【摘要】：電子信息產(chǎn)業(yè)市場的驅(qū)動技術(shù)創(chuàng)新,導致電子產(chǎn)品的更新?lián)Q代速度加快,許多電子產(chǎn)品在沒達到使用壽命時就報廢,產(chǎn)生了非常多的電子垃圾,如何回收并合理利用已經(jīng)成了全球性的難題。因此,本文對面向拆卸多類型電子產(chǎn)品的混流U 型拆卸線平衡排序(Mixed-Model U-Shaped Disassembly Line Balancing and Sequencing Problem,MUDLB/S)問題進行了研究,主要包括以下兩個部分:針對循環(huán)時間(Cycle Time,CT)為主要約束下的MUDLB/S問題,本文考慮了拆卸任務(wù)完成任務(wù)時間隨機分布,建立了該問題最小拆卸線平均閑置率、盡早拆卸危害和高需求零部件、最小化平均方向改變次數(shù)的多目標優(yōu)化模型;此外,還設(shè)計了基于分解和動態(tài)鄰域搜索的混合多目標進化算法(Hybrid Multi-objective Evolutionary Algorithm Based on Decomposition,HMOEA/D)來同時優(yōu)化以上多個目標,該算法通過采用彈性任務(wù)分配策略、動態(tài)鄰域結(jié)構(gòu)和動態(tài)調(diào)整權(quán)重以保證解的可行性并搜索得到分布較好的非劣解集。最后,通過正交實驗設(shè)計了一組算例進行仿真,進而與并行鄰域搜索算法和基于局部搜索的遺傳算法進行了對比,仿真實驗的結(jié)果表明了本文所提出算法的優(yōu)越性。針對固定工作站數(shù)量為主要約束的MUDLB/S問題,考慮了拆卸任務(wù)完成時間服從隨機分布和任務(wù)具有位置約束的情況,并建立了以最小化循環(huán)時間CT和最小化工作站平均空閑時間為目標的數(shù)學模型;此外,設(shè)計了改進的并行鄰域搜索算法(Improved Parallel Neighborhood Search,IPNS)來依次優(yōu)化以上目標,該算法定義兩類不同的鄰域結(jié)構(gòu),采用動態(tài)搜索策略,通過獨立搜索以及直接交換鄰域的方式以最大限度尋找最優(yōu)解。最后,通過仿真實驗表明,本文所提算法不論在搜索效率還是解的質(zhì)量方面都優(yōu)于并行鄰域搜索算法。
[Abstract]:The driving technology innovation in the electronic information industry market has led to the acceleration of the upgrading of electronic products. Many electronic products are scrapped before they reach their useful life, resulting in a lot of electronic waste. How to recycle and make rational use of the problem has become a global problem. Therefore, in this paper, the hybrid U-type disassembly line balancing (Mixed-Model U-Shaped Disassembly Line Balancing and Sequencing Problem,MUDLB/S) problem for disassembly multi-type electronic products is studied, which includes the following two parts: for the MUDLB/S problem with cycle time (Cycle Time,CT) as the main constraint, In this paper, the random distribution of disassembly task completion time is considered, and a multi-objective optimization model of minimum disassembly line average idle rate, early disassembly hazard and high demand parts, minimizing the number of average direction changes is established. A hybrid multiobjective evolutionary algorithm (Hybrid Multi-objective Evolutionary Algorithm Based on Decomposition,HMOEA/D) based on decomposition and dynamic neighborhood search is designed to optimize the above targets simultaneously. Dynamic neighborhood structure and dynamic adjustment of weights to ensure the feasibility of the solution and search for a well-distributed set of non-inferior solutions. At last, a set of examples are designed and simulated by orthogonal experiment, and then compared with the parallel neighborhood search algorithm and the genetic algorithm based on local search. The simulation results show the superiority of the proposed algorithm. For the MUDLB/S problem with fixed number of workstations as the main constraint, the random distribution of the completion time of the disassembly task and the position constraint of the task are considered. A mathematical model aiming at minimizing the cycle time (CT) and minimizing the average idle time of a workstation is established. In addition, an improved parallel neighborhood search algorithm (Improved Parallel Neighborhood Search,IPNS) is designed to optimize the above objectives in turn. The algorithm defines two different neighborhood structures and uses dynamic search strategy to find the optimal solution to maximum extent by independent search and direct exchange of neighborhood. Finally, the simulation results show that the proposed algorithm is superior to the parallel neighborhood search algorithm in terms of search efficiency and solution quality.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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