Disassembly modeling, planning, and leveling for a cam-operated rotary switch assembly: a case study

The manufacturing organizations have been witnessing a paradigm shift on designing products and services from manufacturing to sustainability. Product disassembly is a vital strategy for industrial recycling and remanufacturing which generates the desired parts and/or subassemblies by means of separation of a product into its elements. This article presents the appropriate methods for modeling planning and leveling the disassembly process of a cam-operated rotary switch assembly. The methodology involves disassembly modeling using a graphical approach. Disassembly process planning has been done using reverse assembly approach. Disassembly leveling has been done to maintain profitability and environmental features at a desired level. An optimal disassembly sequence has been derived using disassembly leveling. The resultant cost of disassembly for reusable components has been computed. The article presents a unique contribution to the field of designing for disassembly from the perspective of rotary switches.     

Constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem

Globalization, coupled with environmental requirements, has spearheaded new levels of requirements for product end-of-life, the last phase of product lifecycle management especially for product remanufacturing and recycling which involves product disassembly to retrieve the desired parts and subassemblies. Selection of optimal disassembly schedule is a major challenge for remanufacturing and recycling industries as it directly affects the inventory of the manufacturing unit and influences the final product cost. This paper proposes a constraint-based simulated annealing (CBSA) algorithm methodology to determine the ordering and disassembly schedule to minimize inventory level for products with general assembly product structure, i.e., taking into consideration part commonalities. The proposed CBSA algorithm uses the constraint-based genetic operators integrated with the simulated annealing (SA) approach that makes the algorithm more search exploratory (guarantee the optimal or near-optimal solution) and converge efficiently to the optimal solutions (less time-consuming). The proposed algorithm has higher likelihood of avoiding local optima as compared with standard SA and genetic algorithms. This is achieved by exploring a population of points, rather than a single point in the solution space. The proposed methodology is validated using a numerical case study for disassembly scheduling problem with part commonality.     

Genetic algorithm and Hopfield neural network for a dynamic lot sizing problem

The dynamic lot sizing plays an important role when the demand of an item varies with time. This paper addresses a dynamic lot sizing problem (DLSP) with capacity constraint and discount price structure. Although the well-known dynamic programming (DP) of Wagner-Within is capable of providing an optimal solution for single stage lot sizing problems, it suffers from its high computational complexity. This limits the use of DP in practical problems that are generally larger in size. Two meta-heuristics, genetic algorithm (GA) and Hopfield neural network (HNN) are designed for DLSP to get best trade-off between solution quality and computational time. The DP algorithm is modeled to derive the optimal solution to the experimental problem. The optimality of GA and HNN are tested by comparing the percentage deviation of GA and HNN results against the optimal solution derived using DP.     

A genetic algorithm for the simultaneous lot sizing and scheduling problem in capacitated flow shop with complex setups and backlogging

In this paper the capacitated lot sizing and scheduling problem with sequence-dependent setups, setup carryover, and backlogging has been studied. The problem can be formulated as a mixed-integer program. Most lot sizing problems are hard to solve, especially in medium and large scale. In recent years, to deal with the complexity and find optimal or near-optimal results in reasonable computational time, a growing number of researchers have employed metaheuristic approaches to lot sizing problems. One of the most popular metaheuristics is genetic algorithm which has been applied to different optimization problems successfully. Therefore, we have developed a genetic algorithm to solve this model. To test the accuracy of the genetic algorithm, a lower bound is developed and compared against the genetic algorithm. In computational experiments, proposed genetic algorithm performed extremely well. It is concluded that the genetic algorithm is efficient and effective for this problem.     

Subassembly identification for assembly sequence planning

Assembly sequence planning is a typical of NP-complete problem which will spend a large amount of computation time or disk memory once the assembly becomes complex. The complex product or assembly is composed of many parts and the number of assembly relationships between them is numerous. To decrease the difficulty of assembly sequence planning of complex products, the subassembly identification methods are focused on. It aims to decompose a complex assembly into a limitative number of subassemblies. Each subassembly contains a relatively smaller number of parts and the assembly sequence planning tasks of them can be handled efficiently. The subassembly identification methods for assembly sequence planning are summarized with respect to assembly constraints. The assembly constraints including the topological, geometrical, and process constraints are considered and merged into the assembly models for subassembly identification. The assembly models are generally represented as directed or undirected assembly diagrams including these considered constraints. It is generally taken as the input information to generate appropriate subassemblies complying with the requirements. The graph theories and graph search algorithms, integer programming methods and the emerging techniques, such as the knowledge-based methods, the intelligent algorithms and the virtual technology, etc. are advocated to resolve the subassembly identification problem with respect to the assembly models. The hierarchical assembly tree is widely used to represent the results of subassembly identification. These useful methods are not only used to subassembly identification for assembly sequence planning, but also successfully referred to by product disassembly.     

复杂产品并行拆解建模及规划方法研究

为提高复杂产品并行拆解的效率,提出了一种针对复杂产品的并行拆解建模及规划方法。通过构建拆解约束图确定零件之间的连接关系,利用传递闭包算法进行聚类分析,将产品分解为若干个组件的集合。提取组件与组件之间的连接零件集合,利用人工蜂群算法对连接零件集合以及每个组件分别进行拆解规划,从而得到复杂产品的并行拆解模型及规划序列。以摩托车发动机的并行拆解为例,对该方法进行了验证,结果表明该方法能有效地实现复杂产品的并行拆解,提高复杂产品的拆解并行度和拆解效率。     

考虑不定拆卸程度的选择性异步并行拆卸序列规划

针对废旧产品再生过程中常用的选择性拆卸规划方法无法全面考虑所有零部件再生收益的问题,提出了考虑不定拆卸程度的选择性异步并行拆卸序列规划方法。通过拆卸混合图表达产品零部件之间的连接关系和优先约束关系,在此基础上,以目标件逆向搜索出的最小必拆零件集合为拆卸程度下限,获取不同拆卸程度集合;以拆卸时间最小、拆卸利润最大为优化目标建立了数学模型,提出了一种改进的生物地理学优化算法进行求解优化;结合拆卸序列规划问题特点,设计了基于三层链表的编码方式,采用随机拓扑结构以提高算法的搜索能力;最后以机械臂与二级圆柱圆锥齿轮减速器为实例,验证了所提模型和方法的可行性和有效性。     

面向产品回收设计的拆卸序列规划

基于绿色产品设计的思想，定义了产品的层次化结构模型，对产品的拆卸进行了建模分析，并主要讨论了拆卸序列组合爆炸、零部件邻接矩阵、拆卸算法等关键问题，实现了产品回收与再设计的拆卸序列规划。     

基于Lagrange松弛分解的多产品生产--分销系统的联合决策

考虑全球制造环境下多产品生产分销网络系统中的联合物流决策问题,包括供应商指定的生产任务、生产批量、供应商和用户之间的年运输量和订货批量,提出了基于Lagrange松弛的两层分解启发式算法(LRD)来求解联合决策模型(JDM-M),其中第一层是供应商指定的生产任务、生产批量和运输流量的联合决策(APLS-TF),第二层是运输和订货批量的联合决策(TOQ-M)。仿真分析表明LRD对于大规模的集成决策问题是行之有效的方法。     

A new multi-objective ant colony algorithm for solving the disassembly line balancing problem

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing.     

基于强化学习的群体进化算法求解双边多目标同步并行拆解线平衡问题

双碳背景下,工业制造领域朝着绿色、节能的方向转型,对废旧产品进行回收拆解以及再制造有利于推动高质量发展。论文针对大型复杂产品拆解过程中难以改变拆卸方向的问题,并综合考虑实际拆卸过程中任务之间存在的多种约束类型,研究了同步并行模式下的双边拆解线平衡问题(two-sided disassembly line balancing problem, TDLBP)。首先引入双边布局的拆解线模式,定义了与优先和或优先关系,建立了TDLBP的数学模型以优化产线布置、经济效益和安全环保三个方面共六个指标。然后提出了一种基于强化学习的群体进化算法,采用Q-learing利用所学知识选择迭代中的最佳算子,通过拥挤距离筛选Pareto解集,利用精英保留策略加速算法收敛,进而高效获取近似最优的拆解方案。最后通过求解小规模算例并对比分析,验证了所提出算法的有效性和优越性,并进行大规模案例的应用。     

资源约束下拆卸线平衡问题的建模与改进混合蛙跳算法

针对实际拆卸线中涉及的资源约束和危害零件问题,以资源总数、工作站数和危害指数为目标函数,构建了多目标资源约束拆卸线平衡问题数学模型。基于AND/OR关系,在优先关系矩阵中添加OR关系的描述,解决了产生初始解仅考虑AND关系的不足问题。提出了一种融入Pareto思想的改进混合蛙跳算法  ,该算法采用基于满意度的改进排序分组策略来解决多目标优化种群分组问题;提出了一种新的交叉变异方式进行局部搜索以提高收敛性能;利用拥挤距离机制评价非劣解集以及有效地维护外部档案容量。采用田口实验和统计分析方法确定了算法最佳参数组合,将改进前后的混合蛙跳算法及NSGA-Ⅱ对测试算例的求解结果进行了多指标对比分析,研究结果表明：改进混合蛙跳算法具有良好的综合求解优势。最后,将所提算法应用到某电冰箱的资源约束拆卸线平衡问题中,为决策者提供了较优的拆卸方案。     

A hierarchical approach to disassembly sequence planning for mechanical product

An important aspect of design for the life cycle is assessing the disassemblability of products. This paper presents a novel approach to automatic generation of disassembly sequence from hierarchical attributed liaison graph (HALG) representation of an assembly through recursively decomposing the assembly into subassemblies. In order to increase the planning efficiency, the HALG is built according to the knowledge in engineering, design and demanufacturing domains. In this method, the boundary representation (B-Rep) models are simplified by removing the hidden surfaces to reduce the computational complexity of disassembly planning. For each layer of HALG, the subassembly selection indices defined in terms of mobility, stability, and parallelism are proposed to evaluate the extracted tentative subassemblies and select the preferred subassemblies. To verify the validity and efficiency of the approach, a variety of assemblies including some complicated products are tested, and the corresponding results are presented.     

基于层次概率模糊认知图的产品拆卸序列研究

根据拆卸序列研究的现状,提出了层次概率模糊认知图的数学模型,并用基于层次概率模糊认知图的方法来产生全拆卸中的拆卸序列。该方法能有效地解决其他方法较难解决的从产品装配图中提取拆卸分析所需的某些信息,如拆卸稳定性和拆卸过程中的干涉判断,以及当产品的零件数目过多或零部件之间的配合关系复杂时拆卸模型表达等问题。最后以飞轮组件为例,结合三维软件UG用VC++6.0二次开发,从产品三维模型中提取零件间的配合关系等相关信息,提供给专家建立层次概率模糊认知图时参考,从而得到了拆卸序列及动态实现了拆卸的过程,并可以检查拆卸干涉问题、拆卸稳定性以及拆卸序列的正确性,从而表明该方法具有简单、鲁棒性和实用等特点。     

面向机械零件设计的可选择性拆卸策略研究

机械零件可选择性拆卸规划能够实现从一个装配体上拆除一个或几个零件,这对于产品维修、报废处理和回收利用是非常有用的。尽管完全拆卸规划算法也能得到可行的拆卸序列,但由于完全拆卸规划方法需要将装配体的所有零件都进行拆卸,通过适当的分析和评价也能找到所需的可行拆卸序列,但这是非常困难的,特别当零件数量较多时,通常得不到最优解。本文在建立广义的机械零件CAD模型的基础上,定义了装配体中的拆卸波是装配体中某些零件的一种拓扑关系,可反映零件之间的拆卸顺序。并在此基础上提出了一种基于拆卸波的零件可选择性拆卸规划算法。该算法的计算量小,容易用它来进行高效的搜索,获得最优的零件可选择性拆卸序列。     

基于双层配置体系的产品配置算法研究

在大规模定制生产模式下,产品配置遇到了复杂模糊配置数据的处理问题,为此,提出了基于实例重用的产品配置模糊求解技术,设计了基于多目标遗传算法的产品配置优化算法.将产品配置过程划分为部件配置与零件配置两部分,利用典型条件概率解决产品配置领域的部件模糊配置问题,设计了基于非支配排序遗传算法-Ⅱ,求解以成本、时间和库存为优化目标的零件配置,并结合两者建立完整的产品配置求解算法体系.该算法有效地解决了复杂产品配置中模糊数据处理及配置组合爆炸的问题.     

基于遗传与模拟退火算法的分批作业调度

建立了作业车间分批调度问题的数学模型,然后简要介绍了解决分批调度问题的一类方法:2阶段算法,并详细研究了2阶段之间的集成策略.最后,根据一些实验的计算结果,分析了影响分批问题的几个因素.     

Research on the selectable disassembly strategy of mechanical parts based on the generalized CAD model

Disassembly has gained much attention due to the growing concerns of material resources, energy conservation, and environmental conscious. The process of disassembly attempts to breakup a product into several pieces, with the expectation that the pieces together have a net value greater than the discarded product. In analyzing these aspects, one needs to determine all feasible ways to disassemble a product. This is especially true for the complex products like automobile, aircraft, etc. Most researchers took the algorithm of complete-disassembly planning to design for disassembly, although the feasible disassembly sequence can be obtained, parts of a assembled entity should be removed totally. And the required S (refers to disassembly sequence) could be gained through the proper analysis and evaluation; however, there are so many difficulties in this process and it is hard to obtain the optimum solution, especially when there are many parts in assembled body (such as automotive product). Therefore, it is very necessary to research the selectable disassembly algorithm of mechanical parts. This paper puts forward an algorithm on the selectable disassembly strategy of mechanical parts based on disassembly wave concept. It is comparatively easy and efficient to search, and get the optimal selectable disassembly sequence of mechanical parts with less amount of computing. It is so significant for all the steps of maintaining, recycling and discarding. This project is sponsored by National Natural Science Fund Project (5975042) and Shanghai Natural Science Fund Project (02ZF14024).     

面向再制造的基于Pro/Toolkit的拆卸仿真

产品拆卸是再制造工程中重要的环节,拆卸的好坏直接影响产品再制造性。通过研究Pro/Toolkit二次开发的方法,得到拆卸仿真系统开发的流程:建立产品拆卸模型,提取模型拆卸信息,规划产品拆卸序列,进行产品再制造拆卸评估。研究了层次概率模糊认知图的数学模型规化拆卸序列的方法,得出了实现变速箱目标拆卸的最优拆卸序列。分析了拆卸仿真系统的再制造拆卸评估准则,给出了从零件的拆卸时间、拆卸费用、环境性影响、经济性、可拆卸度性、再制造性、回收价值性、零件耐用性、再制造工艺性、技术稳定性等方面进行产品再制造评估的改进算法。最后使用MFC对话框设计技术设计再制造拆卸仿真系统的对话框,得到面向再制造的拆卸仿真系统。     

Multiple cycle economic lot and delivery-scheduling problem in a two-echelon supply chain

This paper addresses the problem of lot sizing, scheduling, and delivery of several items in a two-echelon supply chain over a finite planning horizon. Single supplier produces the items through a flexible flow line and delivers them directly to an assembly facility where the transfer of sub-lots between adjacent stages of supplier’s production system (i.e., lot streaming) is allowed in order to decrease the manufacturing lead time. At first, a mixed zero-one nonlinear programming model is developed based on the so-called basic period (BP) approach aiming to minimize the average setup, inventory holding, and delivery costs per unit time in the supply chain without any stock-out. The problem is very complex and cannot be solved to optimality especially for real-sized problems. Therefore, two efficient hybrid genetic algorithms (HGA) are proposed based on the power-of-two (PTHGA) and non-power-of-two (NPTHGA) variants of BP approach. The solution qualities of the proposed algorithms are compared with a proposed lower bound. Numerical experiments demonstrate that the NPTHGA outperforms the PTHGA algorithm with respect to the solution quality, but the PTHGA outperforms the NPTHGA with respect to the computation time.