【摘要】：隨著工業(yè)4.0和“中國制造2025”的推進(jìn),綠色制造已成趨勢。考慮低碳排放是制造業(yè)急需解決的問題,節(jié)能環(huán)保、綠色制造應(yīng)寫入制造行業(yè)的發(fā)展規(guī)劃中。在實(shí)際車間生產(chǎn)中,機(jī)器的加工速度、加工工件的材質(zhì)等問題都影響制造企業(yè)的碳排放量。在國內(nèi)外與之相關(guān)的調(diào)度研究中,大多數(shù)研究主要關(guān)注求解問題的優(yōu)化算法或者與調(diào)度有關(guān)的其他約束,然而關(guān)于低碳排放約束的柔性作業(yè)車間調(diào)度成果很少。因此本文主要針對低碳排放約束的柔性作業(yè)車間調(diào)度的建模和問題求解進(jìn)行研究。本文研究首先描述了何為柔性作業(yè)車間調(diào)度,介紹了遺傳算法。通過對遺傳算法的改進(jìn)方式進(jìn)行重組并應(yīng)用于所建模型的求解,驗(yàn)證了重組的遺傳算法對模型求解的有效性。在此基礎(chǔ)上,引入了低碳排放參數(shù),設(shè)置了機(jī)器處于不同狀態(tài)以及機(jī)器加工速度對碳排放量的影響,通過仿真實(shí)例說明了低碳排放約束的柔性作業(yè)車間調(diào)度問題模型的可行性。最后將數(shù)據(jù)驅(qū)動技術(shù)融入到動態(tài)柔性作業(yè)車間調(diào)度問題中,利用數(shù)據(jù)的預(yù)測功能,分析出當(dāng)處于某個時(shí)間段時(shí)生產(chǎn)現(xiàn)場可能出現(xiàn)的突發(fā)狀況,最后給出了幾種調(diào)度情況發(fā)生變化后的更新的調(diào)度方案驗(yàn)證了所建動態(tài)調(diào)度模型的有效性。本文創(chuàng)新點(diǎn)主要體現(xiàn)在低碳排放約束的柔性作業(yè)車間調(diào)度的建模上,在經(jīng)典的調(diào)度問題上,設(shè)計(jì)了低碳排放參數(shù),增加以往文獻(xiàn)沒有考慮的工件的裝夾和卸載時(shí)間,考慮了機(jī)器空轉(zhuǎn)、加工及重啟狀態(tài)的碳排放差異,最后結(jié)合機(jī)器加工速度對生產(chǎn)過程中的機(jī)器總碳排放量的影響等,使問題更實(shí)際。又將數(shù)據(jù)驅(qū)動技術(shù)與動態(tài)調(diào)度相結(jié)合,通過調(diào)整不同突發(fā)情況下的調(diào)度方案驗(yàn)證數(shù)據(jù)驅(qū)動技術(shù)能有效解決動態(tài)柔性作業(yè)車間調(diào)度中的干擾因素對生產(chǎn)的影響。
[Abstract]:With the promotion of industry 4.0 and made in China 2025, green manufacturing has become a trend. Considering low carbon emission is an urgent problem in manufacturing industry, energy saving and environmental protection, green manufacturing should be included in the development plan of manufacturing industry. In actual workshop production, the machining speed of machine and the material of workpiece all affect the carbon emission of manufacturing enterprise. In the domestic and foreign related scheduling research, most of the researches mainly focus on the optimization algorithm for solving the problem or other constraints related to scheduling. However, the flexible job shop scheduling with low carbon emission constraints has little results. So this paper mainly focuses on the modeling and problem solving of flexible job shop scheduling with low carbon emission constraints. In this paper, we first describe what is flexible job shop scheduling, and introduce genetic algorithm. By reorganizing the improved genetic algorithm and applying it to the solution of the established model, the validity of the recombined genetic algorithm for solving the model is verified. On this basis, the low carbon emission parameters are introduced, and the effects of different machine states and machining speed on carbon emissions are set up. The feasibility of the flexible job shop scheduling model with low carbon emission constraints is illustrated by a simulation example. Finally, the data-driven technology is integrated into the dynamic flexible job shop scheduling problem. Using the prediction function of the data, the burst situation of the production site is analyzed when it is in a certain period of time. Finally, several updated scheduling schemes are presented to verify the validity of the proposed dynamic scheduling model. The innovation of this paper is mainly reflected in the modeling of flexible job shop scheduling with low carbon emission constraints. In the classical scheduling problem, the low carbon emission parameters are designed to increase the clamping and unloading time of the workpiece that has not been considered in previous literatures. Considering the difference of carbon emission between idle, processing and restarting states, the problem is more practical by considering the effect of machining speed on the total carbon emission of the machine in the process of production. The data-driven technology is combined with dynamic scheduling to verify that the data-driven technology can effectively solve the impact of interference factors in dynamic flexible job shop scheduling by adjusting the scheduling schemes in different burst situations.
【學(xué)位授予單位】：鄭州航空工業(yè)管理學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH165

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本文編號：2230235