Soft Computing in Product Recovery: A Survey Focusing on Remanufacturing System
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paper_title: A petri net approach to disassembly process planning

paper_content:

Recycling and remanufacturing involve product disassembly to retrieve the desired parts and/or subassemblies. Disassembly is a systematic method for separating a product into its constituent parts, components, or other groupings. Disassembly process planning is critical in minimizing the amount of resources (e.g., time and money) invested in disassembly and maximizing the level of automation of the disassembly process and the quality of the parts (or materials) recovered. We propose an algorithm which automatically generates a disassembly Petri net (DPN) from a geometrically-based precedence matrix. The resulting DPN can be analyzed to generate all feasible disassembly process plans (DPPs), and cost functions can be used to determine the optimal DPP; alternatively, heuristic methods may be used to generate near-optimal DPPs.
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paper_title: A multiple objective tabu search approach for end-of-life product disassembly

paper_content:

This paper addresses the disassembly-to-order (DTO) and the disassembly sequence planning (DSP) problems and employs a multiple objective tabu search (MOTS) algorithm to solve them. The focus of the study is on the disassembly operations of end-of-life (EOL) products taken back from their last users (owners). The proposed approach aims at disassembling the EOL products for reusing, recycling, storing and properly disposing them in an environmentally benign manner. Thus, the model controls and restricts the environmental damage while increasing the value captured from the EOL products. These goals are achieved in an economically viable manner. The proposed model employs two independent modules, namely, the DTO module and the DSP module. The DTO module embodies multiple objectives and provides various options for the number of items to be disassembled from each EOL product for resale, recycling, storage and proper disposal, along with the numbers and types of EOL products to be taken back. The module considers both environmental and financial objectives while meeting the demand expectations. The DSP module provides multiple feasible disassembly sequences for the EOL products. The module considers and preserves the precedence relationships among the items while fulfilling multiple objectives.
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paper_title: A review of current research on disassembly sequence generation and computer aided design for disassembly

paper_content:

For many reasons, environmental consciousness is increasingly becoming a major concern for manufacturers. It is widely acknowledged that the most ecological way to treat worn-out products is by recycling, for which disassembly is critically important. This paper presents a review of recent research on the development of disassembly, focussing on disassemblability, disassembly sequence generation and computer aided design for disassembly. A total of 84 papers are surveyed, and conclusions are drawn as to further directions for research.
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paper_title: Design for Environment: An Environmentally Conscious Analysis Model for Modular Design

paper_content:

Nowadays, more and more manufacturing enterprises are working hard to improve the environmental performance of their products. Pollution may be generated through the life cycle of a product. To maximally improve a product's overall environmental performance and reduce potential environmental impacts, Design for the Environment can be used as early as the design stage for the product. For electromechanical products, modular design is a widely used design method. While traditional modularity analysis pays little attention to environmental factors, this research aims at including comprehensive environmentally conscious criteria into modularity analysis. This paper presents a semi-quantitative environmentally conscious modular analysis model in order to reduce pollution. Eight criteria are identified to capture most of potential environmental impacts of modular products, some of which are fuzzy criteria. Fuzzy Analytic Hierarchy Process is used to rank these environmental criteria, and fuzzy numbers are used to map some uncertain judgments of decision makers with crisp numbers. Structure of a product is represented as a fuzzy graph. The proposed modularity analysis includes similarity analysis and independence analysis. In the end, an example is given to illustrate the developed methodology.
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paper_title: The inventory management system for automobile spare parts in a central warehouse

paper_content:

Because of the complex structure of spare parts supply chain, the conventional approaches, which do not consider the relationships between decision factors globally, cannot achieve the optimal performance. Therefore, this paper aims to develop an enhanced fuzzy neural network (EFNN) based decision support system for managing automobile spares inventory in a central warehouse. In this system, the EFNN is utilized for forecasting the demand for spare parts. However, without considering relevant domain knowledge, traditional neural networks are found to be suffered from the problem of low accuracy of forecasting unseen examples. Therefore, in our EFNN, the following improvement is made: First, it assigns connection weights based on the fuzzy analytic hierarchy process (AHP) method without painstakingly turning them. Second, by generating and refining activation functions according to genetic algorithm, our EFNN can provide comprehensive and accurate activation functions and fit a wider range of nonlinear models. Last, but not least, an adaptive input variable is introduced to decrease the impact of the bullwhip effect on the forecasting accuracy. The proposed system is evaluated with the real word data and experimental results indicate that our EFNN outperforms other five models in fill rate and stock cost measures.
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paper_title: Production?remanufacturing control problem for defective item under possibility constrains

paper_content:

This paper presents a new type of an optimal control production-recovery inventory system under inflation and discounting in fuzzy-stochastic environment. The product defectiveness is a random variable. The return products are the used products collected from the open market. The defective product is treated as return product. Return and defective products are either remanufactured or disposed of. Remanufactured product is used as a new product which can be sold to the customer. The demand depends upon the stock of serviceable product, time and selling price. In this paper, we propose an optimal control approach to optimise the production, remanufacturing and disposal strategy with respect to dynamic demand and return. The upper bounds of production, remanufacturing and disposal are fuzzy parameters. The optimum results are presented in both tabular and graphical forms.
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paper_title: Heuristic genetic algorithm for capacitated production planning problems with batch processing and remanufacturing

paper_content:

Abstract In this paper, we analyze a version of the capacitated dynamic lot-sizing problem with substitutions and return products. Both batch manufacturing and batch remanufacturing are considered within the framework of deterministic time-varying demands in a finite time horizon, where the option of emergency procurement/outsourcing subject to a subcontract is also allowed. Setup costs are taken into account when batch manufacturing or batch remanufacturing takes place. We first apply a genetic algorithm to determine all periods requiring setups for batch manufacturing and batch remanufacturing, then develop a dynamic programming approach to provide the optimal solution to determine how many new products are manufactured or return products are remanufactured in each of these periods. The objective is to minimize the total cost, including batch manufacturing, batch remanufacturing, emergency procurement, holding and setup costs. Numerical examples illustrate the effectiveness of the approach.
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paper_title: Machine learning approach for determining feasible plans of a remanufacturing system

paper_content:

Resource planning for a complex remanufacturing system is in general extremely difficult in terms of, e.g., problem size and uncertainties. In many cases, simulation is the only way to select a good plan among a great number of candidates. When there exist complicated constraints, direct selection could be very inefficient since many candidates may not be feasible but cannot be excluded beforehand. To meet the challenge, a machine learning method is introduced in this paper to perform feasibility analysis. The rough set theory is first applied to establish the relationship between a plan and its feasibility and an iterative reinforcement process is applied to enhance confidence. The numerical testing results show that this method is promising and scalable for the large-scale problems. The research lays a basis for developing an efficient simulation-based optimization method with complicated constraints. Note to Practitioners-This paper was motivated by the resource planning problem for a complex remanufacturing system, which is very important but in general extremely difficult to deal with in terms of, e.g., problem size and uncertainties. Simulation is probably the only way available to select a good plan among a number of candidates. When there exist complicated constraints, simulation becomes even more difficult and selection through simulation could be very inefficient since many candidates may not be feasible but cannot be excluded before simulation. Determining feasibility beforehand is extremely difficult by analytical or numerical methods. This paper suggests a new method using a machine learning-based approach to predict the plan feasibility required in practical applications and can be considered as the first step for optimization-based planning. By applying the rough set theory, the prediction rules are obtained or learned from a training dataset generated by simulation. Then, an iterative reinforcement process is applied to enhance the confidence of learning and to perform iterative retraining on new datasets by the rough set method to generate new rules to add to the knowledge base until the preset threshold is satisfied. The numerical testing results show that the above method is capable of determining the feasible plans for a remanufacturing system with good accuracy. The method is efficient and scalable for the large-scale problems. The method developed in the paper is being incorporated in the framework of ordinal optimization, and a new constrained ordinal optimization method has been developed for remanufacturing planning.
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paper_title: A production-inventory model with remanufacturing for defective and usable items in fuzzy-environment

paper_content:

This paper investigates a production-remanufacturing system for a single product over a known-finite time horizon. Here the production system produces some defective units which are continuously transferred to the remanufacturing unit and the constant demand is satisfied by the perfect items from production and remanufactured units. Remanufacturing unit uses the defective items from production unit and the collected used-products from the customers and later items are remanufactured for reuse as fresh items. Some of the used items in the remanufacturing unit are disposed off which are not repairable. The remanufactured units are treated as perfect items. Normally, rate of defectiveness varies in a production system and may be approximated by a constant or fuzzy parameter. Hence, two models are formulated separately with constant and fuzzy defective productions. When defective rate is imprecise, optimistic and pessimistic equivalent of fuzzy objective function is obtained by using credibility measure of fuzzy event by taking fuzzy expectation. Here, it is assumed that remanufacturing system starts from the second production cycle and after that both production and remanufacturing units continue simultaneously. The models are formulated for maximum total profit out of the whole system. Here the decision variables are the total number of cycles in the time horizon, the duration for which the defective items are collected and the cycle length after the first cycle. Genetic Algorithm is developed with Roulette wheel selection, Arithmetic crossover, Random mutation and applied to evaluate the maximum total profit and the corresponding optimum decision variables. The models are illustrated with some numerical data. Results of some particular cases are also presented.
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paper_title: Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem

paper_content:

The growing amount of waste created by products reaching the end of their useful lives poses challenges for the environment, governments and manufacturers. Processing alternatives include reuse, remanufacturing, recycling, storage and disposal. With disposal considered the least desirable, the first process required by the remaining alternatives is disassembly. Just as the assembly line is considered the most efficient way to assemble a product, the disassembly line is the most efficient way to disassemble a product. Finding the optimal balance for the multi-objective disassembly line balancing problem is computationally intensive due to exponential growth. With exhaustive search calculations quickly becoming prohibitively large, methodologies from the field of combinatorial optimization hold promise for providing solutions. The disassembly line balancing problem is described here, then defined mathematically and proven to belong to the class of unary NP-complete problems. Known optimal instances of the p...
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paper_title: A balancing method and genetic algorithm for disassembly line balancing

paper_content:

Disassembly activities take place in various recovery operations including remanufacturing, recycling and disposal. The disassembly line is the best choice for automated disassembly of returned products. It is therefore important that the disassembly line be designed and balanced so that it works as efficiently as possible. The disassembly line balancing problem seeks a sequence which: is feasible, minimizes workstations, and ensures similar idle times, as well as other end-of-life specific concerns. However finding the optimal balance is computationally intensive with exhaustive search quickly becoming prohibitively large even for relatively small products. In this paper the problem is mathematically defined and proven NP-complete. Additionally, a new formula for quantifying the level of balancing is proposed. A first-ever set of a priori instances to be used in the evaluation of any disassembly line balancing solution technique is then developed. Finally, a genetic algorithm is presented for obtaining optimal or near-optimal solutions for disassembly line balancing problems and examples are presented to illustrate implementation of the methodology.
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paper_title: Disassembly planning of mechanical systems for service and recovery: a genetic algorithms based approach

paper_content:

In a perspective of improving the behavior of a product in its whole life cycle, the efficient planning of the disassembly processes acquires strategic importance, as it can improve both the product’s use phase, by facilitating service operations (maintenance and repairs), and the end-oflife phase, by favoring the recycling ofmaterials and the reuse of components. The present paper proposes an approach to disassembly process planning that supports the search for the disassembly sequence best suited for both aspects, service of the product and recovery at the end of its useful life, developing two different algorithms. Notwithstanding their different purposes, the two algorithms share the typology of modeling on which they operate, and the logical structure according to which the genetic search procedure is developed. The choice of implementing genetic algorithms was prompted by the intrinsic complexity of the complete mathematical solution to the problem of generating the disassembly sequences, which suggests the use of a non-exhaustive approach. As is shown in the results of a set of simulations, both algorithms may be used not only for the purposes related to disassembly process planning but also as supporting tools during the product design phases. This is especially so for the second algorithm, that deals with the problem of a recovery-oriented disassembly through an all-encompassing approach, combining economical and environmental considerations, and extending the evaluations to the whole life cycle of the product. This formulation gives this algorithm and autonomous decisional capacity on both the disassembly level to be reached, and the definition of the optimum recovery plan (i.e., the best destination for the disassembled components, based on some significant properties of them).
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paper_title: Design for Environment: An Environmentally Conscious Analysis Model for Modular Design

paper_content:

Nowadays, more and more manufacturing enterprises are working hard to improve the environmental performance of their products. Pollution may be generated through the life cycle of a product. To maximally improve a product's overall environmental performance and reduce potential environmental impacts, Design for the Environment can be used as early as the design stage for the product. For electromechanical products, modular design is a widely used design method. While traditional modularity analysis pays little attention to environmental factors, this research aims at including comprehensive environmentally conscious criteria into modularity analysis. This paper presents a semi-quantitative environmentally conscious modular analysis model in order to reduce pollution. Eight criteria are identified to capture most of potential environmental impacts of modular products, some of which are fuzzy criteria. Fuzzy Analytic Hierarchy Process is used to rank these environmental criteria, and fuzzy numbers are used to map some uncertain judgments of decision makers with crisp numbers. Structure of a product is represented as a fuzzy graph. The proposed modularity analysis includes similarity analysis and independence analysis. In the end, an example is given to illustrate the developed methodology.
---
paper_title: The inventory management system for automobile spare parts in a central warehouse

paper_content:

Because of the complex structure of spare parts supply chain, the conventional approaches, which do not consider the relationships between decision factors globally, cannot achieve the optimal performance. Therefore, this paper aims to develop an enhanced fuzzy neural network (EFNN) based decision support system for managing automobile spares inventory in a central warehouse. In this system, the EFNN is utilized for forecasting the demand for spare parts. However, without considering relevant domain knowledge, traditional neural networks are found to be suffered from the problem of low accuracy of forecasting unseen examples. Therefore, in our EFNN, the following improvement is made: First, it assigns connection weights based on the fuzzy analytic hierarchy process (AHP) method without painstakingly turning them. Second, by generating and refining activation functions according to genetic algorithm, our EFNN can provide comprehensive and accurate activation functions and fit a wider range of nonlinear models. Last, but not least, an adaptive input variable is introduced to decrease the impact of the bullwhip effect on the forecasting accuracy. The proposed system is evaluated with the real word data and experimental results indicate that our EFNN outperforms other five models in fill rate and stock cost measures.
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paper_title: Fuzzy-Petri-net-based disassembly planning considering human factors

paper_content:

Disassembly, as the process of systematic removal of desirable constituent parts from an assembly, is of growing importance due to the increasing environmental and economic pressure. Although disassembly in practice is manual and labor intensive, little attention has been paid to the human intervention in the disassembly process. This paper addresses this deficiency by developing a fuzzy attributed Petri net (FAPN) model to mathematically represent uncertainty in disassembly due to a large amount of human intervention. An algorithm based upon this model is further proposed for optimal disassembly planning with a view to making the technique more applicable to real industry settings. The benefit of the proposed model and algorithm is illustrated through the disassembly of a personal computer (PC) in a prototypical disassembly system
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paper_title: Heuristics for solving disassemble-to-order problems with stochastic yields

paper_content:

Within the realm of reverse logistics, remanufacturing has become renowned as a popular option in many reverse logistics settings. In remanufacturing, firms take back products at the end of their use, disassemble them to obtain components, and reassemble these components into a “good as new” remanufactured product. As a process, remanufacturing requires parts as an input, parts which are gained mostly from recovered products. As the quality of the returned products are not known in advance, likewise the amount of good quality parts recovered from the returned product is subject to uncertainty. In this paper, we develop two heuristics of different sophistication which take into consideration that the yields of disassembly are stochastic. The methodology is further illustrated with a numerical example, and performance of the heuristics is examined through a performance study. The performance study indicates excellent performance for the more sophisticated heuristic, but also reveals under which conditions the more simple heuristic can be recommended for application.
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paper_title: Economical green product design based on simplified computer-aided product structure variation

paper_content:

Environmental issues have become an imperative concern for most companies in relation to modern product development. Special procedures have to be taken during the product development process to comply with recent green directives. Product structure is recognized as a critical factor that provides effective means for reducing environmental impact in product end-of-life. However, most previous studies failed to leverage the vast latitude at the design stage due to the assumption of a fixed product structure. To overcome this deficiency, we propose a CAD-based approach that allows automatic variation of 3D product structure by means of changing the combination of parts, selecting the assembly method, and rearranging the assembly sequence. A computing scheme uses Genetic Algorithm (GA) techniques to produce an optimal product structure from the design alternatives generated by the approach. This corresponds to lower assembly/disassembly costs, while complying with specified recycling and recovering rates. The scheme also chooses a smaller set of parts that needs to be disassembled and determines an economical disassembly process. Implemented in a commercial CAD system, the test results demonstrated the effectiveness of this scheme in green product design in a cost-effective manner.
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paper_title: Disassembly sequencing using genetic algorithm

paper_content:

At the end-of-life (EOL) of a product, there are several options available for its processing including reuse, remanufacturing, recycling and disposing. In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as the reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly sequencing be developed. In this paper, we present a genetic algorithm for disassembly sequencing of EOL products. A case example is considered to demonstrate the functionality of the algorithm.
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paper_title: Optimal Disassembly Sequence Using Genetic Algorithms Considering Economic and Environmental Aspects

paper_content:

In this paper, a genetic algorithm (GA)-based approach for an optimal disassembly sequence considering economic and environmental aspects is presented. All feasible disassembly sequences are generated by a disassembly tree or an AND/OR graph. Using the disassembly precedence and the disassembly value matrix, a disassembly sequence is optimised. The precedence of disassembly is determined through a disassembly tree or an AND/OR graph and the value of disassembly is induced by considering both economic and environmental aspects in the disassembly, recycling, and disposal phases. Economic and environmental factors can be compared by the same measure through converting environmental factors into economic cost. To solve the disassembly sequence problem, a heuristic algorithm based on GAs is developed. The proposed GA can search for and dynamically explore the disassembly node through the highest disassembly value, keeping their precedence in order to identify an optimal disassembly sequence. It can also help to explore the search space, and an optimal solution can be obtained by applying the optimisation criteria. A refrigerator is used as an example to illustrate the procedure.
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paper_title: Production?remanufacturing control problem for defective item under possibility constrains

paper_content:

This paper presents a new type of an optimal control production-recovery inventory system under inflation and discounting in fuzzy-stochastic environment. The product defectiveness is a random variable. The return products are the used products collected from the open market. The defective product is treated as return product. Return and defective products are either remanufactured or disposed of. Remanufactured product is used as a new product which can be sold to the customer. The demand depends upon the stock of serviceable product, time and selling price. In this paper, we propose an optimal control approach to optimise the production, remanufacturing and disposal strategy with respect to dynamic demand and return. The upper bounds of production, remanufacturing and disposal are fuzzy parameters. The optimum results are presented in both tabular and graphical forms.
---
paper_title: Heuristic genetic algorithm for capacitated production planning problems with batch processing and remanufacturing

paper_content:

Abstract In this paper, we analyze a version of the capacitated dynamic lot-sizing problem with substitutions and return products. Both batch manufacturing and batch remanufacturing are considered within the framework of deterministic time-varying demands in a finite time horizon, where the option of emergency procurement/outsourcing subject to a subcontract is also allowed. Setup costs are taken into account when batch manufacturing or batch remanufacturing takes place. We first apply a genetic algorithm to determine all periods requiring setups for batch manufacturing and batch remanufacturing, then develop a dynamic programming approach to provide the optimal solution to determine how many new products are manufactured or return products are remanufactured in each of these periods. The objective is to minimize the total cost, including batch manufacturing, batch remanufacturing, emergency procurement, holding and setup costs. Numerical examples illustrate the effectiveness of the approach.
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paper_title: Environmentally conscious design by using fuzzy multi-attribute decision-making

paper_content:

Products affect the environment at many points in their life cycles. Once a product moves from the drawing board into the production line, its environmental attributes are largely fixed. Many researchers have focused on developing intelligent systems to provide a variety of design and manufacturing information to help designers make environmentally conscious decisions. However, in the early design stage, not all the information available is precise. A large amount of information, especially those that are based on designer experience, is fuzzy in nature. This paper presents an innovative method, namely green fuzzy design analysis (GFDA), which involves simple and efficient procedures to evaluate product design alternatives based on environmental consideration using fuzzy logic. The hierarchical structure of environmentally conscious design indices was constructed using the analytical hierarchy process (AHP), which include five aspects: (1) energy, (2) recycling, (3) toxicity, (4) cost, and (5) material. After weighting factors for the environmental attributes are determined, the most desirable design alternative can be selected based on the fuzzy multi-attribute decision-making (FMADM) technique. The benefit of using such a technique is to effectively solve the design problem by capturing human expertise.
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paper_title: A hybrid simulation optimization method for production planning of dedicated remanufacturing

paper_content:

This paper presents a hybrid cell evaluated genetic algorithm (CEGA) for optimization of the dedicated remanufacturing system with simulation. The paper first summarizes the special characteristics and problems of the dedicated remanufacturing. The paper then proposes a simulation model with a prioritized stochastic batch arrival mechanism, considering factors that affect the total profit. Based on the simulation model, the CEGA algorithm is developed to optimize the production planning and control policies for dedicated remanufacturing. A case study is provided based on the remanufacturing facility located at Austin, USA
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paper_title: Machine learning approach for determining feasible plans of a remanufacturing system

paper_content:

Resource planning for a complex remanufacturing system is in general extremely difficult in terms of, e.g., problem size and uncertainties. In many cases, simulation is the only way to select a good plan among a great number of candidates. When there exist complicated constraints, direct selection could be very inefficient since many candidates may not be feasible but cannot be excluded beforehand. To meet the challenge, a machine learning method is introduced in this paper to perform feasibility analysis. The rough set theory is first applied to establish the relationship between a plan and its feasibility and an iterative reinforcement process is applied to enhance confidence. The numerical testing results show that this method is promising and scalable for the large-scale problems. The research lays a basis for developing an efficient simulation-based optimization method with complicated constraints. Note to Practitioners-This paper was motivated by the resource planning problem for a complex remanufacturing system, which is very important but in general extremely difficult to deal with in terms of, e.g., problem size and uncertainties. Simulation is probably the only way available to select a good plan among a number of candidates. When there exist complicated constraints, simulation becomes even more difficult and selection through simulation could be very inefficient since many candidates may not be feasible but cannot be excluded before simulation. Determining feasibility beforehand is extremely difficult by analytical or numerical methods. This paper suggests a new method using a machine learning-based approach to predict the plan feasibility required in practical applications and can be considered as the first step for optimization-based planning. By applying the rough set theory, the prediction rules are obtained or learned from a training dataset generated by simulation. Then, an iterative reinforcement process is applied to enhance the confidence of learning and to perform iterative retraining on new datasets by the rough set method to generate new rules to add to the knowledge base until the preset threshold is satisfied. The numerical testing results show that the above method is capable of determining the feasible plans for a remanufacturing system with good accuracy. The method is efficient and scalable for the large-scale problems. The method developed in the paper is being incorporated in the framework of ordinal optimization, and a new constrained ordinal optimization method has been developed for remanufacturing planning.
---
paper_title: A production-inventory model with remanufacturing for defective and usable items in fuzzy-environment

paper_content:

This paper investigates a production-remanufacturing system for a single product over a known-finite time horizon. Here the production system produces some defective units which are continuously transferred to the remanufacturing unit and the constant demand is satisfied by the perfect items from production and remanufactured units. Remanufacturing unit uses the defective items from production unit and the collected used-products from the customers and later items are remanufactured for reuse as fresh items. Some of the used items in the remanufacturing unit are disposed off which are not repairable. The remanufactured units are treated as perfect items. Normally, rate of defectiveness varies in a production system and may be approximated by a constant or fuzzy parameter. Hence, two models are formulated separately with constant and fuzzy defective productions. When defective rate is imprecise, optimistic and pessimistic equivalent of fuzzy objective function is obtained by using credibility measure of fuzzy event by taking fuzzy expectation. Here, it is assumed that remanufacturing system starts from the second production cycle and after that both production and remanufacturing units continue simultaneously. The models are formulated for maximum total profit out of the whole system. Here the decision variables are the total number of cycles in the time horizon, the duration for which the defective items are collected and the cycle length after the first cycle. Genetic Algorithm is developed with Roulette wheel selection, Arithmetic crossover, Random mutation and applied to evaluate the maximum total profit and the corresponding optimum decision variables. The models are illustrated with some numerical data. Results of some particular cases are also presented.
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paper_title: Intelligent decision making in disassembly process based on fuzzy reasoning Petri nets

paper_content:

Practical disassembly process planning is extremely important for efficient material recycling and components reuse. The research work for the process planning in literature focuses on the generation of optimal sequences based on the predictive information of products. The used products, unfortunately, exhibit high uncertainty since products may experience very different conditions during their use stage. The indeterminate characteristics associated to used products often makes the predetermined plan unrealistic. Their disassembly process has to be decided dynamically adaptive to the products' specific status. To be able to deal with uncertainty in a dynamic decision making process, this paper presents a fuzzy reasoning Petri net (FRPN) model to represent related decision making rules in disassembly process. Using the proposed fuzzy reasoning algorithm based on the FRPN model, the multicriterion disassembly rules can be considered in the parallel way to make the decision automatically and quickly. Instead of producing the disassembly sequences before disassembling a whole product, the proposed method makes intelligent decisions based on dynamically updated status of components in the product at each disassembly step. Therefore, it is adaptive to the changes that arise during the process. Finally, an example is used to illustrate the application of the proposed methodology.
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paper_title: Evolutionary sequence planning for selective disassembly in de-manufacturing

paper_content:

Selective disassembly takes apart the selected components from a product and is an important process of de-manufacturing (DM). Selective disassembly aims to minimize the effort required to separate parts selected for separation or replacement. Unlike assembly or disassembly of a product, selective disassembly allows a partial and non-sequential disassembly procedure, regardless of the assembly indenture level. Research has been towards an optimal selective-disassembly plan for the reduction of product life cycle costs or environmental concerns. However, the existing research ignores two important issues in selective disassembly: batch disassembly of components; and tool accessibility to fasteners in a non-procedural and partial disassembly. This paper presents an evolutionary sequence planning for selective disassembly in DM. Two examples presented demonstrate that the evolutionary approach is efficient for DM. The approach generates a feasible and optimal plan for selective disassembly, ensuring both batch disassembly of components and tool accessibility to fasteners.
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Title: Soft Computing in Product Recovery: A Survey Focusing on Remanufacturing System
Section 1: Introduction
Description 1: Introduce the concept of remanufacturing, outline the lack of existing surveys on soft computing techniques applied to reman, and delineate the research questions this paper aims to answer.

Section 2: The Role of Reman in Product Recovery
Description 2: Discuss the significance of remanufacturing in product recovery, highlighting its environmental and economic benefits.

Section 3: Survey Methodology
Description 3: Explain the methodology used for the literature survey, including databases searched, keywords used, and criteria for paper selection.

Section 4: Results of Literature Survey on Reman System
Description 4: Present the results of the literature survey, categorized into key areas such as disassembly and reman. Each category should be subdivided into specific topics such as sequencing, planning, product design, production planning & scheduling, and inventory management.

Section 5: Conclusions
Description 5: Summarize the key findings of the survey, identify gaps in existing research, and suggest directions for future research in the application of soft computing techniques in remanufacturing.