發(fā)布時(shí)間：2017-12-31 20:25

  本文關(guān)鍵詞：多目標(biāo)集成式工藝規(guī)劃與車間調(diào)度問題的求解方法研究　出處：《華中科技大學(xué)》2014年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 集成式工藝規(guī)劃與車間調(diào)度 多目標(biāo)優(yōu)化 多目標(biāo)決策 蜜蜂繁殖優(yōu)化算法 模糊集 不確定調(diào)度

【摘要】：集成式工藝規(guī)劃與車間調(diào)度是制造系統(tǒng)中急需解決的關(guān)鍵問題。在實(shí)際的企業(yè)生產(chǎn)當(dāng)中,管理者需要尋求滿足多個(gè)目標(biāo)的合理折中方案。本文針對多目標(biāo)集成式工藝規(guī)劃與車間調(diào)度(Integrated Process Planning and Scheduling, IPPS)問題開展研究。IPPS問題是最困難的NP-Complete組合優(yōu)化問題之一,多目標(biāo)IPPS問題還需同時(shí)優(yōu)化多個(gè)目標(biāo),問題的求解難度大大增加。目前國內(nèi)外鮮有關(guān)于多目標(biāo)IPPS問題的研究,相關(guān)研究還處于起步階段。 本文提出了先優(yōu)化、后決策的多目標(biāo)IPPS司題求解策略。在優(yōu)化階段,工藝規(guī)劃為車間調(diào)度不斷地提供近優(yōu)的工藝路線以實(shí)現(xiàn)集成優(yōu)化,采用多目標(biāo)優(yōu)化算法求得非支配解集。在決策階段,使用決策準(zhǔn)則從非支配解集中挑選出最終方案。圍繞多目標(biāo)IPPS問題的求解方法,在上述求解策略指導(dǎo)下,本文以一種新興的蜂群算法——蜜蜂繁殖優(yōu)化(Honey Bees Mating Optimization,HBMO)算法為依托,分別對柔性工藝規(guī)劃方法、多目標(biāo)IPPS優(yōu)化方法和多目標(biāo)不確定IPPS優(yōu)化方法進(jìn)行了深入研究,并探討了多目標(biāo)IPPS問題的決策方法。 本文提出了基于HBMO算法的柔性工藝規(guī)劃方法。針對工藝規(guī)劃問題中存在的加工柔性、加工次序柔性和加工機(jī)器柔性,提出了多維編碼方法分別處理多種柔性因素；設(shè)計(jì)了HBMO算法中蜂王婚飛階段、幼蜂生成階段和工蜂培育幼蜂階段的具體操作；采用實(shí)例對提出的HBMO算法進(jìn)行了測試,并與其他算法進(jìn)行了比較,驗(yàn)證了提出的算法具有更高的求解效率和更好的穩(wěn)定性。 本文提出了基于HBMO算法的多目標(biāo)IPPS優(yōu)化方法。該優(yōu)化方法中,使用已提出的柔性工藝規(guī)劃方法,為車間調(diào)度不斷地提供近優(yōu)的工藝路線；設(shè)計(jì)了一種新的多目標(biāo)HBMO算法優(yōu)化車間調(diào)度,提出了基于免疫原理的車間調(diào)度種群多樣性保持策略,并采用快速非支配排序方法更新蜂王集和雄蜂種群；使用測試實(shí)例對提出的優(yōu)化方法進(jìn)行驗(yàn)證,與其他算法進(jìn)行了比較,驗(yàn)證了提出的多目標(biāo)IPPS優(yōu)化方法的有效性和優(yōu)越性。 實(shí)際生產(chǎn)過程中存在著大量的不確定事件,不確定環(huán)境下的IPPS問題調(diào)度結(jié)果能更好地指導(dǎo)實(shí)際生產(chǎn)。本文對多目標(biāo)不確定IPPS問題進(jìn)行了研究。基于模糊集理論建立了多目標(biāo)不確定IPPS問題模型,該模型綜合考慮了模糊數(shù)目標(biāo)和度量調(diào)度方案不確定性的目標(biāo)；設(shè)計(jì)了基于HBMO算法的多目標(biāo)不確定IPPS優(yōu)化方法,采用模糊數(shù)的相關(guān)操作實(shí)現(xiàn)適應(yīng)度評價(jià)、非支配關(guān)系判斷以及調(diào)度解碼；設(shè)計(jì)了多目標(biāo)不確定IPPS問題測試實(shí)例,并使用提出的優(yōu)化方法對測試實(shí)例進(jìn)行求解,驗(yàn)證了該方法的有效性。 在多目標(biāo)IPPS問題的決策階段,本文提出了一種基于組合權(quán)重TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)的多目標(biāo)IPPS決策方法。結(jié)合待決策方案集中元素的特點(diǎn),設(shè)計(jì)了相應(yīng)的決策矩陣規(guī)范化操作方法；針對不同目標(biāo)值的特點(diǎn),提出了相應(yīng)的目標(biāo)權(quán)重確定方法；使用提出的決策方法在不同偏好情況下對已求得的非支配解集進(jìn)行決策,驗(yàn)證了該方法的有效性。 結(jié)合上述理論成果,根據(jù)某機(jī)床廠非標(biāo)設(shè)備生產(chǎn)車間的生產(chǎn)情況,分析了該車間中實(shí)際存在的多目標(biāo)IPPS問題,將本文的理論成果應(yīng)用于實(shí)際車間的生產(chǎn),計(jì)算結(jié)果驗(yàn)證了本文提出的多目標(biāo)IPPS求解方法的有效性。 最后對全文的成果進(jìn)行了總結(jié),并對下一步的研究方向進(jìn)行了展望。
[Abstract]:Integrated process planning and scheduling is the key problem needed to solve in the manufacturing system. In the actual production, managers need to seek a reasonable compromise to meet multiple objectives. Aiming at the multi-objective integrated process planning and scheduling (Integrated Process Planning and Scheduling, IPPS) on.IPPS of problems NP-Complete is one of the most difficult combinatorial optimization, multi-objective IPPS problem also need to simultaneously optimize multiple objectives, the difficulty of solving the problem at home and abroad have greatly increased. A multi-objective IPPS problem research, related research is still in its infancy.
This work presents a multi-objective optimization, IPPS problem solving strategy after the decision. In the optimization stage, process planning for shop scheduling to provide near optimal process route to realize the integrated optimization, using multi-objective optimization algorithm to obtain the non dominated solution set. In the decision-making stage, using decision rule from the Pareto set out the final solution. Solution around the multi-objective IPPS problem, the solving strategy under the guidance, based on a new bee colony algorithm (Honey Bees Mating Optimization propagation optimization algorithm, HBMO) as the basis, with flexible process planning method, uncertain IPPS optimization method is studied multi-objective optimization method IPPS and multi object, and discusses the decision-making method of multi-objective IPPS problem.
This paper proposes a flexible process planning method based on HBMO algorithm. Aiming at the existing problems in the machining process planning of flexible, flexible processing order and processing machine flexibility, proposed a multidimensional encoding method are used to deal in a variety of flexible design factors; the queen swarming phase of the HBMO algorithm, young generation stage and worker bee larvae cultivation of specific operation stage; the example of the proposed HBMO algorithm is tested and compared with other algorithms, the proposed algorithm has verified the stability of higher efficiency and better solution.
This paper presents a multi-objective IPPS optimization method based on HBMO algorithm. The optimization method, the use of flexible process planning method has been proposed, for shop scheduling to provide near optimal route; a shop scheduling optimization new multi-objective HBMO algorithm is designed, based on the principle of immune diversity scheduling the strategy of keeping, and the fast non dominated sorting method to update the queen and drone set of population; the optimization method is verified using the test examples, compared with other algorithms, verify the validity and superiority of the optimization method of multi targets proposed by IPPS.
There are a lot of uncertain events in the actual production process, the problem of uncertain IPPS scheduling results can guide the actual production environment better. The multi-objective problem of uncertain IPPS was studied. The fuzzy set theory to establish the multi-objective problem of uncertain IPPS model based on the model, considering the fuzzy goals and metrics scheduling uncertainty target; design multi-objective HBMO algorithm based on uncertain IPPS optimization method, using fuzzy number to achieve operation of fitness evaluation, non dominance relation judgment and scheduling decoding; multi-objective design problem of uncertain IPPS test cases, and to solve the test case using the proposed optimization method of verification the effectiveness of the proposed method.
In the decision-making phase of multi-objective IPPS problem, this paper proposes a combination weights based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution IPPS) the multi-objective decision method. Combined with the characteristics of decision plan elements, design the corresponding decision matrix standardized operation method; according to the characteristics of different target value. The method to determine the weights of the corresponding target; use decision method proposed in the case of different preferences of the obtained non dominated solutions to make decisions, to verify the effectiveness of the method.
According to the above theoretical results, according to the production of a machine tool factory of non-standard equipment production workshop, analyzes the problems exist in the multi object IPPS in the workshop, the theoretical results are applied in actual production, the calculation results verify the validity of the multi-objective IPPS proposed solution method.
Finally, the results of the full text are summarized, and the future research direction is prospected.

【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TH186;TP18

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