【摘要】：為解決高維多目標(biāo)柔性作業(yè)車間調(diào)度問(wèn)題,提出了一種基于模糊物元模型與粒子群算法的模糊粒子群算法(fuzzy particle swarm optimization,FPSO)。該算法以模糊物元分析理論為依據(jù),采用復(fù)合模糊物元與基準(zhǔn)模糊物元之間的歐氏貼近度作為適應(yīng)度值引導(dǎo)粒子群算法的進(jìn)化,并引入具有容量限制的外部存儲(chǔ)器保留較優(yōu)的Pareto非支配解以供決策者選擇。此外,構(gòu)建了優(yōu)化目標(biāo)為最大完工時(shí)間、設(shè)備總負(fù)荷、加工成本、最大設(shè)備負(fù)荷與加工質(zhì)量的高維多目標(biāo)優(yōu)化模型,并以Kacem基準(zhǔn)問(wèn)題與實(shí)際生產(chǎn)數(shù)據(jù)為例進(jìn)行仿真模擬與對(duì)比分析。結(jié)果表明,該算法具有良好的收斂性,且搜索到的非支配解分布性較好,能夠有效地應(yīng)用于求解高維多目標(biāo)柔性作業(yè)車間調(diào)度問(wèn)題。
[Abstract]:A fuzzy particle swarm optimization (fuzzy particle swarm optimization,FPSO) algorithm based on fuzzy matter-element model and particle swarm optimization (PSO) is proposed to solve the high dimensional flexible job shop scheduling problem. Based on the theory of fuzzy matter-element analysis, the Euclidean closeness between compound fuzzy matter-element and reference fuzzy matter-element is used as the fitness value to guide the evolution of PSO. Furthermore, the external memory with limited capacity is introduced to retain the optimal Pareto non-dominated solution for decision makers to choose. In addition, a high-dimensional multi-objective optimization model with maximum completion time, total equipment load, processing cost, maximum equipment load and machining quality is constructed. The Kacem benchmark problem and actual production data are taken as an example for simulation and comparative analysis. The results show that the proposed algorithm has good convergence and good distribution of non-dominated solutions. It can be effectively applied to solve high-dimensional multi-objective flexible job shop scheduling problems.
【作者單位】： 西北工業(yè)大學(xué)管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(U1404702)
【分類號(hào)】：TB497;TP18

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