基于OBDD的拆卸序列表示模型的优化

拆卸序列的紧凑表示是拆卸序列的评价与优化的基础,AND-OR图是拆卸序列表示的一种典型方法,但是拆卸序列的AND-OR图表示模型存在冗余信息。通过对AND-OR图模型作等价变换,消除AND-OR图表示模型中的冗余信息。设计了拆卸序列的优化表示模型的生成算法,建立了优化的基于OBDD的拆卸序列表示模型,用来表示复杂装配体的拆卸序列。并论证了优化模型的拆卸序列表示可以节省存储空间,最后的实验表明,基于OBDD的拆卸序列优化表示模型的存储空间远小于AND-OR图的存储空间。     

基于证据理论的产品选型决策方法

 基于模糊数学原理和证据理论,提出了综合处理不确定性设计信息、合理选择产品机械结构类型的方法。为此,首先利用已有的产品实例为每个产品子类提取一组设计规则,并为每个子类构造一个可以描述其典型特征的模糊向量;然后在模糊机制下统一处理以普通实数、实数域上的区间值和模糊数表达的设计信息,采用模糊推理、证据理论等不精确推理方法处理不确定性信息,确定产品类型。上述方法可作为智能设计系统求解设计问题的推理策略,并实现知识的自动获取。通过一个选型设计的实例,验证了该方法的正确性。     

拆卸与或图生成及其权重不定问题研究

为了高效地规划装配体的拆卸序列,首先利用生成完备可行子装配体的算法构造所有可行的拆卸操作,并以与或图的形式将所有可行的拆卸操作加以表达,对该与或图进行简约,从而构建能够表达所有可行拆卸序列的拆卸与或图;为了基于该拆卸与或图规划拆卸序列,利用广义权重解决了拆卸与或图中有向边上权重不确定的问题,并给出了广义权重的计算方法;最后,通过实例验证了这种拆卸与或图表达拆卸序列的高效性和用于拆卸序列规划的可行性和有效性.结果表明拆卸与或图是一种可用于拆卸序列规划的高效模型.     

多类约束下的随机混流U型拆卸线平衡排序问题优化

为提高混流产品拆卸效率,针对固定工作站数量约束、位置约束、优先关系约束,考虑任务操作完成时间的不确定性,建立了以最小化循环时间(Cycle Time,CT)和最小化工作站平均空闲时间为目标的混流U型拆卸线平衡排序问题的数学模型。结合混流拆卸线的具体特点,提出了一种改进的并行邻域搜索算法(Improved Parallel Neighborhood Search,IPNS),该算法定义两类不同的邻域结构,采用动态搜索策略,通过独立搜索以及直接交换邻域的方式以最大限度寻找最优解。最后,通过多个算例验证了算法的有效性。     

基于模块化设计的混合图拆卸建模研究

拆卸建模是拆卸序列规划的基础,也是绿色设计的关键。在传统的拆卸序列规划过程中,随着产品零件数目的增加会造成“组合爆炸”问题。为解决该问题,提出了一种基于模块化设计思想的拆卸建模方法。首先,运用图论中有向图和无向图的概念分别表示零、部件之间的约束关系,并将有向图和无向图结合成混合图,建立产品混合图拆卸模型;其次,利用模块化设计中的模块划分原则生成子装配体,通过将模块化设计方法整合入混合图模型中,得到模块化设计的混合图拆卸模型,利用该模型可以有效地解决图论模型中边与顶点产生“组合爆炸”的问题;最后,以模块化设计的计算机主机箱的混合图拆卸建模为例来验证所提出的方法。     

基于可信性理论的闭环供应链生产计划

针对生产、库存和拆卸能力有限情况下,考虑可回收旧产品数量、分销商需求量、生产成本、外包成本、单位旧产品拆卸时间、拆卸成本和单位产品生产时间为模糊不确定数,以供应链总成本最小化为目标,采用期望值规划和机会约束规划相结合的形式,建立不确定环境下供应链生产计划模型。基于可信性规划方法,将不确定模型转化为清晰确定形式。最后,设计算例,通过求解给出在供应链总成本最小下,企业各个计划期内的最优生产量、零部件采购量以及制造商和回收中心对物料的运输计划;并分析了可信性水平、再制造率和回收率变动对供应链总成本的影响,为企业制定生产计划提供参考依据。     

在Pro/E中自动生成零件拆卸优先约束矩阵

拆卸优先约束矩阵为建立产品的拆卸层次信息图模型提供了基础信息,保证了拆卸序列规划结果的正确性和有效性.在Pro/E中用C语言完成了零件拆卸优先约束矩阵自动生成的二次开发,包括模型调入和预处理、简单拆卸路径生成和包容盒逐级分解的干涉检验等3个模块.解决了自动判别节点可拆卸性的关键问题,为机电产品基于拆卸层次信息图建模的自动化提供了有力的支持.     

基于GA-ANFIS的边坡稳定性评价

自适应神经模糊推理系统将模糊推理的可解释性和神经网络的自适应、自学习的能力结合起来,克服了边坡岩体的不确定性问题带给边坡稳定性分析的巨大困难,同时针对模糊推理系统内部参数设定的合理性问题,建立了基于遗传算法的自适应模糊推理评判模型。结果表明,GA-ANFIS评判模型结果与现场监测情况吻合,从而使其成为边坡稳定性评价的一种有效方法。     

工程液压油缸的拆解能量分析

拆解能量是产品可拆卸性评价的关键指标,对产品的可拆卸性设计和再制造回收决策具有重要意义.基于图论思想,构建油缸的拆解约束图并生成油缸的拆卸序列;定义零部件拆解能量的组成模型和传送带移出零部件的能量消耗模型;讨论油缸零部件间典型连接方式与约束解除所消耗能量的数学关系.以某型号工程液压油缸为例,对其零部件的不同拆卸序列进行总的拆解能量分析计算与评估,为油缸的拆卸序列规划、专用拆卸设备设计和液压油缸的可拆卸性设计提供参考.     

基于模糊逻辑的产品意象造型设计研究

为设计出符合消费者感性需求的产品,采用基于模糊逻辑的产品意象造型设计方法。首先确定感性词汇与造型设计要素,利用模糊逻辑建立二者之间的关系。通过模糊化、模糊规则的构建、模糊推理以及反模糊化等过程进行模糊逻辑控制器的设计,用Matlab建立仿真模型,最后通过测试验证了模型的有效性。结合折叠自行车造型设计进行研究,结果表明该方法是正确可行的。     

Integrated assembly and disassembly sequence planning using a GA approach

In a product life cycle, an assembly sequence is required to produce a new product at the start, whereas a disassembly sequence is needed at the end. In typical assembly and disassembly sequence planning approaches, the two are performed as two independent tasks. In this way, a good assembly sequence may contradict the cost considerations in the disassembly sequence, and vice versa. In this research, an integrated assembly and disassembly sequence planning model is presented. First, an assembly precedence graph (APG) and a disassembly precedence graph (DPG) are modelled. The two graphs are transformed into an assembly precedence matrix (APM) and a disassembly precedence matrix (DPM). Second, a two-loop genetic algorithm (GA) method is applied to generate and evaluate the solutions. The outer loop of the GA method performs assembly sequence planning. In the inner loop, the reverse order of the assembly sequence solution is used as the initial solution for disassembly sequence planning. A cost objective by integrating the assembly costs and disassembly costs is formulated as the fitness function. The test results show that the developed method using the GA approach is suitable and efficient for the integrated assembly and disassembly sequence planning. Example products are demonstrated and discussed.     

回购契约下应对突发事件的供应链协调策略

在基本回购契约模型基础上,分析了回购契约下实现供应链协调的条件,并针对突发事件造成零售商面临的需求分布变化时供应链协调被打破的问题,提出了具有抗突发事件性的回购契约。这个契约主要将增加的供应商成本考虑进回购价格,使得供应商新增加的成本能够在供应商和零售商之间分配,从而使新的回购契约实现对突发事件的协调应对。应用一个算例对比加以说明。     

Quality control of an unreliable random FMS: with Bernoulli and CSP sampling

It is widely recognized that disassembly-based product End-of-Life strategies, such as component reuse or simple fraction material recycling, are environmentally beneficial. However, current disassembly costs hinder a widespread application of these strategies. This paper quantifies the disassembly time reductions required to achieve economic feasibility of systematic product disassembly. A modelling framework, based on linear programming, is used to investigate the effect of reducing the expected disassembly time and cost on the selection of the optimal End-of-Life strategy. The problem is optimized from an End-of-Life treatment facility point of view. All findings are based on the Belgian cost and price information captured in spring 2004. The linear programming model shows that for small products from the Waste of Electric and Electrical Equipment (WEEE) category disassembly-based End-of-Life strategies will hardly become optimal, while for medium- and large-sized products, this scenario can be made optimal if a substantial disassembly time reduction is achieved. Possible strategies to realize such reduction are briefly sketched.     

Mixed model disassembly line balancing problem with fuzzy goals

The collection of used products is the driving force of remanufacturing systems and enterprises can gain significant economic, technical and social benefits from recycling. All products are disassembled up to some level in remanufacturing systems. The best way to disassemble returned products is valid by a well-balanced disassembly line. In this paper, a mixed integer programming (MIP) model is proposed for a mixed model disassembly line balancing (MMDLB) problem with multiple conflicting objectives: (1) minimising the cycle time, (2) minimising the number of disassembly workstations and (3) providing balanced workload per workstation. In most real world MMDLB problems, the targeted goals of decision makers are frequently imprecise or fuzzy because some information may be incomplete and/or unavailable over the planning horizon. This study is the first in the literature to offer the binary fuzzy goal programming (BFGP) and the fuzzy multi-objective programming (FMOP) approaches for the MMDLB problem in order to take into account the vague aspirations of decision makers. An illustrative example based on two industrial products is presented to demonstrate the validity of the proposed models and to compare the performances of the BFGP and the FMOP approaches.     

Planning product disassembly for material recovery opportunities

This paper addresses an increasingly important aspect of product design and its relationship to life-cycle costing: disassembly for material recovery opportunities (MRO). MRO is defined as an opportunity to reclaim post-consumer products for recycling, remanufacturing and re-use. The authors have developed a methodology which can be used to identify and assess cost-effective characteristics of disassembly for the recovery of products. The central focus of this paper is aimed at improving the efficiency of the disassembly planning process and generating an optimal disassembly sequence. Four criteria are established to optimize the generation of the disassembly sequence: (1) material compatibility, (2) clustering for disposal, (3) concurrent disassembly operations, and (4( maximizing yield. In this paper we define the ‘disassemblability’ of a product as the ability to optimize the design and disassembly process for removal of specific pans or materials in a manner which will minimize costs. Steps have been taken to incorporate this methodology within a life-cycle analysis software tool (EDIT) to be used at the early concept stage of product design.     

Heuristic algorithms for minimising total recovery cost of end-of-life products under quality constraints

Recently, the optimisation of end-of-life (EOL) product recovery processes has been highlighted. At the inspection phase after disassembly, each part can have various recovery options such as reuse, reconditioning, remanufacturing, and disposal. Depending on the selected options of parts, the values of recovered products that are made by reassembling parts will be different. Hence, it is important to decide appropriate recovery options of parts at the treatment of EOL products, in order to maximise the values of recovered products. To this end, this study deals with a decision making problem to select the best recovery options of parts for minimising the total recovery cost of products under quality constraints. This problem is formulated with a mixed integer nonlinear programming model and heuristic search algorithms are proposed to resolve it. A case study for a turbocharger product is introduced with computational experiments of the proposed algorithms.     

Fuzzy geometric programming approach to a fuzzy machining economics model

Machining economics is an important function of the process planning activity for manufacturing products with high quality and low cost. The machining economics model usually contains a highly non-linear objective function and equations that could be formulated as a geometric programming problem. The paper develops a solution method for deriving the fuzzy objective value of the fuzzy machining economics problem when some of the parameters in the problem are fuzzy numbers. A pair of geometric programs is formulated to calculate the lower and upper bounds of the unit production cost at possibility level α. With the ability to calculate the fuzzy objective value developed, it might help lead to a more realistic modelling effort. The developed methodology can also be applied to other engineering design problems with fuzzy numbers.     

Selective disassembly sequencing with random operation times in parallel disassembly environment

Selective disassembly sequencing is the problem of determining the sequence of disassembly operations to extract one or more target components of a product. This study considers the problem with random operation times in the parallel disassembly environment in which one or more components can be removed at the same time by a single disassembly operation. After representing all possible disassembly sequences using the extended process graph, a stochastic integer programming model is developed for the objective of minimising the sum of disassembly and penalty costs, where the disassembly costs consist of sequence-dependent set-up and operation costs and the penalty cost is the expectation of the costs incurred when the total disassembly time exceeds a threshold value. A sample average approximation-based solution algorithm is proposed that incorporates an optimal algorithm to solve the sample average approximating problem under a given set of scenarios for disassembly operation times. The algorithm is illustrated with a hand-light case and a large-sized random instance, and the results are reported.     

Evaluation model and algorithm of product disassembly process with stochastic feature

Disassembly planning is considered as the optimization of disassembly sequences with the target of the shortest disassembly time, the lowest disassembly cost, and the minimum disassembly energy consumption. However, obsolete products suffer from the influence of a variety of uncertainties, the disassembly process of products has the strong uncertain feature. Traditionally, to account for this uncertainty, each removal operation or removal task is assumed to be an activity or event with certain probability, and the determination of the optimal path of a disassembly process is merely a probabilistic planning problem based on this assumption. In this article, based on the established stochastic disassembly network graph, combined with different disassembly decision-making criterion, typical stochastic models for disassembly time analysis are developed. In addition, a two-phase approach is proposed to solve the typical stochastic models. Initially, according to different removal probability density functions, disassembly probability density functions of feasible disassembly paths are determined by a time-domain method or frequency-domain method, and additionally, after the disassembly probability density functions have been obtained, the quantitative evaluation of a product disassembly process and stochastic optimization of feasible disassembly paths are realized by a numerical solution method. Finally, a numerical example is illustrated to test the proposed concepts and the effectiveness of the proposed approach.