A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning

Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better productdesign. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology.     

Eco-architecture analysis for end-of-life decision making

In order to improve ease of disassembly and recycling of a product at its retirement stage, it is essential to design a product architecture that allows for easy disassembly and recycling. In this paper, a novel concept of eco-architecture is introduced, and the eco-architecture analysis, a design approach supporting the end-of-life decision making process, is proposed. The eco-architecture is the product architecture described from the end-of-life (EOL) viewpoint, in which a product is represented as an assembly of end-of-life modules. Not only does it prescribe an EOL strategy, but it also gives information about the connections and arrangement among EOL modules. Therefore, understanding the eco-architecture is helpful in enhancing the disassembly and recycling capabilities of an architecture design. The proposed eco-architecture analysis supports architecture improvement; it helps designers to derive the most desirable eco-architecture which entails the optimal end-of-life strategy. It also facilitates the extraction of meaningful redesign guidelines which make it possible to improve an architecture in an efficient and effective manner.     

An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products

There is a strong need for recovery decision-making for end-of-life (EOL) products to satisfy sustainable manufacturing requirements. This paper develops and tests a profit maximisation model by simultaneously integrating recovery option selection and disassembly planning. The proposed model considers the quality of EOL components. This paper utilises an integrated method of multi-target reverse recursion and partial topological sorting to generate a feasible EOL solution that also reduces the complexity of genetic constraints handling. In order to determine recovery options, disassembly level and disassembly sequence simultaneously, this paper develops an improved co-evolutionary algorithm (ICA) to search for an optimal EOL solution. The proposed algorithm adopts the evolutionary mechanism of localised interaction and endosymbiotic competition. Further, an advanced local search operator is introduced to improve convergence performance, and a global disturbance strategy is also suggested to prevent premature convergence. Finally, this paper conducts a series of computational experiments under various scenarios to validate the meta-heuristic integrated decision-making model proposed and the superiority of the developed ICA. The results show that the proposed approach offers a strong and flexible decision support tool for intelligent recovery management in a ubiquitous information environment. We discuss the theoretical and practical contributions of this paper and implications for future research.     

Disassembly process planning algorithms for end-of-life product recovery and environmentally conscious disposal

Disassembly process planning is an act of preparing detailed operation instructions for disassembling used or an end-of-life (EOL) product to recover or dispose of its constituent parts or subassemblies. The main decisions are: (a) disassembly level; (b) disassembly sequence; and (c) EOL options such as reuse, remanufacturing, recycling, incineration, landfill, etc. This study deals with the three decision variables simultaneously in the parallel disassembly environment for the objective of maximising the profit. Unlike previous studies, we consider practical constraints, i.e., reuse probability and environmental impacts of parts or subassemblies, sequence-dependent setup costs, regulation on recovery rate, and incineration capacity. To represent and solve the problem, we develop an extended AND/OR graph, and then suggest a two-phase algorithm. To show the effectiveness of the proposed algorithm, two case studies have been carried out on an automatic pencil and a telephone. Also, test results on other general product structures are reported.     

Automatic derivation of transition matrix for end-of-life decision making

Recently strengthened environmental regulations have obligated manufacturing companies to treat end-of-life (EOL) products both environmentally consciously and economically. EOL treatment begins with disassembling a product into recyclable or disposable sub-assemblies. Therefore, the economic value of an EOL product is largely a function of the plan for its disassembly: the means by which it is to be disassembled into smaller sub-assemblies, and the choice of sub-assemblies to be disassembled first. In order to make these decisions, a disassembly structure describing every possible sub-assembly division and its disassembly path from the original product has to be presented in a typical form. A widely used form of such a structure is a transition matrix. A transition matrix shows all feasible sub-assemblies and their disassembly hierarchy. Whereas it can be easily transformed into mathematical disassembly planning problem, the tedious work required for its generation limits its practical use. In this paper, we propose an algorithm for automatic derivation of a transition matrix. The proposed algorithm provides an efficient way to derive a transition matrix based on a product's architectural information, which includes the product's physical connections and the relative geometric locations between individual parts. The algorithm was validated in deriving a transition matrix of a car door-trim. Our algorithm can significantly expand the applicability of transition-matrix-based disassembly planning research.     

Multicriteria decision-aid approach for product end-of-life alternative selection

The selection of the best compromise alternative for treating a product at its end of life (EOL) is presented. Each EOL alternative has its own consequences from an economical, environmental and social point of view. The criteria used to determine these consequences are often contradictory and not equally important. In the presence of multiple conflicting criteria, an optimal EOL alternative rarely exists. Hence, the decision-maker should seek the best compromise EOL alternative. The present paper proposes a multicriteria decision-aid (MCDA) approach to aid the decision-maker in selecting the best compromise EOL alternative on the basis of his/her preferences and the performances of EOL alternatives with respect to the relevant environmental, social and economic criteria. This approach is important because it allows the user to consider various conflicting criteria simultaneously and it takes into account his/her preferences. The paper analyses the most important aspects of this approach such as the constitution of a set of EOL alternatives, the selection of a list of relevant criteria to evaluate the EOL alternatives and the choice of an appropriate multicriteria decision-aid method. A case study is provided to illustrate how the proposed approach can be used for product EOL alternative selection in real-world applications.     

An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times

The purpose of this work is to efficiently design disassembly lines taking into account the uncertainty of task processing times. The main contribution of the paper is the development of a decision tool that allows decision-makers to choose the best disassembly alternative (process), for an End of Life product (EOL), and assign the corresponding disassembly tasks to the workstations of the line under precedence and cycle time constraints. Task times are assumed to be random variables with known normal probability distributions. The case of presence of hazardous parts is studied and cycle time constraints are to be jointly satisfied with at least a certain probability level, or service level, fixed by the decision-maker. An AND/OR graph is used to model the precedence relationships among tasks. The objective is to minimise the line cost composed of the workstation operation costs and additional costs of workstations handling hazardous parts of the EOL product. To deal with task time uncertainties, lower and upper-bounding schemes using second-order cone programming and approximations with convex piecewise linear functions are developed. The applicability of the proposed solution approach is shown by solving to optimality a set of disassembly problem instances (EOL industrial products) from the literature.     

An integrated product service system modelling methodology with a case study of clothing industry

Product-service system (PSS) is a business model designed to help enterprises achieve servitisation. PSS not only considers service-oriented logic to increase product value but also supports enterprises with waste reduction. An increasing number of studies have emphasised PSS development. However, much of the literature remains focused on separate development phases, such as idea generation or a specific product or industry application. An integrative system modelling methodology designed to help companies formulate a complete scenario is absent. This study provides a methodology for developing a systematic and complete PSS strategy. It combines failure modes and effects and importance-performance analysis to identify potential customer needs. A service-product development matrix and a product-service life cycle analysis are developed. Following the service concepts generation, grey relational analysis and scenario analysis are utilised to evaluate the alternatives. A case study involving the clothing industry was conducted to test the proposed approach. Results indicate the methodology can help service providers identify existing problems and develop service alternatives systematically. These alternatives can then be evaluated and the scenario which achieves the highest profitability, is the most environmental friendly, and is the most social friendly can be identified.     

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.     

A comprehensive end-of-life strategy decision making approach to handle uncertainty in the product design stage

This study employs fuzzy logic to evaluate uncertain component end-of-life (EOL) options in the design stage. Determining EOL strategies during the product design stage can be complex. For example, EOL strategies for retired bicycle components are various and may change with geographic location. Thus, adopting fixed EOL strategies in the product design stage may not always be appropriate; the element of uncertainty should be considered. Limited research has examined uncertainty of EOL strategies during the design stage. Moreover, the evaluation of EOL strategies in a comprehensive manner has not been shown in a realistic case study. These facts motivate this investigation. Fourteen evaluation criteria are used to generate a comprehensive framework for assessing seven EOL strategies. The evaluation process generates the likelihood for each of these strategies by aggregating fuzzy set operations and a left–right fuzzy ranking method. Using SUMPRODUCT calculation for these weights/probabilities and input sustainability value (i.e., cost, environmental impact and labor time), expected values are derived to represent the sustainability values for each EOL strategy. A Technique-for-Order-of-Preference-by-Similarity-to-Ideal-Solution (TOPSIS) based method is employed to identify the appropriate EOL strategy for each component/product. A refrigerator is used as a case study to illustrate the methodology. This study addresses the uncertainty involved in identifying an EOL strategy for a specific product component during the design stage through the use of fuzzy logic. The method closes a gap in the current EOL strategy assessment criteria and introduces a comprehensive evaluation framework to capture multiple strategic perspectives by incorporating 14 key evaluation criteria.     

Sustainable disassembly line balancing model based on triple bottom line

End of life (EOL) phase of a product is receiving more attention due to increase in environmental concerns, and many studies have been conducted for value creation in EOL, focusing on concepts as remanufacturing, reuse and recycling in sustainable production manner. This study especially focuses on one of global problem, e-waste. To minimise the amount of wastes and maximise recovered materials from EOL, disassembly is one of the most important concept, associated with reuse, and balancing disassembly line in an optimal way is essential for organisations. In disassembly line balancing (DLB), not only precedence of tasks, but also risk criteria related to environment and human safety should be considered for sustainability. The aim of this study is to propose a model based on triple bottom line (TBL) dimensions, i.e. human safety, environmental safety and business criteria. To achieve sustainability in DLB, and for risk assessment in sustainable DLB, it had been decided to use a multi-criteria method, i.e. TODIM, acronym in Portuguese of ‘Tomada de Decisão Iterativa Multicritério’. The proposed model included 22 disassembly criteria categorised under TBL dimensions, which are derived from the literature. Implementation of the study was conducted for computer disassembly processes, and as a result of the study approximately 12% an improvement in cycle time was succeeded. In the long run, the integration of sustainability in disassembly operations may contribute to the competitive advantage of the company in terms of differentiation and corporate image by achieving business, environment and human targets simultaneously.     

Determining end-of-life policy for recoverable products

We address a problem that arises for an original equipment manufacturer (OEM) who produces a product both in new and remanufactured forms. A remanufactured product is produced using the parts harvested from recovered products (cores) upon their disassembly, and it may contain some new parts while the excess good parts from cores are salvaged for profit. Other options are available to the OEM for handling cores that do not require disassembly. It follows that the per-unit remanufacturing cost is not constant and it may change depending on the number of recovered cores, good-part reclamation yields, and sales of remanufactured products. We present analytical results for determining an optimal solution with regard to: (i) quantity of cores to collect, (ii) end-of-life (EOL) options for the cores and (iii) product pricing of new and remanufactured products. Our analysis reveals existence of a ‘limiting part’ that dictates the number of cores to collect and a ‘key part’ that determines the number of remanufactured products to make as well as the fact that the availability of cores does not impact the EOL policy type for a product. Our analysis also enables mapping of product characteristics onto corresponding EOL policy types.     

An algorithm for optimising the servicing of products with constrained,multiple defects

The twin pillars of sustainable development are the conservation of natural resources and the management of waste. Waste is generated whenever a product is serviced or repaired, or when it is ultimately discarded at the end of its useful life. In order to manage such waste, the servicing options and costs must first be ascertained. This paper presents an algorithm for the generation of optimal disassembly and re-assembly sequences for the servicing of products with multiple defects, subject to constraints such as inaccessible components. The multiple service action (MSA) algorithm determines the minimum total servicing cost for a product network based on Floyd's Algorithm, a shortest path algorithm. Well-established shortest path algorithms, which compute the shortest route between any pair of nodes in a network, are unable to handle multiple defects. The product network is first constructed, depicting the components and subassemblies as nodes, and embodying in directed arcs, the labour, materials and tooling costs associated with disassembly and re-assembly, as well as the cost to repair, reuse, recycle or dispose the defective components. The MSA algorithm was tested on seven different product networks representing multiple defective components that can be serviced by different feasible routes. For each feasible service route, associated costs were computed. It was established that the algorithm was able to generate optimum disassembly and re-assembly routes for the servicing of products with multiple defects subject to constraints.     

Mathematical model and solution algorithms for selective disassembly sequencing with multiple target components and sequence-dependent setups

This study considers selective disassembly sequencing under the sequential disassembly environment in which one component is obtained at each disassembly operation. The problem is to determine the sequence of disassembly operations to obtain multiple target components of a used or end-of-life product for the purpose of repair, reuse, remanufacturing, disposal, etc. In particular, we consider sequence-dependent setups in which setup costs depend on the disassembly operation just completed and on the operation to be processed. The problem is represented as a disassembly precedence graph and then a new integer programming model is suggested for the objective of minimising the total disassembly cost. After it is proved that the problem is NP-hard, we suggest two types of heuristics: (1) branch and fathoming algorithm for small-to-medium-sized instances; and (2) priority-rule-based algorithm for large-sized instances. A series of computational experiments, i.e., effectiveness of the new integer programming model and performances of the two heuristic types, were done on various test instances, and the results are reported. In addition, to show the applicability of the mathematical model and the solution algorithms, a case study is reported on an end-of-life electronic calculator.     

A two-stage robust programming approach to demand-driven disassembly planning for a closed-loop supply chain system

The closed-loop supply chain system, which integrates forward and reverse logistics, is a desirable policy for retaining recoverable resources and extending the life cycles of products. In this study, we propose a methodology to contend with a demand-driven disassembly planning problem under a closed-loop supply chain system. A two-stage robust programming model is developed correspondingly, such that multiple products with a hierarchical product's structure are disassembled to satisfy uncertain demands in multiple periods. The objective of the model is to determine a robust decision for recycle volume and timing of each type of end-of-life (EOL) product, as well as recovery strategies. The results provide two-stage decisions by considering future scenarios of periodic demands at the beginning of a planning horizon. The first-stage decision is to determine a compromise solution that is close to the optimal solution for every scenario while retaining a certain level of infeasibility of constraints, such as unsatisfied demand. Afterward, when the outcome of a scenario has been realised, the second-stage decision, such as, inventory volume, is conducted to become a buffer for mitigating uncertain impacts. Furthermore, the computational results confirm the trade-off relationship between solution robustness and model robustness, which are core results of the robust model apart from expected profit. The different types of decision makers’ preferences toward risk can be accounted for to determine a compromise robust solution.     

Improving cost effectiveness in an existing product line using component product platforms1

Previously, we introduced a new method for improving commonality in a highly customised, low volume product line using component product platforms. The method provides a bottom-up platform approach to redesign family members originally developed one-at-a-time to meet specific customer requirements. In this paper, we extend the method with an activity-based costing (ABC) model to specifically capture the manufacturing costs in the product line, including the cost associated with implementing a platform strategy. The valve yoke example is revisited in this paper, the customised ABC model is defined, two design strategy alternatives are addressed, and the new method is used to determine which alternative is better at resolving the trade-off between commonality, total cost, and product performance. The proposed method shows promise for creating a product platform portfolio from a set of candidate component platforms that is most cost-effective within an existing product line. The proposed method allows for arbitrary leveraging as it does not rely solely on the traditional vertical, horizontal, or beachhead strategies advocated for the market segmentation grid, and this is especially beneficial when applied to an existing product line that was developed one-at-a-time time such that artefact designs are inconsistent from one to another.     

产品物料清单BOM的XML实现方法研究

针对产品物料清单(BOM)的复杂性、多样性、灵活性等特点,结合XML所具有的特点,提出了基于XML的BOM树的建立方法,由此有效地实现了数据的查询、修改和删除等操作.论文分析了产品全生命周期各阶段BOM数据的特点、演变过程;详细阐述了产品BOM结构的XML的建立优势;产品BOM树的XML模式的设计和实现;以及各种BOM视图之间的转化和BOM数据的交换.重点介绍了.Net Framework环境下BOM树的实现机制.     

基于分类和模块化的工程机械BOM构建

分析了工程机械BOM构建现状和不足,给出一种适合工程机械基于分类和模块化的超级BOM构建方案.从工程机械产品销售实际,对产品的主参数、特征参数、可选参数概念作出说明.将属于某特定型号工程机械产品的所有部件划分为公共部分、特征部分、可选部分,并建立相应部分虚拟层级.根据模块化思想,对特征部分和可选部分建立具有约束关系的选择模块.将物料分类原理应用于模块化超级BOM构建,运用Excel和VBA实现了产品编码自动生成和BOM自动构建功能.分析该方案特点并应用于工程机械产品BOM构建,结果表明,基于分类的模块化BOM构建方法简单易行、合理有效,能够快速准确构建单一产品BOM,对于工程机械信息化具有较好的参考价值.     

A new distribution-free model for disassembly line balancing problem with stochastic task processing times

Effective conduct with End of Life (EOL) products is a hot research topic in green and smart manufacturing. For EOL product recycling and remanufacturing, a fundamental problem is to design an efficient disassembly line under consideration of stochastic task processing times. This problem focuses on selecting alternative task processes, determining the number of opened workstations, and assigning operational tasks to the workstations. The goal is to minimise the total cost consisting of workstation operational cost and hazardous component processing cost. Most existing works assume that the probability distribution of task processing times can be estimated, however, it is often not likely to access the complete probability distribution due to various difficulties. Therefore, this study investigates disassembly line design with the assumption that only the mean, standard deviation and an upper bound of task processing times are known. Our main contributions include: (i) a new decomposition color graph is proposed to intuitively describe all possible processes, (ii) a new distribution-free model is proposed, and (iii) some problem properties are established to solve the model. Experimental results show that the distribution-free model can effectively deal with stochastic task processing times without given probability distributions.     

Product end-of-life options selection: grey relational analysis approach

Management of the product end-of-life (EOL) for a manufacturing enterprise is important. An improper EOL strategy can negatively affect the productivity and profitability and undermines the reputation of an enterprise because of the growing demands for extended producer responsibility. Producers need tools and methods to evaluate each EOL option since it is a complicated but must-be-accomplished task to achieve in order to solve the multiple-criteria problem that combines aspects such as eco-system quality, environmental impacts, human health issues, and economic factors etc. This paper presents an alternative decision-making process to generate an optimal solution from a list of EOL options under the uncertainty condition of incomplete information. Using Grey Relational Analysis, the multi-criteria weighted average is proposed to rank the product EOL options with respect to several criteria at the material level. It will guide the selection process and help a decision-maker solve the selection problem. The method is demonstrated with an example. Various EOL options are evaluated using the developed multicriteria methodology that takes account the environmental, economical and social factors.