第Ⅱ类拆卸线平衡问题建模及优化

环境资源问题日益凸显、人们环保意识的增强以及经济利益的驱动,促使社会进行废旧产品回收再利用,以发展绿色经济。拆卸作为回收再利用过程中的核心环节,采用流水线的方式组织产品精细化拆卸,可实现资源最大化价值。然而,零部件作业任务在流水线上分配排序过程复杂,各工作站上的作业负荷需保持相对均衡,才能保证拆卸线高效运行。为此,本文在拆卸线工作站数量固定的情况下,以最短节拍时间和均衡任务在工作站上的分配为目标,建立第Ⅱ类拆卸线平衡问题优化模型,并提出一种并行动态邻域深度搜索算法进行求解。所提算法采用动态搜索机制实现解在各邻域结构集内的并行深度搜索;通过设置扰动阈值对解实施干扰,以加快跳出局部最优;在节拍时间调整过程中,采用基于二分法的定界策略,以实现向最优节拍时间的快速靠拢。最后,通过不同规模算例验证了模型的有效性以及算法的高效性。     

A general approach to disassembly planning and control

Closing product and material cycles have emerged as a paradigm for industry in the 21st century, and are directed towards an economical and responsible use of our limited resources. Disassembly plays a key role in recycling. It enables the recovery of functioning components for re-use of pure materials for re-utilization and the separation of harmful substances. Major challenges are presented by the enormous variety of products to be disassembled, by uncertainty as to quantity and by their unknown properties. An appropriate planning and control of disassembly processes is essential for an economic disassembly. Based on an investigation of the suitability of common production planning and control (PPC) methods for disassembly, a specific disassembly planning and control concept (DPC) is outlined.     

作业时间依赖顺序的拆卸线平衡多目标优化

在废旧产品拆卸时无先后关系约束的任务之间可能存在拆卸干扰，使任务作业时间依赖于拆卸顺序，导致产品总作业时间不确定，从而影响拆卸线的平衡。为此，考虑拆卸过程中任务间的相互干扰，以最小化拆卸成本和环境危害为目标，构建作业时间依赖顺序的拆卸线平衡多目标优化模型，并提出一种Pareto人工蜂群算法进行求解，采用混合生成法保证种群质量与多样性，设计变邻域深度搜索策略以提高雇佣蜂局部开采效率，为降低侦察蜂探测的盲目性采用基于外部存档的单点变异算子进行搜索。最后通过算例与实例验证算法的有效性以及平衡任务在拆卸线上分配的重要性。     

Pricing decisions in a multi-criteria setting for product recovery facilities

Unpredictability in the arrival time and quantity of discarded products at product recovery facilities (PRFs) and varying demand for recovered components contribute to the volatility in their inventory levels. Achieving profit under such capricious inventory levels and stringent environmental legislations remains a challenge to many PRFs. This paper presents a multi-criteria decision model to determine a pricing policy that can simultaneously address two issues: stabilize inventory fluctuations and boost profits. The model considers that PRFs passively accepts discarded products as well as acquires them proactively if necessary. Under a multi-criteria setting, the current work determines prices of reusable and recyclable components to maximize revenue and minimize product recovery costs. A genetic algorithm is employed to solve the multi-criteria decision making problem. Sensitivity analysis is performed to investigate the effect of sorting yield, disassembly yield, and reusable component yield on the profits, prices, inventory levels, and disposal quantities.     

Disassembly of complex product structures with parts and materials commonality

This paper addresses the issue of parts and materials commonality when scheduling disassembly. In a disassembly environment, inventory management is complex due to the presence of multiple demand sources at the component level of the product structure. Commonality introduces a new layer of complexity by creating alternative procurement sources for the common component items. A novel scheduling algorithm is presented, followed by an example.     

MIXED ASSEMBLY AND DISASSEMBLY OPERATIONS FOR REMANUFACTURING

In this paper we consider the problem of designing a mixed assembly‐disassembly line for remanufacturing. That is, parts from the disassembly and repair of used products can be used to build “new” products. This is a problem common to many OEM remanufacturers, such as Xerox or Kodak. We study two main configurations, under the assumption that the disassembly sequence is exactly the reverse of the assembly sequence. Under a parallel configuration, there exist two separate dedicated lines, one for assembly and one for disassembly, which are decoupled by buffers—from both disassembly operations, which have preference, as well as parts from an outside, perfectly reliable supplier. Under a mixed configuration, the same station is used for both disassembly and assembly of a specific part. The problem is studied using GI/G/c networks, as well as simulation. Due to a loss of pooling, we conclude that the parallel configuration outperforms the mixed line only when the variability of both arrivals and processing time are significantly higher for disassembly and remanufacturing than for assembly. Via a simulation, we explore the impact of having advanced yield information for the remanufacturing parts. We find that advanced yield information generally improves flow times; however, there are some instances where it lengthens flow times.     

装配式产品拆卸的随机网络模型研究

针对废弃回收产品拆卸不确定的特点,打破了确定性拆卸的假设,提出了用随机网络模型的方法研究装配式产品的拆卸过程,分为中结点和叶结点2种拆卸情况,并用解析法求出了每道拆卸环节的零部件可得率和平均拆卸时间。最后,以手机产品的拆卸为例,绘制了拆卸树,给出了包含手机产品全部零部件的所有拆卸可能性的随机拆卸网络图。     

废旧产品回收再制造计划模式研究述评

在分析再制造基本概念及流程的基础上,从废旧产品回收计划、回收产品拆卸计划、再制造生产计划等三个方面对国内外再制造计划模式的研究现状进行综述。指出目前研究中对再制造过程的不确定性特征认识不足、对产品回收后的状态不能有效预测、对不确定情形的产品拆卸计划研究较少、对于再制造生产计划与控制缺乏系统研究。针对上述不足,提出了进一步研究的方向。     

企业战略行动成败与行动进程安排之间的关系——合法性视角下的多案例研究

从合法性的视角出发,基于对7个案例的分析,考察了企业战略行动成败与行动进程安排之间的关系.研究发现: ①在初始合法性不完备,或者因政策变化导致战略行动面临合法性危机时,战略行动进程安排对战略行动成败有至关重要的影响;②战略行动的横向拆分和纵向拆分都有可能降低合法性要求,甚至改变合法性要求的性质,但是2种拆分方式应对合法性危机的效果是不同的;③对民营企业来说,为了应对合法性风险,应尽量选择横向拆分;④对国有企业来说,尽管横向拆分能够降低风险,但是却可能使项目萎缩,不能达到战略目标,而纵向拆分更有可能与政府利益进行捆绑,可以调动更多的力量支持自己的行为.     

Disassembly to order system under uncertainty

This paper presents a multi-criteria optimization model of a disassembly-to-order (DTO) system under uncertainty. The goal of the proposed model is to determine the best combination of the number of each product type to be taken back from the last user and/or collectors. The EOL products are then disassembled for the retrieval of reusable components and materials and resold in order to meet a certain level of demand under a variety of physical, financial and environmental constraints. The surplus components are recycled, stored for usage in subsequent periods or properly disposed. The problem is modeled as a multi-criteria decision-making problem under uncertainty, where the aspiration levels for various goals are more likely to be in the “approximately more (less) than” and/or “more (less) is better” form. We employ fuzzy goal programming technique to solve the problem. When solved, the model provides the number of EOL products to be taken back as well as the number of items reused, recycled, stored and disposed. The values of a host of other performance measures are also obtained, including total profit, materials and items sales revenues, take back cost, transportation costs as well as costs of preparation of EOL products, destructive disassembly, non-destructive disassembly, recycling, storage and disposal. A case example is presented to illustrate the model's implementation.     

Remanufacturing of mobile phones—capacity, program and facility adaptation planning

Successful remanufacturing of mobile phones must meet the challenges of continuously falling prices for new phone models, short life cycles, disassembly of unfriendly designs and prohibiting transport, labor and machining costs in high-wage countries. A generic remanufacturing plan for mobile phones is developed. For the planning of remanufacturing capacities and production programs, a linear optimization model is introduced. To support the planner in the periodic adaptation of an existing remanufacturing facility under quickly changing product, process, and market constraints, discrete-event simulation is applied. Uncertainties regarding quantity and conditions of mobile phones, reliability of capacities, processing times, and demand are considered. The simulation model is generated by an algorithm using results from the linear optimization approach.     

Performance of Selected Part-Machine Grouping Techniques for Data Sets of Wide Ranging Sizes and Imperfection

This study addresses the part-machine grouping problem in group technology, and evaluates die performance of several cell formation methods for a wide range of data set sizes. Algorithms belonging to four classes are evaluated: (1) array-based methods: bond energy algorithm (BEA), direct clustering analysis (DCA) and improved rank order clustering algorithm (ROC2); (2) non-hierarchical clustering method: ZODIAC; (3) augmented machine matrix methods: augmented p-median method (APM) and augmented linear clustering algorithm (ALC); and (4) neural network algorithms: ART1 and variants: ART1/KS, ART1/KSC, and Fuzzy ART. The experimental design is based on a mixture-model approach, utilizing replicated clustering. The performance measures include Rand Index and bond energy recovery ratio, as well as computational requirements for various algorithms. Experimental factors include problem size, degree of data imperfection, and algorithm tested. The results show that, among the algorithms applicable for large, industry-size data sets, ALC and neural networks are superior to ZODIAC, which in turn is generally superior to array-based methods of ROC2 and DCA.     

Exact methods in optimum disassembly sequence search for problems subject to sequence dependent costs

Disassembling complex products is formally approached via network representation and subsequent mathematical modeling, aimed at selecting a good or optimum sequence of disassembly operations. This is done via heuristics, metaheuristics or mathematical programming. In contrast with heuristics and metaheuristics, which select a near-optimum solution, mathematical programming guarantees the selection of the global optimum. This problem is relatively simple if the disassembly costs can be assumed sequence independent. In practice, however, sequence dependent disassembly costs are frequently encountered, which causes NP-completeness of the problem. Although methods, e.g., based on the two-commodity network flow approach, are available to solve this constrained asymmetric Traveling Salesperson problem rigorously, this requires the introduction of integer variables. In this paper, a modification of the two-commodity network flow approach is proposed, which reduces the number of integer variables. This is applied to product structures that can be represented by a disassembly precedence graph. It is demonstrated that use of integer variables is completely avoided by iteratively solving a binary integer linear programming problem. This appears to be more efficient than solving the corresponding integer linear programming problem. It is demonstrated, on the basis of some cases, that this method might provide the exact solution of problems with increased complexity compared to those discussed so far in the literature. This appears particularly useful for evaluating heuristic and metaheuristic approaches.     

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

How Does Product Recovery Affect Quality Choice?

We study the impact of product recovery on a firm's product quality choice, where quality is defined as an observable performance measure that increases a consumer's valuation for the product. We consider three general forms of product recovery: (i) when product recovery reuses (after reprocessing) quality inducing components or material (e.g., remanufacturing), (ii) when product recovery does not reuse quality inducing components or material but it is overall profitable (e.g., cell phone recycling), and (iii) when product recovery is costly (but mandated by legislation, e.g., recycling of small appliances in the European Union). Using a stylized economic model, we show that the form of product recovery, recovery cost structure, and the presence of product take‐back legislation play an important role in quality choice. Generally speaking, product recovery increases the firm's quality choice, except for some instances of recovery form (ii). In addition, we find that product take‐back legislation can lead to higher quality choice as opposed to voluntary take‐back. We further demonstrate that both the firm and the consumers benefit from recovery form (ii), while both are worse off with recovery form (iii). However, environmental implications of the three recovery modes differ from their impact on consumer surplus and firm profit. While recovery forms (i) and (iii) reduce consumption and increase environmental benefits, the same is not true with recovery form (ii), which can increase consumption, potentially resulting in higher environmental impact.     

Exact and approximation algorithms for?the?complementary maximal strip recovery problem

Given two genomic maps G ₁ and G ₂ each represented as a sequence of n gene markers, the maximal strip recovery (MSR) problem is to retain the maximum number of markers in both G ₁ and G ₂ such that the resultant subsequences, denoted as $G_{1}^{*}$ and $G_{2}^{*}$ , can be partitioned into the same set of maximal substrings of length greater than or equal to two. Such substrings can occur in the reversal and negated form. The complementary maximal strip recovery (CMSR) problem is to delete the minimum number of markers from both G ₁ and G ₂ for the same purpose, with its optimization goal exactly complementary to maximizing the total number of gene markers retained in the final maximal substrings. Both MSR and CMSR have been shown NP-hard and APX-hard. A?4-approximation algorithm is known for the MSR problem, but no constant ratio approximation algorithm for CMSR. In this paper, we present an O(3 ^k n ²)-time fixed-parameter tractable (FPT) algorithm, where k is the size of the optimal solution, and a 3-approximation algorithm for the CMSR problem.     

How Sourcing of Interdependent Components Affects Quality in Automotive Supply Chains

In the automotive industry, many firms source key components from different suppliers, even though the components may function interdependently. In this study, we investigate how component level interdependence impacts quality performance and analyze how various operational factors moderate this relation. We synthesize information from several case studies to model the quality challenges faced by an automotive firm. For several sub‐assemblies that go into its products, the firm sourced key components from two different suppliers. The sub‐assemblies would fail whenever a component fails, but due to interdependent operations, failure of one component could cause the failure of the other. The firm found it challenging to improve the suppliers' quality performance as it was difficult to trace the failures to specific components. Our analysis reveals that – (i) the impact of interdependence is governed by the supply chain structure: reducing the interdependence between components improves quality when suppliers provide the components, but reducing interdependence worsens quality when the firm manufactures the entire sub‐assembly; and (ii) the relation between interdependence and quality performance is moderated by factors such as penalties, production costs, and interdependence costs. Additionally, we find that quality performance is lower when the firm outsources the components than when the firm manufactures the entire sub‐assembly. We identify coordinating mechanisms that leverage incentives and penalties to bridge the quality performance gap.     

Production planning and control for remanufacturing: literature review and analysis

This article reviews the literature on production planning and control (PPC) for remanufacturing. The objectives are threefold: to determine whether the gaps identified by Guide (Guide Jr, V.D.R., 2000. Production planning and control for remanufacturing: industry practice and research needs. Journal of Operations Management, 18, 467–483) on the subject with respect to the main complicating characteristics of remanufacturing have been fulfilled; to classify the literature based on an appropriate structure to obtain a better understanding of the subject identifying sources of future research and to provide a useful source for scholars and practitioners. Seventy-six papers were examined and classified. It was found that none of the studies deals simultaneously with all of the complicating characteristics, and that more practical research is needed. In addition it was found that we lack studies regarding forecasting and aggregate planning considering disassembly of returned products, material matching restrictions or stochastic routings, master production scheduling considering material matching restrictions or stochastic routings, ordering system and capacity planning considering material matching restrictions and inventory control and management considering stochastic routings.     

运力受扰的多车场车辆调度干扰管理问题研究

为解决物流配送系统中因运输车辆毁坏而产生的干扰问题,基于干扰管理思想提出了解决问题的扰动恢复策略与实施方案。在扰动度量的基础上,设计了多车场车辆调度扰动恢复策略,建立相应的干扰管理模型。针对多车场车辆调度干扰管理问题的特有属性,设计了一系列求解简化策略,有效简化了问题的求解空间。结合干扰管理模型的特点,使用改进的遗传算法进行求解。最后给出了一个算例,其结果证明了干扰管理模型与算法的有效性。