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.
The authors deal with genetic algorithms as an effective tool for design and planning of production system. Genetic algorithms are part of evolutionary algorithms, which are rapidly spreading into all main fields of product and production system design and optimization. The main advantage of genetic algorithms is its ability to search large and complex set of solutions and to find acceptable solution in acceptable time. In the paper authors describe their basic principlesalgorithm itself, its steps and possible rules used within it. Several examples of utilization are presented, and subsequently we focus on applications developed at the Department of Industrial engineering, University of Zilina. Particularly we focus on line balancing, scheduling and production system design.
Within the design of production layout, the planners are often confronted with complex, sometimes conflicting demands and a number of restrictive conditions, which encourages their efforts to develop new, progressive approaches to the development of production layouts. The purpose of the innovative approaches in this field is to provide users with better, elaborated designs in less time, while they are able to implement various restrictive conditions and company priorities to the design. One of the ways is a use of metaheuristic algorithms by space solution optimisations of manufacturing and logistics systems. These methods have higher quality results compared to classical heuristic methods. Genetic algorithms belong to this group. Main goal of this article is to describe the Genetic Algorithm Layout Planner (GALP) that was developed by authors, and its experimental verification and comparison with results of the classical heuristic. minimum crossing. However, in case of PLAN/OPT algorithm, there is a crossing, where material flow keeps coming back and there is not any technology island creation in manufacturing system. Genetic algorithm proposed layout with greatly lower value of transportation performance (38.18%) than heuristic algorithm in a PLAN/OPT module. Experiment result comparison is stated in table 2.
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