increasing level of productivity and decreasing level of cost. One of the most important potentials for sustainability competitiveness of an enterprise is the continuous restructuring of production and logistics systems to continuously optimize material flows in the enterprise in terms of the changing requirements of customers and the behavior of enterprise system surroundings. Increasing pressure has been applied to projecting manufacturing and logistics systems due to labor intensity, time consumption, and costs for the whole technological projecting process. Moreover, it is also due to quality growth, complexity, and information ability of outputs generated from this process. One option is the use of evolution algorithms for space solution optimization for manufacturing and logistics systems. This method has higher quality results compared to classical heuristic methods. The advantage is the ability to leave specific local extremes. Classical heuristics are unable to do so. Genetic algorithms belong to this group. This article presents a unique genetic algorithm layout planner (GALP) that uses a genetic algorithm to optimize the spatial arrangement. In the first part of this article, there is a description of a framework of the current state of layout planning and genetic algorithms used in manufacturing and logistics system design, methods for layout design, and basic characteristics of genetic algorithms. The second part of the article introduces its own GALP algorithm. It is a structure which is integrated into the design process of manufacturing systems. The core of the article are parameters setting and experimental verification of the proposed algorithm. The final part of the article is a discussion about the results of the GALP application.

scholarly journals Representasi Matriks untuk Proses Crossover Pada Algoritma Genetika untuk Optimasi Travelling Salesman Problem

Matematika ◽  
2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Ismi Fadhillah ◽  
Yurika Permanasari ◽  
Erwin Harahap
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Travelling Salesman Problem ◽  
Optimal Number ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
The City

Abstrak. Travelling Salesman Problem (TSP) merupakan salah satu permasalahan optimasi kombinatorial yang biasa terjadi dalam kehidupan sehari-hari. Permasalahan TSP yaitu mengenai seseorang yang harus mengunjungi semua kota tepat satu kali dan kembali ke kota awal dengan jarak tempuh minimal. TSP dapat diselesaikan dengan menggunakan metode Algoritma Genetika. Dalam Algoritma Genetika, representasi matriks merupakan representasi kromosom yang menunjukan sebuah perjalanan. Jika dalam perjalanan tersebut melewati n kota maka akan dibentuk matriks n x n. Matriks elemen Mij dengan baris i dan kolom j dimana entry M(i,j) akan bernilai 1 jika dan hanya jika kota i dikunjungi sebelum kota j dalam satu perjalanan tersebut, selain itu M(i,j)=0. Crossover adalah mekanisme yang dimiliki algoritma genetika dengan menggabungkan dua kromosom sehingga menghasilkan anak kromosom yang mewarisi ciri-ciri dasar dari parent. Algoritma Genetika selain melibatkan populasi awal dalam proses optimasi juga membangkitkan populasi baru melalui proses crossover, sehingga dapat memberikan daftar variabel yang optimal bukan hanya solusi tunggal. Dari hasil proses crossover dalam contoh kasus TSP melewati 6 kota, terdapat 2 kromosom anak terbaik dengan nilai finess yang sama yaitu 0.014. Algoritma Genetika dapat berhenti pada generasi II karena berturut-turut mendapat nilai fitness tertinggi yang tidak berubahKata kunci : Travelling Salesman Program (TSP), Algoritma Genetika, Representasi Matriks, Proses Crossover Abstract. Travelling Salesman Problem (TSP) is one of combinatorial optimization problems in everyday life. TSP is about someone who had to visit all the cities exactly once and return to the initial city with minimal distances. TSP can be solved using Genetic Algorithms. In a Genetic Algorithm, a matrix representation represents chromosomes which indicates a journey. If in the course of the past n number of city will set up a matrix n x n. The matrix element Mij with row i and column j where entry M (i, j) will be equal to 1 if and only if the city i before the city j visited in one trip. In addition to the M (i, j) = 0. Crossover is a mechanism that is owned by the Genetic Algorithm to combine the two chromosomes to produce offspring inherited basic characteristics of the parent. Genetic Algorithms in addition to involve the population early in the optimization process will also generate new populations through the crossover process, so as to provide optimal number of variables is not just a single solution. From the results of the crossover process in the case of TSP passing through six cities, there are two the best offspring with the same finess value which is 0.014. Genetic Algorithms can be stopped on the second generation due to successive received the highest fitness value unchanged.Keywords: Travelling Salesman Program (TSP), Genetic Algorithm, Matrix Representation, Crossover Process

APPLICATION OF COMPUTER SIMULATION IN IMPROVING THE PROCESS OF SCREWS PRODUCTION

2017 ◽  
Vol 66 (0) ◽  
pp. 89-96
Author(s):  
Julia Siderska ◽  
Katarzyna Perkowska
Keyword(s):  
Computer Simulation ◽  
Spatial Arrangement ◽  
Simulation Software ◽  
Production Flow ◽  
Processing Times ◽  
Machine Simulation ◽  
Process Line ◽  
Set Up ◽  
Plant Simulation

The aim of this work is to present and discuss the possibility of using computer simulation to improve the production flow of sheet metal screws in the carpentry plant. The paper includes descriptive and schematic characterization of the present production system of the plant in one of the Polish companies. Using the Tecnomatix Plant Simulation software, a digital model of the process line was drawn up, mirroring the spatial arrangement of workstations in the production hall and their operators, as well as actual processing times and set-up times of each machine. Simulation was performed to identify bottlenecks in the entire production process, and several modifications were proposed on that basis so as to optimize the workloads of the workstations and increase production.

The Industry Assembly Line Layout’s Modeling and Simulation in Industry Manufacturing Based on Genetic Algorithm

2013 ◽  
Vol 675 ◽  
pp. 3-7
Author(s):  
Fang Guo ◽  
Zhi Hong Huang
Keyword(s):  
Genetic Algorithm ◽  
Modeling And Simulation ◽  
Equilibrium Problem ◽  
Assembly Line ◽  
Simulation Software ◽  
Heuristic Procedure ◽  
Plant Simulation ◽  
Line Design

The equilibrium problem is one important aspect of industry assembly line design. This paper puts forward the method to solve the industry assembly line’s equilibrium problem based on the genetic algorithm’s heuristic procedure and on this basis it also optimizes the industry assembly line’s layout and synthetically considers the material carrying cost, plant area’s use ratio and other factors in industry manufacturing. Then it optimizes by eM-Plant simulation software and combining with genetic algorithm to efficiently acquire visual and satisfying layout effects. At last, it uses examples of industry assembly line to verify this method’s feasibility.

scholarly journals Simulation and Optimization of the VTC200C Machining Center Production Plan

TEM Journal ◽  
2020 ◽  
pp. 1295-1306
Author(s):  
Marian Králik ◽  
Vladimír Jerz
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Input Data ◽  
Simulation Software ◽  
Efficient Production ◽  
Production Plan ◽  
Output Data ◽  
Simulation And Optimization ◽  
Machining Center ◽  
Plant Simulation

The paper describes the use of the Plant Simulation software to create a simulation model of the manufacturing process in the VTC200C machining centre. A genetic algorithm was used to optimize the production process. It is an algorithm that learns itself and looks for the best solution based on input data. The optimization is done based on the assessment of the output data from the simulation model. Based on the results of the optimization, a more efficient production plan was designed in the selected company.

scholarly journals A coarse-grained parallelization of genetic algorithms

2018 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Muhamad Radzi Rathomi ◽  
Reza Pulungan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Message Passing ◽  
Optimization Problems ◽  
Convergence Speed ◽  
Experimental Results ◽  
Coarse Grained ◽  
Speed Up ◽  
Multiple Threads ◽  
Algorithm Parallelization

Genetic algorithms are frequently used to solve optimization problems. However, the problems become increasingly complex and time consuming. One solution to speed up the genetic algorithm processing is to use parallelization. The proposed parallelization method is coarse-grained and employs two levels of parallelization: message passing with MPI and Single Instruction Multiple Threads with GPU. Experimental results show that the accuracy of the proposed approach is similar to the sequential genetic algorithm. Parallelization with coarse-grained method, however, can improve the processing and convergence speed of genetic algorithms.

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