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.     

Capacitated disassembly scheduling with random demand

This paper considers disassembly scheduling, which is the problem of determining the quantity and timing of the end-of-use/life products to be disassembled while satisfying the demand for their parts obtained from disassembling the products over a planning horizon. This paper focuses on the problem with stochastic demand of parts/modules, capacity restrictions on disassembly resources, and multiple product types with a two-level product structure. The two-level product structure implies that an end-of-use/life product is hierarchically decomposed into two levels where the first level corresponds to the parts/modules and the second level corresponds to the product. We formulate the problem as a stochastic inventory model and to solve the problem we propose a Lagrangian heuristic algorithm as well as an optimisation algorithm for the sub-problems obtained from Lagrangian decomposition. The test results on randomly generated problems show that the Lagrangian heuristic algorithm demonstrates good performance in terms of solution quality and time.     

A bill of materials-based approach for end-of-life decision making in design for the environment

Re-use, recycling or remanufacturing of products and components are good alternatives for reducing the environmental problems resulting from the huge amounts of waste currently arriving at landfills. A new approach is proposed in this paper for enhancing these alternatives from the earliest stages of product design. Given the product structure (obtained from its bill of materials (BOM)) and the joining and geometrical relationships among the components (obtained from the three-dimensional, computer-aided design representation), a model is proposed that will determine the EOL (EOL) strategy, i.e. the depth of disassembly inside the structure and the final end (re-use, recycle, remanufacture or disposal) for each disassembled part leading to the highest profit. A scatter search (SS) metaheuristic is used to determine the disassembly cost at each level of the BOM. The model presents a number of major improvements with respect to previous research. It addresses the problems of simultaneously determining both the best EOL strategy and the disassembly sequence, as well as allowing removal of components not only over the two or three Cartesian axes and affording the possibility of modifying the encountered strategy in a further step so as to fulfil other business criteria (such as disassembly time, resources availability or maximum waste generation rate).     

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.     

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.     

基于模糊推理的不确定环境下拆卸工艺规划

回收产品因磨损等原因其结构和质量会发生改变,拆卸序列规划存在不确定性,拆卸前很难确定产品最优拆卸序列。首先建立一个模糊拆卸Petri网模型表示产品可行拆卸序列和拆卸过程存在的不确定信息,为降低产品质量和拆卸能力的不确定性对拆卸序列优化的影响,建立一个自适应的模糊推理系统,利用模糊推理和反馈学习的方法对产品各拆卸步骤的成本进行预测,然后通过计算不同拆卸序列下拆卸的收益来得到产品最优拆卸序列,最后通过算例证明了方法的有效性。     

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.     

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.     

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.     

Dynamic disassembly planning for remanufacturing of multiple types of products

With the increased need for remanufacturing of end-of-life products, achieving economic efficiency in remanufacturing is urgently needed. The purpose of this study was to devise a cost-minimisation plan for disassembly and remanufacturing of end-of-life products returned by consumers. A returned end-of-life product is disassembled into remanufacturable parts, which are supposed to be used for new products after being remanufactured. Each end-of-life product is disassembled into parts at variable levels and through variable sequences as needed, taking into account not only disassembly but also manufacturing, remanufacturing, and holding inventory of remanufacturable parts. This study proposes a mixed integer linear programming (MILP) model for derivation of the optimal disassembly plan for each returned product, under deterministically known demand and return flows. For the purposes of an illustrative example, the proposed model was applied to the formulation of an optimal disassembly and remanufacturing plan of ‘fuser assembly’ of laser printers. The solution reveals that variable-level disassembly of products saves a significant remanufacturing cost compared with full disassembly.     

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.     

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.     

A Petri Net based approach to determine the disassembly strategy of a product

In order to protect the environment and regain value added to products, a process known as disassembly has come into the limelight. This process is to be applied to reuse abandoned goods and materials. Manufacturers are being forced to establish disassembly plants and to develop their products' designs so as to maintain the government's dictate to dispose off their products in an environmentally responsible manner. This research presents a cost-based heuristic analysis for a circuit board assembly. Various components of the product and their assembly relationships are represented by a Petri Net diagram. Firing the transitions of the disassembly Petri Net is integrated with cost-based indices to develop an effective disassembly strategy. The methodology discussed here simplifies the decisionmaking process involved in disassembly planning. A comprehensive disassembly process planning system is proposed here and is exemplified by a case study of circuit board assembly.     

Interactive AR-assisted product disassembly sequence planning (ARDIS)

This paper presents a proof-of-concept novel near real-time interactive AR-assisted product disassembly sequence planning system (ARDIS) based on product information, such as interference matrix and 3D models. The system is developed using Unity and consists of three modules, including an intelligent disassembly sequence planning module, an automatic content authoring module and an intuitive augmented reality (AR) user interface (UI) with various features, such as a virtual panel for customisation and an option panel for sequence regeneration. Given the retrieval targets specified by a user, optimised disassembly sequences are computed using an evolutionary computing algorithm. For the sequences computed, the respective AR disassembly instruction sequences, such as 2D text instructions and animated 3D models, are generated dynamically based on a taxonomy that links each disassembly step in a sequence with the corresponding Unity templates that have been created beforehand. Hence, the need for manual authoring to provide AR disassembly guidance is reduced. If necessary, the user can request for alternative disassembly sequences which can be re-computed in near real-time. Several case studies have been carried out to demonstrate and evaluate the performance of the system within the laboratory environment.     

Analyzing the effects of inventory cost setting rules in a disassembly and recovery environment

In this study we consider a disassembly and recovery facility receiving end-of-life products and facing demand for a specific part that is disassembled from the product and then recovered. The disassembly and recovery operations can be either performed before hand, or upon customer arrival. In the latter case, a discount on the selling price is applied to compensate the customer for waiting for the completion of the disassembly and recovery operations. One of the difficulties faced in planning for such a system is the determination of the opportunity cost associated with carrying recovered parts inventory. The difficulty arises in seeking the value added to the part given the costs incurred for maintaining the product return, disassembly and recovery costs and revenue earned from the hulk, that is the remaining product after the disassembly of the part. The main objective of the study is to investigate the effect of different rules to determine this opportunity cost on the performance of the system. Six rules are considered in the study. The performance of the rules is assessed by a computational study under an approximate inventory control policy.     

Incorporating production concerns in conceptual product design

This paper presents a set of analytical tools that can be used to alter a product's design, manufacturing processes and assembly techniques to increase production rate. The analytical tools obtain these improvements by simultaneously considering each part's geometric attributes and complexity, vendor selection, material and process selection, and capacity planning at the conceptual stage of the product realization process. The method detects and then avoids heavily used resources by indicating which combination of one or more of its components' geometric attributes, manufacturing processes, material and assembly methods should be altered. The method is illustrated with the analysis of an overhead projector. It is shown that production rate can be doubled by either making small changes to a component's geometric attributes or by selecting different manufacturing processes. Neither of these changes affects the functionality of the product.     

Economical evaluation of disassembly operations for recycling,remanufacturing and reuse

This paper introduced a procedure which integrates economical factors into the scheduling of disassembly operations for Material Recovery Opportunities (MRO). MRO are defined as opportunities to reclaim post-consumer products for recycling, remanufacturing and reuse. Traditionally, recyclers have resorted to using heuristics for analysing the breakdown of products and the associated costs. In this paper, a quantitative method of disassembly analysis is developed. Its aim is to improve the efficiency of the disassembly planning process and to generate an optimal disassembly sequence which maximizes profit. Three economic indices are used to evaluate the trade-off between reclamation and disposal of individual components. A systematic procedure of generating an optimal disassembly sequence based on maximizing the profits of material recovery is presented. Three criteria are established to reduce the search space and facilitate recovery opportunities: (1) material compatibility, (2) clustering for disposal, and (3) concurrent disassembly operations. An example is provided on a product being disassembled for recycling at a local recycling plant in Canada.     

A multi-phase model for product part change problems

The parts of a product may be changed over the product life cycle for the purpose of enhancing profits, that is to say, product part change has a direct impact on production efficiency and customer satisfaction. The analysis of product part change can provide producers with related components/part suppliers by analysing the product's configuration. Before performing the physical assembly of a specific product, selected part suppliers are connected and have to verify their ability to assemble. This paper proposes an assessment model consisting of three major phases: (1) analysis of the assembly relationship among the parts; (2) preliminary selection of the appropriate parts and their suppliers; and (3) solution selection for supplier association. The proposed model solves the product part change problem for a given related quality level, operation and cost data. The liaison graph, fuzzy theory and value engineering approaches are employed in phases 1 and 2 to analyse the product's configuration and assembly relationships, to deal with production data and to carry out the preliminary analysis. To solve the solution selection model of phase 3, a genetic algorithm based heuristic approach was developed. Finally, the configuration of the stand module of TFT-LCD is used as an example to demonstrate the application and working of the proposed approach. The results of the illustrated example are used to demonstrate its efficiency and effectiveness in solving the part change problem.     

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.     

Scheduling disassembly

We present an algorithm for scheduling the disassembly of discrete parts products characterized by a well defined product structure. As opposed to MRP where the demand occurs at the end item level, the demand in the disassembly case is motivated by the component level of the product structure. Even though the objective in the disassembly case is the reverse of that of MRP, the algorithm itself is not the reverse of the MRP algorithm. It is considerably more complicated. An example is presented and its implementation in a spreadsheet is described.