【摘要】：針對戰(zhàn)時武器裝備維修保障問題,建立了包括最大完工時間、延遲時間和維修單元負荷在內(nèi)的多目標優(yōu)化調(diào)度模型。為提高解的多樣性和收斂性,構(gòu)建了一種基于Pareto排序法和小生境技術(shù)相結(jié)合的遺傳算法用于模型求解,引入Pareto排序和擁擠距離進行適應(yīng)度計算,通過混沌系統(tǒng)隨機生成權(quán)重系數(shù),并使用小生境技術(shù)改進選擇方式。通過實例驗證表明,該方法能夠有效地解決裝備維修多目標調(diào)度問題。
[Abstract]:In order to solve the problem of weapon equipment maintenance support in war, a multi-objective optimal scheduling model, including maximum completion time, delay time and maintenance unit load, is established. In order to improve the diversity and convergence of the solution, a genetic algorithm based on Pareto sequencing method and niche technique is constructed to solve the model, and Pareto ranking and congestion distance are introduced to calculate the fitness. The weight coefficient is generated randomly by chaotic system, and the niche technique is used to improve the selection method. The example shows that this method can effectively solve the multi-objective scheduling problem of equipment maintenance.
【作者單位】： 中北大學(xué)機械與動力工程學(xué)院;
【基金】：山西省自然科學(xué)基金資助項目(2015011060)
【分類號】：E92;TP18
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本文編號：2480592