Workshop Layout Optimization Based On Ant Colony Optimization Algorithm

The reduction in manufacturing cost is seriously influenced by the layout of workshops in manufacturing system, the arrangement of facilities in workshop and the condition of material handling. The rationality of workshop layout has directly effects on the efficiency of the logistics and production, the manufacturing cost and the production security, as well as the production cycle and occupies amounts of products.20%-50% of the operation costs in a workshop lie in material handling, and efficient layout of facilities could reduce the proportion to 10%-30%, which therefore will significantly reduce the whole manufacturing cost for companies. So, it is necessary to design the workshop layout and to study in a quick and efficient way of doing this.The problem of the workshop layout is belongs to the NP-hard problem. When the problem scale is large, the method based on exhaustive search will not be able to solve practical problems within a limited time, which can only be turned to apply the heuristic algorithm to find a better pattern of the workshop layout, such as the Genetic Algorithm,the Simulated Annealing, the Ant Colony Optimization. The Ant Colony Optimization has wide applications, for many complicated combinatorial optimizing problem solving is superior to other algorithms.This paper summarizes the research background and current situation of designing workshop layout in the first place, and introduces the basic theory of the workshop layout. Then, based on the workshop layout features of manufacturing companies, the paper builds a model according to the quadratic assignment problem and determines the reasonable constraints, with the optimization goal of minimizing material transport costs for the workshop layout. Then it applies the Max-Min Ant System(MMAS) to solve the problem model combined with local search algorithm to obtain better solutions. There are two main parts of solving the layout scheme:applying the MMAS to obtain the initial solution and combining with local search algorithm updates the initial solution to improve the solution quality. As MMAS only applies the iteration-best ants and up to now the best ant updates pheromone, thus it can make a better use of historical information, increase the possibility of ant build quality solutions, and limit the range of pheromone concentrations to avoid premature algorithm convergence in non-global optimal solution.The purpose of this study is to apply the ant colony optimization algorithm to solve problems of workshop layout, and to use the C++ language programming to achieve the algorithm. The result shows that the algorithm provides a useful heuristic decision support tool and is feasible and effective for solving workshop layout problem.