375 Chapter 14 Optimal Disassembly Planning From the viewpoint of environmental protection, an effi cient planning of disassembly operations takes on strategic importance, since it can improve both the useful life of the product (facilitating service interventions) and the end-of-life phase (favoring the recycling of materials and the reuse of compo- nents). After an overview of the main issues and current approaches to the problem of Disassembly Planning, this chapter proposes a new approach aiding the identifi cation of optimal disassembly in relation to service opera- tions and the recovery of resources at the end-of-life. Despite their diverse aims, the two tools proposed here have in common both the modeling typology on which they operate and the logic underlying the search algo- rithms used. The choice of genetic algorithms is dictated by the complexity inherent in the complete mathematical solution of the problem of generating the disassembly sequences, which suggests the use of a nonexhaustive approach. The results of a wide-ranging series of simulations indicate that both tools can be used not only for the characteristic purposes of disassembly planning, but also for aiding design. This is particularly true for the second tool, which offers a complete approach to the problem of disassembly aimed at recovery; it combines evaluations of economic and environmental impacts and extends these evaluations over the product’s entire life cycle. Its structure provides the designer with an autonomous capacity to decide on both the level of disassembly to be achieved and the defi nition of the optimal recovery plan (i.e., the best destination for the disassembled components on the basis of their properties). The fundamental issues in this chapter were previously introduced in 14.1 Disassembly Planning The disassembly of products is necessary every time it is appropriate to remove subsystems or single components comprising the product itself. From 2722_C014_r02.indd 3752722_C014_r02.indd 375 11/30/2005 1:52:47 PM11/30/2005 1:52:47 PM © 2006 by Taylor & Francis Group, LLC Chapters 9 and 13. the viewpoint of environmental protection, disassembly can have several goals (Lambert, 1997): recovering parts, components, and subassemblies that can be reused in new products; recovering recyclable materials; and accessing parts or components that may be subject to service operations (repair, maintenance, diagnostics). As highlighted in the previous chapter, interventions to improve the process of product disassembly can be made at two different levels (Jovane et al., 1993; Gungor and Gupta, 1999): • At the design phase, adopting choices that can favor the disassembly of the constructional system (Design for Disassembly—DFD) • Seeking to best plan and optimize the process of disassembly (Disassembly Process Planning—DPP) Disassembly Process Planning, which includes all the problems relating to disassembly of constructional systems, is considered in terms of two differ- ent levels of analysis (Lambert 2003): • Sequence Level—Starting from a mathematical representation of the assembled system, the analysis considers the problem of generating and optimizing the sequence of disassembly (Disassembly Sequencing). • Detailed Level—Starting from the physical and geometric properties of components and fasteners, disassembly analysis takes into consid- eration the handling of components, directions of disassembly, conditions of obstruction, and choice of equipment, as far as deter- mining the trajectories of any possible automatic manipulators (Disassembly Path Planning). In a more complete view, these aspects should also be complemented by an analysis of the optimal disassembly level (Disassembly Leveling), which is the study of the level of disassembly that bests reconciles the requirements and the advantages of disassembling a system’s components with the costs Section 13.1. The literature contains general evaluations of cost–revenue curves for disassembly interventions aimed at repair and recovery at end-of- disassembly become prohibitive as the level of disassembly increases. On the contrary, beyond a certain level the revenues tend to stabilize. Consequently, the profi t curve has a maximum, after which it tends to decrease as the level of disassembly increases. The profi t of recovery can be improved if the product architecture is such that the fi rst disassembly results in freeing the most critical or most valuable 376 Product Design for the Environment 2722_C014_r02.indd 3762722_C014_r02.indd 376 11/30/2005 1:52:47 PM11/30/2005 1:52:47 PM © 2006 by Taylor & Francis Group, LLC entailed by this disassembly. This concept was introduced in Chapter 13, life (refer to Chapter 9, Figure 9.5). According to these evaluations, the costs of Optimal Disassembly Planning 377 parts. This consideration highlights the direct dependence of the effi ciency of disassembly on the product architecture, and particularly on the depth of disassembly of single components in relation to certain characteristics that can make their removal opportune or necessary during use or at end- The profi t function of disassembly is also strongly sensitive to the effi ciency of the disassembly sequence. With a judiciously optimized disassembly sequence it is possible to: • Reduce the times and costs of disassembly operations • Perform the most appropriate disassemblies, avoiding those that are superfl uous or less profi table This is, therefore, the fi rst intervention factor for profi table disassembly. It is indirectly conditioned by the characteristics of the product’s architecture, which, depending on the typology, can favor the effi ciency of the disassembly sequence. In conclusion, the problem of disassembly planning includes the main aspects: • Analysis of the system characteristics (component geometries, rela- tions between components, junctions) • Generation of possible disassembly sequences • Identifi cation of the most effi cient and economical disassembly sequences • Determination of optimal level of disassembly 14.1.1 General View of the State of the Art Early approaches to the problem of disassembly planning were developed on the basis of understanding previously acquired in relation to the prob- lems associated with assembly. These approaches came from the assump- tion that disassembly sequences could be assimilated to assembly sequences in reverse (Homem De Mello and Sanderson, 1991). However, various authors have subsequently described the profound differences existing between the two problems (principally, the fact that assembly processes are often not completely reversible, and the frequent need for selective or partial disassembly). These two problems demonstrate the need to treat disassembly planning in a specifi c and more appropriate manner (Srinivasan et al., 1997; Dini et al., 2001; Lambert, 2003). With this new perspective, a large number of approaches to disassembly have been proposed over the last decade, suggesting diverse attempts at classifi cation 2722_C014_r02.indd 3772722_C014_r02.indd 377 11/30/2005 1:52:48 PM11/30/2005 1:52:48 PM © 2006 by Taylor & Francis Group, LLC of-life. Depth of disassembly was thoroughly discussed in Chapter 13. (O’Shea et al., 1998; Tang et al., 2000; Lambert, 2003). In particular, authors have emphasized: • The different levels of approach (oriented toward components, or oriented toward products) • The differences in the aims of disassembly (maintenance and servicing, or removal and recovery at end-of-life) • The differences in disassembly modeling, in generating the disas- sembly sequences, and in methods to identify the optimum solution 14.1.2 Extension to Design of the Life Cycle In environmental terms and in relation to the phases of a product’s life cycle, the great importance of disassembly is its functionality in the phases of use (supporting maintenance and servicing operations) and end-of-life (support- ing operations of recovery and disposal). With regard to servicing operations, several different approaches are described in the literature (Subramani and Dewhurst, 1991; Yokota and Brough, 1992; Vujosevic et al., 1995). While these have in common the criterion of determining the optimum disassembly sequence based on the minimization of costs, they differ in the typologies of models used to represent the disassembly process (connection diagrams or component–junction diagrams, “AND/OR” graphs). However, the problem is generally rooted in planning the selective disassembly of components requiring maintenance. The subsequent consideration of the operations of recovery and disposal at end-of-life required an extension of the problem of disassembly planning. Here, disassembly sequences must be optimal not only in strictly economic terms, but also in terms of environmental impact of the end-of-life phase (Gungor and Gupta, 1999). This led to the introduction of the concept of Recovery Planning, based on a quantitative evaluation of a product’s value at end-of-life in terms of its potential for reuse, remanufacturing, and the recycling of materials (Navin-Chandra, 1993; Navin-Chandra, 1994; Zussman et al., 1994; Pnueli and Zussman, 1997). These approaches all use graphic models to represent disassembly processes. In contrast, other approaches tend toward analytical mathematical modeling. Beginning with component–junction diagrams, these introduce matrix representations to express the precedences of component disassem- bly (Disassembly Precedence Matrices) (Zhang and Kuo, 1997). This allows the elaboration of more data and makes it possible to study the problem at a greater level of detail, going so far as to consider even the times and costs of changing a tool or the direction of disassembly. Nearly optimal 378 Product Design for the Environment 2722_C014_r02.indd 3782722_C014_r02.indd 378 11/30/2005 1:52:48 PM11/30/2005 1:52:48 PM © 2006 by Taylor & Francis Group, LLC Optimal Disassembly Planning 379 disassembly sequences can then be developed on the basis of a much more detailed analysis than those possible with graphic models (Gungor and Gupta, 1998). The introduction of matrix modeling also allows direct inte- gration with CAD modeling of the assembly under examination (Dini et al., 2001). 14.1.3 Application of Artifi cial Intelligence Extending the problem of disassembly planning and placing it in the context of the product’s life cycle results in a signifi cant increase in the amount of data to be elaborated and in the complexity of the performances to be opti- mized. To deal with the resulting increased complexity of the problem, researchers have turned to certain instruments of artifi cial intelligence (Lambert, 2003): neural networks, fuzzy logic, and genetic algorithms. The latter, in particular, have been applied in various cases, by virtue of their characteristic of working effi ciently in open research domains and of identi- fying solutions close to optimal. In this context, some studies have described the use of genetic algorithms as aids in: • The optimization of disassembling components or subassemblies for the purpose of maintenance, based on a component–contact constraints diagram model (Li et al., 2002) • The economic and environmental analysis of disassembly processes, based on an AND/OR graph model and converting environmental factors into economic costs (Seo et al., 2001) • The defi nition of the most effi cient disassembly strategy in relation to the requirements of recovery at end-of-life, based on criteria of maximum revenue and minimum number of components to be disposed of as waste (Caccia and Pozzetti, 2000) 14.1.4 Concluding Considerations The great variety of methods proposed in the literature, summarized above, is directly correlated to the specifi c problems the various authors intended to resolve. At present, there is little effort directed at the defi nition of a system- atic methodology that is able to integrate the different approaches and to guide in solving different problems using the most effective approach. Apart from the specifi city of the various methods found over the entire spectrum of studies on disassembly planning, it is also worth noting the limited vision of the environmental problem, which in this context is usually treated by trans- lating environmental aspects into economic costs and considering only the end-of-life phase in the analysis. 2722_C014_r02.indd 3792722_C014_r02.indd 379 11/30/2005 1:52:48 PM11/30/2005 1:52:48 PM © 2006 by Taylor & Francis Group, LLC 380 Product Design for the Environment 14.2 Objectives and Approach to the Problem With clear reference to these observations on the state of the art, this chapter proposes an approach to disassembly planning characterized by: • Matrix-type modeling, based on the analysis of component geome- tries and on the relations of junctions and movement, in a way that facilitates direct interfacing with conventional CAD modeling of assemblies • A calculation program with a structure allowing the identifi cation of the disassembly sequence that is optimal in terms of the aspects considered most signifi cant—servicing operations and planning recovery at end-of-life The two different aspects treated here, service and the recovery of resources at end-of-life, require that a distinction be made between the two different typologies of disassembly (Srinivasan et al., 1997): • Selective disassembly, where the objective is the disassembly of one or more preselected components (an approach oriented toward servicing operations) • Partial or complete disassembly, where the components to be disas- sembled are not chosen a priori, but are defi ned by the research algo- rithm itself on the basis of certain important properties characterizing the components (an approach oriented toward recovery operations) With these objectives, it is possible to develop two algorithms to solve the problem of optimizing disassembly in the two distinct cases. Despite their different aims, the two proposed tools operate on the same typology of modeling and share the logic followed in developing the two algorithms. It is interesting to note, however, that while the fi rst is limited to a conventional approach to the problem (selective disassembly optimized by minimizing disassembly times), the second tool deals with the environmental problem of recovery through an innovative approach, in that it is: • Based on a complete analysis (bringing together functions of cost and environmental impact) and extends the evaluations to cover the entire life cycle • Characterized by an autonomous capacity to determine both the level of disassembly to be achieved and the optimal recovery plan (i.e., the best destination for the disassembled components, on the basis of their properties) 2722_C014_r02.indd 3802722_C014_r02.indd 380 11/30/2005 1:52:48 PM11/30/2005 1:52:48 PM © 2006 by Taylor & Francis Group, LLC Optimal Disassembly Planning 381 14.3 Common Structure of the Proposed Tools In general, the complete mathematical solution to the problem of generating disassembly sequences requires such a large number of calculations that it becomes extremely complex (Lambert, 1997; Moyer and Gupta, 1997). While heuristic methods can be used to manage this problem, they do not ensure the determination of optimal solutions (Gungor and Gupta, 1997; Kuo et al., 2000). To reach the objectives that have been set, the tools proposed here are directed at the defi nition of optimal (or near optimal) disassembly sequences, using algorithms of the genetic type (GA—Genetic Algorithms) (Holland, 1975; Goldberg, 1989). This choice is principally motivated by two character- istics of the research space to be explored that make the use of GA advisable (Mitchell, 1998): the space is vast and is not unimodal. Starting from this core choice of GA, the two problems discussed above can be treated by developing two tools that are distinct but nevertheless share the same calculation code structure. Furthermore, in both cases the code elaborates the same mathemat- ical model for the description of the geometries of, and relations between, the various components (expressed through constraint matrices). The code is thus able to take into account the changes occurring in the system’s structure during the progressive disassembly of the single parts. Figure 14.1 shows the scheme common to both the tools proposed. It postulates a preliminary FIGURE 14.1 Common schematization of tools. 2722_C014_r02.indd 3812722_C014_r02.indd 381 11/30/2005 1:52:48 PM11/30/2005 1:52:48 PM © 2006 by Taylor & Francis Group, LLC 382 Product Design for the Environment modeling, which has the aim of interpreting the assemblies under analysis in mathematical terms, allowing the subsequent elaboration of this information by the resolving algorithm. The latter, drawing on a set of data (included in a database) with varying typologies depending on the tool, identifi es the optimal solution through three main phases: • Identifi cation of the system, requiring the formalization of the solu- tion type to investigate and the defi nition of the objective function • Generation of possible disassembly sequences • Identifi cation of the optimal solution The main elements common to both tools for disassembly planning are described below. In later sections, they will be described in greater detail, delineating their more specifi c characteristics. 14.3.1 Common Preliminary Modeling The system to be disassembled is represented here as consisting of a fi nite number of independent elements that can be removed individually. System elements are taken to mean: • Single components linked to the system by reversible fasteners • Any possible subgroups of components linked together by irrevers- ible fasteners • All reversible fasteners and junction systems (screws, clips, snap- fi ts, etc.) With this type of approach, it is not possible to consider subgroups of elements to be treated as a single entity during the disassembly sequence, unless they are predefi ned on the basis of their homogeneity, the compatibility of their Considering a generic system consisting of n elements, the properties rela- tive to each i-th element E i are expressed by an index defi ning the typology of the element. This index allows one component, whose removal does not require particular operations other than simple translation in space, to be distinguished from a fastener, whose removal instead requires a specifi c intervention with a resulting increase in cost and time. Indicating the total number of different element typologies by n e , each index of element typology e j (which can assume integer values in the range [0, n e Ϫ1]) is represented by data quantifying the diffi culty of removing each element of that type. This property can be quantifi ed using disassembly times (already assessed by other authors applying general methods for the evaluation of work times 2722_C014_r02.indd 3822722_C014_r02.indd 382 11/30/2005 1:52:48 PM11/30/2005 1:52:48 PM © 2006 by Taylor & Francis Group, LLC materials, or other criteria of affi nity between the elements (see Chapter 11). Optimal Disassembly Planning 383 [Vujosevic et al., 1995; Kroll and Carver, 1999]), or developing specifi c new approaches (Sodhi et al., 2004). In this way, each element type can be described by a mean disassembly time and the corresponding term can be normalized with respect to the simplest intervention, that of horizontal translation. characterization adopted in the present study, based on the approximate disassembly times reported in the literature for the most common types of unfastening operations (Dowie and Kelly, 1994). The elements defi ned in this way can be subjected to different elementary operations; these, too, are described by an index expressing the typology of disassembly operation. Indicating the total number of operation typologies by n o , it is simple to distinguish between operations given that, once the diversifi cation of the fasteners and other junction systems has been included in the analysis of the elements, the operations are reduced exclusively to movements of linear translation. In the three-dimensional case, such transla- tions occur along the directions X, –X, Y, –Y, Z, and –Z; therefore, n o ϭ 6. Each generic index of operation typology o k , which can assume integer values in the range [0, n o Ϫ1], is associated with an execution time for the operation, ultimately consisting of translation along the k-th direction. Also in this case, execution times are normalized with respect to the simplest operation of horizontal linear translation and can be compiled in a table analogous to Table 14.1. In this way, if it is considered opportune, it is possible to take into account the potentially greater diffi culty that may be ascribed to operations of vertical translation, planning for a longer execution time than that for hori- zontal translation. In representing the system in function of disassembly planning, the model- disassembly depth analysis), based on the geometric characteristics of the system and on the consequent precedences for disassembly among the elements constituting the assembly. Although this modeling is based on a geometric-type approach for the analysis of movements (Woo and Dutta, TABLE 14.1 Indices of element typologies and characterization ELEMENT TYPOLOGY INDEX (e j ) DESCRIPTION MEAN TIME (sec) NORMALIZED TIME (tne j ) 0 Component (to remove) 1.25 1 1 Screw (to remove) 0.6 (per turn) 0.48 (per turn) 2 Snap–fi t (to open) 1.5 1.2 3 Clip (to remove) 1 0.8 4 Connection (to break) 2 1.6 5 Wires (to disconnect) 1.5 1.2 2722_C014_r02.indd 3832722_C014_r02.indd 383 11/30/2005 1:52:49 PM11/30/2005 1:52:49 PM © 2006 by Taylor & Francis Group, LLC ing proposed here is of a matrix type (such as that proposed in Chapter 13 for Table 14.1 shows the indexing of element typologies together with the 384 Product Design for the Environment 1991; Srinivasan and Gadh, 2002), and makes use of preexisting matrix models (Zhang and Kuo, 1997; Gungor and Gupta, 1998; Dini et al., 2001), this modeling is particularly simple and comes down to the compilation of binary constraint matrices, one for each possible direction of disassembly. In the case of a system consisting of n elements, for the disassembly direction X this matrix is expressed by: Vv X ij i 1,2, , j 1,2, , ϭ ϭ ϭ ⎡ ⎣ ⎤ ⎦ … … (14.1) where the term v X ij has a value of 1 if the j-th element obstructs the removal of the i-th element in the X direction; otherwise it is 0. Analogously, the spatial constraint matrices can be defi ned in the other directions (in the case of three- dimensional analysis there will be six matrices, one for each direction of disassembly X, –X, Y, –Y, Z, and –Z). Concerning the compilation of these matrices, it should be specifi ed that: • If the i-th element is a component, it is necessary to consider as obstacles all the other components impeding its movement in the direction under examination, and all the fasteners and junction systems acting directly on it, constraining it to other components (the latter may also not appear as direct obstacles to the movement of the component in question). • If the element is a fastener, it is necessary to consider as obstacles all the components impeding its accessibility and movement in the direction under examination. 14.3.2 Disassembly Sequence and Operation Time Using the preliminary modeling described above, the resolving algorithm will generate the possible disassembly sequences in a random manner, as explained in detail below. It will then evaluate the real practicability of each sequence based on a simple rule: To disassemble any system component, it is necessary to begin by disassembling the more external components whose removal is unobstructed, until the fi nal objective is reached. A sequence will, therefore, consist of a series of element–operation couplets, ordered in such a way that all the constraint matrices of the type defi ned by Equation (14.1) are respected. Beginning with the removal of the most external elements, and updating the constraint matrix step-by-step as the elements are removed, a correctly ordered (i.e., truly practicable) disassembly sequence is obtained. On the basis of this statement for the generation of the sequences, it is possi- ble to defi ne a function that quantifi es the excellence of the sequence. With this aim, many of the studies present in the literature propose mathematical 2722_C014_r02.indd 3842722_C014_r02.indd 384 11/30/2005 1:52:49 PM11/30/2005 1:52:49 PM © 2006 by Taylor & Francis Group, LLC [...]... of these characteristics, the proposed new tool can be effectively employed in planning the end-of -life phase of preexisting products and as an aid in the product development phase In fact, it can be used as a tool for the simulation and optimization of a system, for design specifically aimed at favoring the end-of -life phase of a new product, or in a more attentive design intervention that concentrates... of the end-of -life, it is more appropriate to have a complete vision of the product s behavior over the entire life cycle It is, therefore, necessary to quantify the economic costs and environmental impacts beginning with the production phases, because it can happen that a design intervention aimed at improving the possibility of recovering the product at end-of -life can also lead to an increase in the. .. end-of -life phase (Design for Recovery), but also for a more attentive design intervention concentrating on the product s overall environmental performance In fact, it evaluates the optimal level of disassembly of a product and the most appropriate final destination for the disassembled components, correlating these with several properties of the components that characterize their behavior over the. .. to evaluate the weight of each element, the cost of production, and the environmental impact associated with its production (the latter evaluation will be treated below) The reusability of an element depends on the designed duration of the element’s useful life, as a ratio of the anticipated working life of the entire product If the duration is at least twice the working life of the product, the element... reusing an element allows the recovery of the environmental impact associated with its production • The second term quantifies the impact of recycling the recyclable fraction of material (this impact can also assume a negative value, that is, it can also be a recovery of impact), and the impact of disposing of the remaining nonrecyclable fraction • The third term quantifies the impact of disposing of the material... requires the creation of a table analogous to Table 14. 1 for element typology In this case, however, each material index is associated with a set of data, as shown in Table 14. 2, which summarizes the complete characterization Having chosen a congruous number of materials, each with an identifying index, it is possible to associate each material with the following terms that characterize it in relation to the. .. for characterizing the difficulty of disassembling a component; that is a basic issue in the method of disassembly depth analysis proposed in Chapter 13 Because of its particular characteristic of being based on an analysis that can be extended over the entire life cycle, the second prototype can be used as a tool for optimizing the system not only for design specifically oriented toward favoring a product s... directed at recovery at the product s end-of -life, seeking a partial or complete disassembly where the components to be disassembled are not preset but are defined by the algorithm itself on the basis of certain important properties characterizing them In general, the problem of product recovery at end-of -life can be formulated as follows (Chapter 9, Section 9.3): For a given product, determine the recovery... and operations, as defined in Section 14. 3.1 (i.e., the values of removal and handling times relative, respectively, to the typologies of elements and disassembly operations) • Those concerning the complete characterization of the materials, as defined in Table 14. 2 As was suggested in Chapter 12, information on the recyclable fractions of the more common materials may be obtained from commercially available... that are explained below The algorithm initially generates a random population From this population, each execution cycle (generation) selects individuals on the basis of their fitness and then applies genetic operators to them, with the most common being cross-over and mutation These operators are applied according to parameters (called “probabilities of execution”) that remain constant throughout the . under analysis in mathematical terms, allowing the subsequent elaboration of this information by the resolving algorithm. The latter, drawing on a set of data (included in a database) with varying. the product development phase. In fact, it can be used as a tool for the simulation and optimization of a system, for design specifi cally aimed at favoring the end-of -life phase of a new product, . for reuse, remanufacturing, and the recycling of materials (Navin-Chandra, 1993; Navin-Chandra, 1994; Zussman et al., 1994; Pnueli and Zussman, 1997). These approaches all use graphic models