CNC Machine Shop Floor Facility Layout Design Using Genetic Algorithm

2020 ◽  
pp. 223-234
Author(s):  
S. M. Vadivel ◽  
A. H. Sequeira ◽  
Sunil Kumar Jauhar ◽  
K. S. Amirthagadeswarn ◽  
T. Aravind Krishna
Keyword(s):  
Genetic Algorithm ◽  
Facility Layout ◽  
Layout Design ◽  
Shop Floor ◽  
Cnc Machine ◽  
Machine Shop ◽  
Facility Layout Design

scholarly journals Facility layout design using a multi-objective interactive genetic algorithm to support the DM

2013 ◽  
Vol 32 (1) ◽  
pp. 94-107 ◽  
Author(s):  
Laura García-Hernández ◽  
Antonio Arauzo-Azofra ◽  
Lorenzo Salas-Morera ◽  
Henri Pierreval ◽  
Emilio Corchado
Keyword(s):  
Genetic Algorithm ◽  
Facility Layout ◽  
Layout Design ◽  
Interactive Genetic Algorithm ◽  
Facility Layout Design ◽  
Multi Objective

Holistic simulation-based optimisation methodology for facility layout design with consideration to production and logistics constraints

2021 ◽  
pp. 095440542110173
Author(s):  
Enrique Ruiz Zúñiga ◽  
Erik Flores García ◽  
Matías Urenda Moris ◽  
Masood Fathi ◽  
Anna Syberfeldt
Keyword(s):  
Material Handling ◽  
Facility Layout ◽  
Holistic Approach ◽  
Multiple Attribute Decision Making ◽  
Layout Design ◽  
Analytic Hierarchy ◽  
Facility Layout Design ◽  
Multiple Attribute ◽  
Simulation Based ◽  
Optimisation Methods

Facility layout design is becoming more challenging as manufacturing moves from traditionally emphasised mass production to mass customisation. The increasing demand for customised products and services is driving the need to increase flexibility and adaptability of both production processes and their material handling systems. A holistic approach for designing facility layouts with optimised flows considering production and logistics systems constraints seems to be missing in the literature. Several tools, including traditional methods, analytic hierarchy process, multiple-attribute decision making, simulation, and optimisation methods, can support such a process. Among these, simulation-based optimisation is the most promising. This paper aims to develop a facility layout design methodology supported by simulation-based optimisation while considering both production and logistics constraints. A literature review of facility layout design with simulation and optimisation and the theoretical and empirical challenges are presented. The integration of simulation-based optimisation in the proposed methodology serves to overcome the identified challenges, providing managers and stakeholders with a decision support system that handles the complex task of facility layout design.

scholarly journals Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Amir-Mohammad Golmohammadi ◽  
Hasan Rasay ◽  
Zaynab Akhoundpour Amiri ◽  
Maryam Solgi ◽  
Negar Balajeh
Keyword(s):  
Machine Learning ◽  
Programming Model ◽  
Facility Layout ◽  
Cell Formation ◽  
Layout Design ◽  
Metaheuristic Algorithms ◽  
Sensitivity Analyses ◽  
Mixed Integer ◽  
Facility Layout Design ◽  
Proposed Model

Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.

scholarly journals Rancangan Tata Letak Fasilitas Produksi Produk Rehabilitasi Dengan Algoritma Genetika

2019 ◽  
Vol 16 (3) ◽  
pp. 73
Author(s):  
Steaven Leonardo Chandra ◽  
Theresia Sunarni ◽  
Kristoforus Jawa Bendi ◽  
Dominikus Budiarto
Keyword(s):  
Genetic Algorithm ◽  
Production System ◽  
Layout Design ◽  
Production Volume ◽  
Production Facility ◽  
Total Flow ◽  
Facility Layout Design ◽  
Machine Layout ◽  
Section Production ◽  
Optimal Machine

The layout of production facilities has a very important impact and interrelationship between facilities with each other to support the smoothness of the production process.Frequently the biggest problems in the production system are caused by unsystematic handling of materials. This study, applies a genetic algorithm to obtain a facility layout design (machine) optimally. Inputs from this study include machine numbers, machine dimensions, machine sequences in each section, production volume in each section, and flow frequency in each section. The output of this study is the machine layout with a minimum total flow cost. This study solved the case in the rehab product production facility at PT Shima Prima Utama which consisting of 16 machines and 29 components. The results of the study in this case are that the optimal machine placement sequence is 13, 3, 9, 15, 6, 10, 2, 12, 8, 16, 7, 1, 11, 5, 14 and 4 with a total flow cost of 197,434.

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