Use of Genetic Algorithm in Layout Design (original) (raw)
Related papers
Production Layout Planning Using Genetic Algorithms
Communications - Scientific letters of the University of Zilina, 2015
Globalisation of entrepreneurship has not only opened world markets but also global competition. The fundamental criteria for decisions on allocation of manufacturing capacities are currently not only costs (manufacturing, logistics, etc.), but mainly quality (of products, processes and services), flexibility (types of products, capacity, space, time) and a company´s innovative capability. Therefore the enterprise's facility layout needs to be more flexible to adapt to the rapidly changing environment. The need for flexibility of layout planning puts higher requirements for utilisation of layout and location problem solving methods. Classical methods, like linear programming, dynamic programming or conventional heuristics are being replaced by advanced evolutionary algorithms, which give better solutions to large-scale problems. One of these methods are also genetic algorithms. This article describes the genetic algorithm utilisation in the production layout planning.
Utilisation of Evolution Algorithm in Production Layout Design
Applied Computer Science, 2017
The need for flexibility of layout planning puts higher requirements for utilisation of layout and location problem solving methods. Classical methods, like linear programming, dynamic programming or conventional heuristics are being replaced by advanced evolutionary algorithms, which give better solutions to large-scale problems. One of these methods are also genetic algorithms. This article describes the genetic algorithm utilisation in the production layout planningunder the terms of the digital factory concept. 1. REQUIREMENTS FOR THE PRODUCTION LAYOUT PLANNING Current pressure on rapid innovations in the factory places increasing requirements also on the manufacturing and logistics systems design from the point of view of reduced laboriousness, consumption of time and costs for the whole system of technological design and, at the same time, on growth of quality, complexity and ability to testify the outputs generated from this process (Mičieta, Biňasová & Haluška, 2014). Based ...
A genetic algorithm for facility layout problems of different manufacturing environments
2004
This paper describes a genetic algorithm (GA) to solve the problem of optimal facilities layout in manufacturing systems design so that material-handling costs are minimized. The paper considers the various material flow patterns of manufacturing environments of flow shop layout, flow-line layout (single line) with multi-products, multi-line layout, semi-circular and loop layout. The effectiveness of the GA approach is evaluated with numerical examples. The cost performance is compared with other approaches. The results show the effectiveness of the GA approach as a tool to solve problems in facilities layout.
International Journal of Multivariate Data Analysis, 2016
Arrangement of the facilities on shop floor in industries termed as facility layout planning. It is a vital issue at the premature stage while designing a manufacturing structure because it affects the total manufacturing cost considerably. The dynamic environment is such an industrial condition in which flexibility exists in the demand of the product. The purpose of this research paper is to present a review on the implementation of meta-heuristics approaches for handling the problem of facility layout in a dynamic environment. Various meta-heuristic approaches which have been implemented in facility layout planning (FLP) are discussed briefly and the percent utilisation of different approaches is analysed in various time spans. Tabu search (TS), genetic algorithm (GA), particle swarm optimisation (PSO), and ant colony optimisation (ACO) are several typically used methods by researchers for layout optimisation. In the present study, % utilisation of these algorithms has been analysed for different time span, ACO utilised by maximum researchers in the time span '2010-2015'. The present study also revealed GA has been executed by most of the researchers (25%), whereas PSO (8%) utilised by very least designers.
Using genetic algorithms to resolve facility layout problem
Serbian Journal of Management, 2007
The component layout problem requires efficient search of large, discontinuous spaces. The efficient layout planning of a production site is a fundamental task to any project undertaking. This paper describes a genetic algorithm (GA) to solve the problem of optimal facilities layout in manufacturing system design so that material-handling costs are minimized. The performance of the proposed heuristic is tested over problems selected from the literature. Computational results indicate that the proposed approach gives better results compared to many existing algorithms in this area.
Parameter Setting for a Genetic Algorithm Layout Planner as a Toll of Sustainable Manufacturing
Sustainability
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, co...
A Facility Layout Planner tool based on Genetic Algorithms
2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016
Nowadays, in order to maintain their competitiveness, manufacturing companies must adapt their production methods quickly, with minimum expenditure, to frequent variations on demand. With the shortage of the product life time, flexibility, efficiency and reusability of industrial processes are important factors, which may determine the survival of the company. The ReBORN project is working around these ideas, namely studying how can an old production equipment be reused into new contexts. The ReBORN Workbench is a simulation tool for factory layout design, which generates solutions based on the production requirements and facilities' availability, corresponding Life Cycle Assessment, and associated location cost. This paper is concerned with the ReBORN Workbench module responsible to generate solutions for equipment location, generally know as Facility Layout Problems. A Genetic Algorithm was implemented to solve these problems, which aims to minimize the total material handling costs. The effectiveness of the proposed approach is evaluated with a numerical example and compared with other similar approaches. The results show that the proposed approach is indeed effective to solve problems regarding facilities layout.
Genetic Algorithms Optimization for the Machine Layout Problem
This paper gives descriptions on various methods of solving the layout problem and describes a novel method based on genetic algorithms (GA) to solve the machine layout problem. Developing a machine layout is an important step in designing manufacturing facilities due to the impact of the layout to material handling cost and time, and consequently, affects the overall productivity of the shop floor. Poor layout would result in having more parts spending longer time moving from one machine to another, and thus results in increasing material handling costs. In contrast to the block layout, the objective of the machine layout problem is to find the appropriate placement of machines in a cell. The GA-based method developed to solve this uses the objective of minimizing the movements of parts being processed in the cell.
Applying Genetic Algorithm to Dynamic Layout Problem
International Journal of Applied Operational Research - An Open Access Journal, 2011
In today's economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in the product mix and demand.(1) Layout design has a significant impact on manufacturing efficiency. Initially, it was treated as a static decision but due to improvements in technology, it is possible to rearrange the manufacturing facilities in different scenarios. The Plant layout affects on the total cost in the industry. Nowadays Dynamic layout is becoming an important issue. Dynamic layout is the different layout at different time periods to satisfy the needs of industry; due to change in product, or reduced product life cycle, or change in demand. Layout problem is a quadratic assignment problem, and for larger size problems it becomes impossible to be solved. So, for solving this problem Meta heuristic algorithms are used. In this paper, Dynamic layout problem is solved using Genetic algorithm. This Dynamic Problem is restricted up to two-time periods only.
Modified genetic algorithms for solving facility layout problems
International Journal on Interactive Design and Manufacturing (IJIDeM), 2016
A facility layout design is one of the most commonly faced problems in the manufacturing sectors. The problem is mixed-integer in nature and usually an NP-hard problem. Due to mixed-integer nature of the problem, it is difficult to solve the problem using classical optimization techniques. The classical optimization techniques are better for local search of the optimal solution. However, these algorithms are not efficient when there are multiple optimal solutions and alternate optimal solutions. To overcome these limitations, this paper proposed a new interactive evolutionary algorithm based local search algorithm for solving static facility layout problems with unequal compartments. This is an iterative based two steps algorithm. The evolutionary algorithm creates the new solutions for the local search algorithm to obtain a local optimal solution. The designer can interact between these processes to derive the best possible solution of the problem. The objective function of the problem is nonlinear one in which the sum of the material handling cost has been minimized. Apart from the conventional evolutionary operators, i.e. selection, crossover, mutation and elitism, this paper has also proposed exchange and rotation operators. The rotation operator is used to avoid mixed-integer formulation of the problem for the local search problem. The use of rota-B Rajib Kumar Bhattacharjya