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  本文選題：拆卸線平衡問(wèn)題　切入點(diǎn)：多目標(biāo)　出處：《西南交通大學(xué)》2012年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著生產(chǎn)者責(zé)任延伸制的推行、各國(guó)新的更多的環(huán)境立法的建立以及公眾環(huán)境意識(shí)的提高,制造商開(kāi)始回收和再制造廢舊的產(chǎn)品。此外,重新使用廢舊產(chǎn)品所帶來(lái)的經(jīng)濟(jì)吸引力也從另一個(gè)層面推動(dòng)了更多制造商的投入。拆卸是重新使用、制造、回收、存儲(chǔ)以及合理處理產(chǎn)品關(guān)鍵的第一步,拆卸線是實(shí)現(xiàn)大規(guī)模拆卸的最佳選擇,因而有效設(shè)計(jì)和平衡拆卸線對(duì)提高拆卸效率至關(guān)重要,因此研究拆卸線平衡問(wèn)題具有重要的理論和實(shí)際意義。 結(jié)合拆卸線的特點(diǎn),提出了多目標(biāo)拆卸線平衡問(wèn)題的數(shù)學(xué)模型,其優(yōu)化目標(biāo)為最小化工作站數(shù)、均衡各工作站空閑時(shí)間,并考慮拆卸產(chǎn)品部件的危害、需求以及方向。在此基礎(chǔ)上,采用兩種群集智能優(yōu)化算法—粒子群算法和蟻群算法研究了多目標(biāo)拆卸線平衡問(wèn)題。 提出了一種目標(biāo)基于優(yōu)先排序的粒子群算法,該算法采用隨機(jī)數(shù)生成粒子的位置和速度,而位置和速度的更新則為對(duì)應(yīng)隨機(jī)數(shù)相加減,進(jìn)而把位置的隨機(jī)數(shù)作為選擇零件的權(quán)重,從而根據(jù)權(quán)重的大小來(lái)選擇拆卸的零件。并通過(guò)具體的實(shí)例以及基準(zhǔn)例子驗(yàn)證了算法的有效性。 提出了一種基于Pareto的粒子群算法來(lái)求解多目標(biāo)拆卸線平衡問(wèn)題,該算法采用小生境技術(shù)選取多目標(biāo)的全局最優(yōu)解,采用Pareto占優(yōu)以及分散度作為個(gè)體評(píng)價(jià)以及局部最優(yōu)解選取,最后通過(guò)具體的實(shí)例以及基準(zhǔn)例子驗(yàn)證了算法的有效性。 提出了一種目標(biāo)基于優(yōu)先排序的蟻群算法來(lái)求解多目標(biāo)拆卸線平衡問(wèn)題,該算法考慮了以零件拆卸時(shí)間、危害以及需求三種規(guī)則的啟發(fā)式信息,并綜合考慮利用先驗(yàn)知識(shí)、探索新路徑、隨機(jī)選擇三種方式的混合搜索機(jī)制,有效的提高了算法的搜索效率,并通過(guò)具體的實(shí)例以及基準(zhǔn)例子驗(yàn)證了算法的有效性。
[Abstract]:With the extension of producer responsibility, the establishment of new and more environmental legislation in various countries and the increasing public awareness of the environment, manufacturers began to recycle and re-manufacture used products. The economic appeal of reusing used products also drives more manufacturers on another level. Disassembly is a critical first step in reusing, manufacturing, recycling, storing, and reasonably disposing of products. The disassembly line is the best choice to realize the large-scale disassembly, so it is very important to design and balance the disassembly line effectively to improve the disassembly efficiency. Therefore, it is of great theoretical and practical significance to study the disassembly line balance problem. Combined with the characteristics of disassembly line, the mathematical model of multi-objective disassembly line balance problem is put forward. Its optimization goal is to minimize the number of workstations, to balance the idle time of each workstation, and to consider the harm of disassembly parts. On the basis of this, two cluster intelligent optimization algorithms, particle swarm optimization (PSO) and ant colony algorithm (ACA), are used to study the multi-objective disassembly line balance problem. A priority-based particle swarm optimization algorithm is proposed, in which the random number is used to generate the position and velocity of the particle, and the update of the position and velocity is the addition and subtraction of the corresponding random number. Then the random number of positions is taken as the weight of the selected parts, and then the disassembled parts are selected according to the weight, and the validity of the algorithm is verified by concrete examples and benchmark examples. A particle swarm optimization (PSO) algorithm based on Pareto is proposed to solve the multi-objective disassembly line balance problem. The algorithm uses niche technology to select the global optimal solution of multi-objective, Pareto dominance and dispersion as individual evaluation and local optimal solution selection. Finally, the effectiveness of the algorithm is verified by concrete examples and benchmark examples. An ant colony algorithm based on priority is proposed to solve the multi-objective disassembly line balance problem. The algorithm takes into account the heuristic information of disassembly time, harm and requirement rules, and synthetically considers the use of prior knowledge. The search efficiency of the algorithm is improved by exploring the new path and selecting three kinds of hybrid search mechanism randomly, and the effectiveness of the algorithm is verified by concrete examples and benchmark examples.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP18;TH186

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本文編號(hào)：1629419