USING GENETIC ALGORITHMS TO RESOLVE FACILITY LAYOUT PROBLEMUniversity of Belgrade, Technical Faculty at Bor,Vojske Jugoslavije 12, 19210 Bor, Serbia(Received 12 May 2006; accepted 23 July 2006)Abstract: 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.Keywords: facility layout; flexible manufacturing; stochastic programming1. INTRODUCTION Component layout plays an important role in the design and usability of many engineering products. The layout problem is also classified under the headings of packing, packaging, configuration, container stuffing, pallet loading or spatial arrangement in the literature. The problem involves the placement of components in an available space such that a set of objectivescan be optimized while satisfying optional spatial of performance constraints.Current tools available in practice to designers to aid in the general mechanical layout process mostly remain at the stages of physical or electronic models with the assistance of manual adjustment and visual feedback.The difficulty in automating the mechanical and electromechanical layout processes stems from: (1) the modeling of the design objectives and constraints; (2) the constraints; (3) the identification of appropriate optimization search strategies.A number of design goals can be modeled as layout objectives. In addition, a set of constrains often has to be satisfied to ensure the applicability of the layouts. Efficient calculations of objectives and constraints are necessary to solve the layout problems in reasonable time since the analysis of objectives and constraints can be computationally expensive and a large number of evaluations may be required to achieve convergence. The search space of the layout problem is non-linear and multimodel, making it vital to identify a suitable algorithm to navigate the space and find good quality solutions. The layout goals are usually formulated as objective functions. The objectives may reflect the cost, quality, performance and service requirements. Various constraints may be necessary to specify spatial relationships between components. The specifications of components, objectives,constraints, and topological connections define a layout problem and an optimization search algorithm takes the problem formulation and identifies promising solution by evaluating design alternatives and evolving design states. Analysis of objectives and constraints vary from problemto problem. However, the optimization search technique and geometric representation and the resulting interference evaluation are problem independent and are,thus, the focus for a generic layout tool1. The primary objective of the design problem is to minimize the costs associatedwith production and materials movement layout, semiconductor manufacturing andservice center layout. For US manufacturers,between 20% and 50% of total operating expenses are spent on material handling and an appropriate facilities design can reduce these costs by at least 10%-30% 2,3. Altering facility designs due to incorrect decisions, forecasts or assumptions usually involves considerable cost, time and disruption of activities. On the other hand,good design decisions can reap economic and operational benefits for a long time period. Therefore, the critical aspects are designs that translate readily into physical reality and designs that are robust to departures from assumptions. The project manager or planner usually performs the task of preparing the layout based on his/her own knowledge and expertise. Apparently, this could result in layouts that differ significantly from one person to another. To put this task into more perspective, researchers have introduced different approaches to systematically plan the layout of production sites 4,5 Facility layout planning can generally be classified according to two main aspects: (1) method of facility assignment and (2) layout planning technique. Mathematical techniques usually involve the identification of one or more goals that the sought layout should strive to achieve. A widely used goal is the minimization of transportation costs on site. These goals are commonly interpreted to what mathematicians term objective functions. This objective function is then optimized under problem-specific constraints toproduce the desired layout. Systems utilizing knowledge-based techniques, in contrast, provide r