多目标集成式工艺规划与车间调度问题的求解方法研究

发布时间：2017-12-31 20:25

本文关键词：多目标集成式工艺规划与车间调度问题的求解方法研究　出处：《华中科技大学》2014年博士论文　论文类型：学位论文

【摘要】：集成式工艺规划与车间调度是制造系统中急需解决的关键问题。在实际的企业生产当中,管理者需要寻求满足多个目标的合理折中方案。本文针对多目标集成式工艺规划与车间调度(Integrated Process Planning and Scheduling, IPPS)问题开展研究。IPPS问题是最困难的NP-Complete组合优化问题之一,多目标IPPS问题还需同时优化多个目标,问题的求解难度大大增加。目前国内外鲜有关于多目标IPPS问题的研究,相关研究还处于起步阶段。本文提出了先优化、后决策的多目标IPPS司题求解策略。在优化阶段,工艺规划为车间调度不断地提供近优的工艺路线以实现集成优化,采用多目标优化算法求得非支配解集。在决策阶段,使用决策准则从非支配解集中挑选出最终方案。围绕多目标IPPS问题的求解方法,在上述求解策略指导下,本文以一种新兴的蜂群算法——蜜蜂繁殖优化(Honey Bees Mating Optimization,HBMO)算法为依托,分别对柔性工艺规划方法、多目标IPPS优化方法和多目标不确定IPPS优化方法进行了深入研究,并探讨了多目标IPPS问题的决策方法。本文提出了基于HBMO算法的柔性工艺规划方法。针对工艺规划问题中存在的加工柔性、加工次序柔性和加工机器柔性,提出了多维编码方法分别处理多种柔性因素；设计了HBMO算法中蜂王婚飞阶段、幼蜂生成阶段和工蜂培育幼蜂阶段的具体操作；采用实例对提出的HBMO算法进行了测试,并与其他算法进行了比较,验证了提出的算法具有更高的求解效率和更好的稳定性。本文提出了基于HBMO算法的多目标IPPS优化方法。该优化方法中,使用已提出的柔性工艺规划方法,为车间调度不断地提供近优的工艺路线；设计了一种新的多目标HBMO算法优化车间调度,提出了基于免疫原理的车间调度种群多样性保持策略,并采用快速非支配排序方法更新蜂王集和雄蜂种群；使用测试实例对提出的优化方法进行验证,与其他算法进行了比较,验证了提出的多目标IPPS优化方法的有效性和优越性。实际生产过程中存在着大量的不确定事件,不确定环境下的IPPS问题调度结果能更好地指导实际生产。本文对多目标不确定IPPS问题进行了研究。基于模糊集理论建立了多目标不确定IPPS问题模型,该模型综合考虑了模糊数目标和度量调度方案不确定性的目标；设计了基于HBMO算法的多目标不确定IPPS优化方法,采用模糊数的相关操作实现适应度评价、非支配关系判断以及调度解码；设计了多目标不确定IPPS问题测试实例,并使用提出的优化方法对测试实例进行求解,验证了该方法的有效性。在多目标IPPS问题的决策阶段,本文提出了一种基于组合权重TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)的多目标IPPS决策方法。结合待决策方案集中元素的特点,设计了相应的决策矩阵规范化操作方法；针对不同目标值的特点,提出了相应的目标权重确定方法；使用提出的决策方法在不同偏好情况下对已求得的非支配解集进行决策,验证了该方法的有效性。结合上述理论成果,根据某机床厂非标设备生产车间的生产情况,分析了该车间中实际存在的多目标IPPS问题,将本文的理论成果应用于实际车间的生产,计算结果验证了本文提出的多目标IPPS求解方法的有效性。最后对全文的成果进行了总结,并对下一步的研究方向进行了展望。
[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.

【学位授予单位】：华中科技大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TH186;TP18

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