Heuristic algorithms for maximising the total profit of end-of-life computer remanufacturing

Recently the optimisation of end-of-life (EOL) computer remanufacturing has been highlighted since a big amount of used computers have been disposed of every year. Each part inspected after disassembling EOL computers can have various EOL options such as reuse, repair, reconditioning and so on. Depending on EOL options, recovered values and costs of parts will be different. Hence, in order to maximise the profit of remanufactured computers, it is important to develop the method as to how to decide the EOL options of computer parts. To this end, this study deals with a decision-making problem to select the best EOL option policy of the computer parts for maximising the total profit of computer remanufacturing considering its incurred costs and demand of remanufactured computers during multiple production periods. In particular, to maximise the total profit, the conditional repair option is newly proposed. To resolve the problem, a genetic search algorithm and an ant colony search algorithm have been developed. Computational experiments have carried out to evaluate the algorithms and the proposed conditional repair option.     

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.     

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.     

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.     

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.     

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.     

Product recovery optimization in closed-loop supply chain to improve sustainability in manufacturing

When business practices shift from a traditional open supply chain to a closed loop instead, the environmental and societal issues are efficiently integrated in business development. However, even an efficiently integrated shift introduces a number of trade-offs due to the contradictory goals that emerge from that business’s economical, environmental and social dimensions. In this paper, we propose a multi-objective mixed integer mathematical problem for a generic closed-loop supply chain (CLSC) network to rationalise how a system’s product recovery helps to improve manufacturing sustainability. The CLSC network proposed in this study consists of a hybrid manufacturing facility, warehouse, distribution centres, collection centres and a hybrid recovery facility (HRF). The proposed model determines the best location for the HRF and optimal flow of products, recovered parts and material in the network while it simultaneously maximises profit, saves activity costs, helps to decrease the harmful effects of the manufacturing process and makes a positive impact on societal development. To validate the model, a numerical illustration with the help of a case study from an electrical manufacturing industry is offered. The results authenticate the approach of the model towards the fulfilment of various environmental regulations. A sensitivity analysis, completed on demand, and the return rate also assists decision-makers to manage their decisions with a broader insight towards manufacturing sustainability.     

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.     

Modelling and evaluating product end-of-life options

The paper focuses on the modelling and evaluating product end-of-life options, which is the problem of representing products and determining disassembly sequences with the objective of maximizing revenue. For the problem considered here, three algorithms were developed. The first is the algorithm to generate the product recovery graph semi-automatically for a given product liaison graph. Then, using the generated product recovery graph, another algorithm is developed to obtain optimal disassembly plans that maximize revenue. This algorithm is based on the backward calculation so that the hyperedges of the recovery graph are visited only once. Finally, to cope with uncertainties of the end-of-life products, a recovery graph questioning algorithm is suggested to find the margin of allowed revenue reduction of a given target edge that maintains the same optimal plan. Application of the three algorithms is illustrated using an example.     

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.     

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).     

Integrating reverse logistics into the strategic planning of a supply chain

This paper proposes a mixed-integer programming (MIP) model for the strategic production and distribution planning of a supply chain (SC) integrating reverse logistics system. Such reverse logistics planning addresses the collection, recovery and marketing of recovered products, in addition to returned components and packing/wrapping materials. The model includes an approach that uses retail outlets as a two-way channel for marketing new products, collecting used/returned products and remarketing recovered products as a way of promoting an effective product recovery system in SC operation and optimising costs. The recovery of products/components is planned through a pool of recovery service providers (RSPs), so that maximum recovery can be ensured through combining the expertise of RSPs within optimum costs. The model follows a two-step process that addresses strategic decisions about product recovery in the first step, and the integration of the recovery process into overall SC decisions in the final step. A numerical example illustrates the applicability of the model. A sensitivity analysis has been conducted to show the effects that changes in the recovered product quantity have on the overall SC performance.     

Modelling end of life phase of the complex products: the case of end of life aircraft

There are different approaches to modelling the products at the End of Life (EOL), including closed loop approach, the ladder of lansink and material flow. The sustainability principles and the complexity of the products may affect the choice of modelling type. The life cycle costs, several subsystems and the interaction between design features, construction, technology, suppliers and legislation affect the degree of complexity of the products. Hence, developing appropriate strategies are required for modelling EOL phase of the complex products. This article presents strategies for modelling EOL phase of complex products considering sustainability tools, EOL phase models and the characteristics of these products. An ingenerated approach is proposed to address the four essential aspects of the complex product at the EOL: operational, tactical, strategic and sustainability. The case of aircraft at the EOL is presented to highlight the application of the proposed approach.     

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.     

Boundary recovery after 3D Delaunay tetrahedralization without adding extra nodes

In this paper, we investigate boundary recovery, the problem that has troubled researchers ever since Delaunay-based methods were applied to generate mesh. There are a number of algorithms for boundary recovery already and most of them depend heavily on adding extra nodes. In this paper, we make an effort to seek a method to recover boundaries without using extra nodes. It was noted that some previous algorithms imposed artificial boundary constraints on a meshing problem at the recovering stage; we first try to discard these artificial constraints and thus make things easier. Then a new method is proposed by which the boundaries can be recovered by means of two operations: (1) creating a segment in the mesh and (2) removing a segment from the mesh. Both operations are special cases of a general local transformation called small polyhedron reconnection operation. The method works well when coupled with the sphere-packing method proposed by the first author. If the mesh sizing function is suitable, a good configuration of nodes will be created accordingly by the sphere-packing method and the boundary can be recovered by the local transformation presented here without inserting extra nodes. Copyright © 2007 John Wiley & Sons, Ltd.     

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.     

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.     

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.     

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.     

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.