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Layout Based on Genetic Algorithm and Application Design

Distance from the margin and adjacent 6 equal portions (each equally divided into d), were divided into A, E, I, O, U, X; according to actual needs is taken as 30m, △ = 8m (i = 1 , 2, ..., 10). The above example, the application of these evolutionary algorithms, population size of 100 evolutionary generations is 3030, crossover probability 0.6, mutation probability 0.4, the non-dominated solutions obtained and displayed graphically shown in Figure 6, The optimal solution for the 163.874 (2o times repeating the same results were obtained), or between operating units in 11 operating units and the ideal layout of the square difference between the sum of the layout, the average variance of 1.16 units per operation. Figure 6 obtained plant genetic algorithm numerical analysis of the preliminary layout 3.2.2 Genetic Algorithm for the test problem in the application of the effectiveness and sensitivity, using the evolutionary algorithm, population size is fixed at 100, the evolution of generations is fixed at 3030, in the length of the genetic code 16, for different probabilities of crossover and mutation operations to 2o times found that crossover probability of 0.4 to 0.8, mutation probability of 0.3 ~ 0.5, the evolutionary computation to the maximum probability to reach the optimal solution, as shown in Figure 7. To further illustrate the effectiveness of the algorithm,[link widoczny dla zalogowanych], the number of operating units to take 7-19, the population size of 100, 3000 generations of evolution, cross Union 2.467 3 Yemu Jing, Zhou Gengui: Layout Based on Genetic Algorithm Design and Application of 101 forks rate 0.6, mutation rate 0.4, the same 20 tests were conducted,[link widoczny dla zalogowanych], the results are shown in Table 4. From the test results, the number of units when the job is less than equal to 14, the genetic algorithm can achieve the same probability of 100% non-inferior solutions; the same time, the number of jobs is less than 11, check with the enumeration algorithm, the results Genetic algorithms with the corresponding stable solution obtained is consistent. The number of jobs in the 15 to 19, the algorithm can be obtained by the standard deviation of less satisfactory solution. Therefore, the evolutionary algorithm is very suitable for medium scale operations unit layout problems. Slightly sweet Pi solution in Figure 7 the impact of the mutation probability noon on the total plant layout table 4 the result of evolutionary algorithm applied over 3.3 final layout of the program from the initial genetic algorithms plant a total floor plan,[link widoczny dla zalogowanych], other factors also need to be adjusted in accordance with the amended ; the same time, considering the actual conditions of requirements, such as roads, green, security, cost, and finally determine the plant's total plant floor plan, shown in Figure 8.4 Conclusion Layout Design System is an advanced layout design method, system analysis,[link widoczny dla zalogowanych], considering the logistics between the operating units and non-logistics relationship, but because of the later need for iterative design, Figure 8, the general layout plans work J and by subjective factors, so the final design of the best uncertain. In this paper, genetic algorithm to solve the problem, the initial layout of a satisfactory design, make up for the inadequacies of SLP methods to ensure the non-inferiority design, layout design method to make the system more scientific, efficient, rational . References: [1] A number of Song, Shi Yongfeng. Logistics mechanization technology [M]. Beijing: Mechanical Industry Press ,1991.81-96. [2] Joseph Richard Bartholomew. Systematic Layout Planning [M]. Beijing: Mechanical Industry Press,[link widoczny dla zalogowanych], 1988. [3] Cheng Kwok-chuen. Facilities planning and logistics of curriculum instruction [M]. Beijing: Mechanical Industry Press, 1995. [4] Zhang Chengqian. SLP and the adjustment of the layout of the production [J]. Operations Research and Management, 1995,12 (4) :45-50. [5] Cheng Kwok-chuen. SLP method in the chemical plant general layout of [J]. Logistics Technology and Application, 1997,2 (2): 34_36. [6] HollandJH. Adaptationinnaturalandartificialsystems [M]. Cambridge: MITPress, 1975.17] GomezA, FemandezQI, DelaFuenteGarciaD, eta1. Usinggeneticalgorithmstol'eSo [velayoutproblemsinfacilitieswhere Hill ereareaisles [JJ. InternationalJournalofProductionEc 【) nom-ics, 2003,84 (3) :271-282. [8] Hyun Mitsuo, Cheng Runwei. Genetic Algorithms and Engineering Design [M], Beijing: Science Press, 2000.84.89.

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