本文選題：柔性作業(yè)車間 + 差分進(jìn)化算法��； 參考：《華中科技大學(xué)》2014年碩士論文

【摘要】：柔性作業(yè)車間調(diào)度是在實(shí)際制造生產(chǎn)中廣泛存在的一類問(wèn)題。對(duì)該問(wèn)題的研究，可以有效提高車間的生產(chǎn)效率，縮短制造周期。此外，實(shí)際生產(chǎn)調(diào)度問(wèn)題還具有多目標(biāo)、動(dòng)態(tài)性等特點(diǎn)，同時(shí)需要對(duì)生產(chǎn)中各種突發(fā)事件進(jìn)行及時(shí)的響應(yīng)。 本文首先研究了經(jīng)典的柔性作業(yè)車間靜態(tài)調(diào)度問(wèn)題。在該問(wèn)題的研究中，提出了一種“預(yù)調(diào)度確定各工序的加工機(jī)器”的優(yōu)化策略，并將其應(yīng)用到差分進(jìn)化算法的種群初始化中，提高初始種群的質(zhì)量。同時(shí)，提出了一種新的種群改進(jìn)策略，，在算法進(jìn)化陷入局部最優(yōu)解，最優(yōu)解一段時(shí)間不改進(jìn)的情況下，適時(shí)得去改進(jìn)種群的質(zhì)量。將該策略融合到差分進(jìn)化算法的框架中得到改進(jìn)差分進(jìn)化算法。通過(guò)和其他算法的比較驗(yàn)證本文提出的改進(jìn)差分進(jìn)化算法求解性能優(yōu)越。 隨后，本文研究了不同再調(diào)度周期下的柔性作業(yè)車間動(dòng)態(tài)調(diào)度問(wèn)題。通過(guò)模擬隨機(jī)工件到達(dá)的生產(chǎn)環(huán)境，運(yùn)用周期性再調(diào)度的調(diào)度策略將各個(gè)工件依次劃入到對(duì)應(yīng)的調(diào)度區(qū)間去進(jìn)行求解。在各個(gè)調(diào)度區(qū)間上，以效率和穩(wěn)定性為目標(biāo)，設(shè)計(jì)一種基于Pareto概念的多目標(biāo)差分進(jìn)化算法對(duì)該調(diào)度區(qū)間的工件進(jìn)行調(diào)度優(yōu)化，并提出了一種二級(jí)選擇策略應(yīng)用于多目標(biāo)算法中，最后從優(yōu)化算法獲得的非支配解集中采用決策策略選出一個(gè)調(diào)度方案作為實(shí)際調(diào)度加工方案。通過(guò)研究在不同的再調(diào)度周期下，對(duì)先后到達(dá)相同數(shù)量的工件進(jìn)行調(diào)度得到的最后的完工時(shí)間、總拖期、總效率和總穩(wěn)定性之間的差異，對(duì)結(jié)果進(jìn)行分析，得出了不同再調(diào)度周期對(duì)各個(gè)性能指標(biāo)的影響，便于指導(dǎo)生產(chǎn)實(shí)踐。 最后，本文研究了不同動(dòng)態(tài)事件下的柔性作業(yè)車間動(dòng)態(tài)調(diào)度問(wèn)題�？紤]了機(jī)器故障/修復(fù)，緊急訂單到達(dá)，普通訂單到達(dá)等動(dòng)態(tài)事件，采用基于周期與事件驅(qū)動(dòng)的再調(diào)度策略。在窗口工件的調(diào)度優(yōu)化中，以完工時(shí)間，總拖期，總偏離度為優(yōu)化目標(biāo)，并設(shè)計(jì)了Pareto決策策略從最后的非支配解集中選擇出一個(gè)合適的方案作為實(shí)際調(diào)度方案。通過(guò)實(shí)例測(cè)試，比較了在動(dòng)態(tài)事件發(fā)生時(shí)，再調(diào)度前后調(diào)度方案的變化。
[Abstract]:Flexible job shop scheduling is a widespread problem in actual manufacturing. The research on this problem can effectively improve the production efficiency and shorten the manufacturing cycle. In addition, the actual production scheduling problem also has the characteristics of multi-objective, dynamic and so on, and needs timely response to all kinds of unexpected events in production. In this paper, the classical static scheduling problem of flexible job shop is studied. In order to improve the quality of the initial population, an optimization strategy of "pre-scheduling and determining the processing machines in each process" is proposed in this paper, and it is applied to the population initialization of the differential evolution algorithm (DEA) to improve the quality of the initial population. At the same time, a new population improvement strategy is proposed to improve the population quality in time when the algorithm evolves into a local optimal solution and the optimal solution is not improved for a period of time. The strategy is fused into the framework of differential evolution algorithm (DEA) and improved differential evolutionary algorithm (DEA). Compared with other algorithms, the improved differential evolution algorithm proposed in this paper is superior to other algorithms. Then, the dynamic scheduling problem of flexible job shop under different rescheduling periods is studied. By simulating the production environment arrived by random jobs, the scheduling strategy of periodic rescheduling is used to assign each job to the corresponding scheduling interval in turn to solve the problem. Aiming at efficiency and stability, a multi-objective differential evolutionary algorithm based on Pareto concept is designed to optimize the scheduling of jobs in each scheduling interval, and a two-level selection strategy is proposed to apply to the multi-objective algorithm. Finally, a scheduling scheme is selected from the non-dominated solution set obtained by the optimization algorithm as an actual scheduling scheme. By studying the difference between the final completion time, the total delay period, the total efficiency and the total stability obtained by scheduling the same number of jobs in different rescheduling cycles, the results are analyzed. The influence of different rescheduling cycle on each performance index is obtained, which is convenient to guide production practice. Finally, the dynamic scheduling problem of flexible job shop under different dynamic events is studied. Dynamic events such as machine fault / repair emergency order arrival and general order arrival are considered and rescheduling strategy based on periodicity and event driven is adopted. In the scheduling optimization of the window workpiece, the completion time, total delay time and total deviation are taken as the optimization objectives, and the Pareto decision strategy is designed to select a suitable scheme from the final non-dominated solution set as the actual scheduling scheme. The change of scheduling scheme before and after rescheduling is compared by example test.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TH186;TP18

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相關(guān)期刊論文 前3條

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