本文選題：多目標(biāo)調(diào)度 + 柔性作業(yè)車間�。� 參考：《計(jì)算機(jī)應(yīng)用研究》2017年12期

【摘要】：針對(duì)面向綠色制造的車間調(diào)度問(wèn)題,考慮能源消耗、最大完工時(shí)間、生產(chǎn)成本等調(diào)度目標(biāo),建立多目標(biāo)柔性作業(yè)車間調(diào)度問(wèn)題模型。針對(duì)該模型特點(diǎn),提出一種改進(jìn)離散蝙蝠算法對(duì)其求解;為有效地表達(dá)出工序與粒子種群之間的關(guān)系,采用一種整數(shù)編碼策略;通過(guò)設(shè)計(jì)一種具有記憶能力的粒子變異操作避免了算法早熟收斂、求解精度低等問(wèn)題;同時(shí)算法中引入了一種線性遞減的慣性權(quán)重策略,更好地平衡了算法的全局搜索能力和局部搜索能力。最后針對(duì)具體生產(chǎn)實(shí)例進(jìn)行驗(yàn)證,實(shí)驗(yàn)數(shù)據(jù)表明,該改進(jìn)算法在求解多目標(biāo)柔性作業(yè)車間調(diào)度問(wèn)題上具有良好的性能,是一種有效的調(diào)度算法。
[Abstract]:Considering the energy consumption, maximum completion time and production cost, a multi-objective flexible job shop scheduling model is established for the job shop scheduling problem oriented to green manufacturing.According to the characteristics of the model, an improved discrete bat algorithm is proposed to solve the problem, and an integer coding strategy is used to express the relationship between process and particle population.By designing a particle mutation operation with memory ability to avoid premature convergence and low precision of the algorithm, a linearly decreasing inertial weight strategy is introduced in the algorithm.The global search ability and the local search ability of the algorithm are better balanced.Finally, the experimental data show that the improved algorithm has good performance in solving multi-objective flexible job shop scheduling problem, and it is an effective scheduling algorithm.
【作者單位】： 江南大學(xué)物聯(lián)網(wǎng)工程學(xué)院;
【基金】：江蘇省自然科學(xué)基金資助項(xiàng)目(BK20140165) 國(guó)家留學(xué)基金委資助項(xiàng)目(201308320030)
【分類號(hào)】：TB497;TP18
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本文編號(hào)：1746128