Disassembly Sequence Planning Research Articles

Multiobjective Human–Robot Collaborative Disassembly Sequence Planning: Considering the Properties of Components

Multiobjective Human–Robot Collaborative Disassembly Sequence Planning: Considering the Properties of Components

A novel reinforcement learning framework for disassembly sequence planning using Q-learning technique optimized using an enhanced simulated annealing algorithm

Abstract The increase in Electrical and Electronic Equipment (EEE) usage in various sectors has given rise to repair and maintenance units. Disassembly of parts requires proper planning, which is done by the Disassembly Sequence Planning (DSP) process. Since the manual disassembly process has various time and labor restrictions, it requires proper planning. Effective disassembly planning methods can encourage the reuse and recycling sector, resulting in reduction of raw-materials mining. An efficient DSP can lower the time and cost consumption. To address the challenges in DSP, this research introduces an innovative framework based on Q-Learning (QL) within the domain of Reinforcement Learning (RL). Furthermore, an Enhanced Simulated Annealing (ESA) algorithm is introduced to improve the exploration and exploitation balance in the proposed RL framework. The proposed framework is extensively evaluated against state-of-the-art frameworks and benchmark algorithms using a diverse set of eight products as test cases. The findings reveal that the proposed framework outperforms benchmark algorithms and state-of-the-art frameworks in terms of time consumption, memory consumption, and solution optimality. Specifically, for complex large products, the proposed technique achieves a remarkable minimum reduction of 60% in time consumption and 30% in memory usage compared to other state-of-the-art techniques. Additionally, qualitative analysis demonstrates that the proposed approach generates sequences with high fitness values, indicating more stable and less time-consuming disassembles. The utilization of this framework allows for the realization of various real-world disassembly applications, thereby making a significant contribution to sustainable practices in EEE industries.

Selective disassembly sequence planning under uncertainty using trapezoidal fuzzy numbers: A novel hybrid metaheuristic algorithm

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA–II–SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA–II–SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings.

A novel MILP model and an improved genetic algorithm for disassembly line balancing and sequence planning with partial destructive mode

Collaborative optimization of disassembly line balancing and disassembly sequence planning can effectively reduce ineffective operation time. Considering the uncertainty of the connection relationship between parts of end-of-life products, a partial destructive disassembly mode based on the disassembly feasibility is introduced. In this study, a mixed integer linear programming (MILP) model of disassembly line balancing and sequence planning (DLBSP) is constructed. The aim is to minimize the number of stations, smoothness index, and energy consumption, and maximize the disassembly profit simultaneously. To obtain high-quality disassembly schemes, a multi-objective improved genetic algorithm is proposed. An encoding and decoding strategy based on problem characteristics is designed to improve the quality of the initial solutions. The feasibility of crossover and mutation operations is ensured by introducing precedence constraints, and a single-point insertion neighborhood strategy is designed to improve the local search ability of the algorithm. Then, the effectiveness of the proposed model and algorithm is verified in a small-scale case, and the performance of the proposed algorithm is better than that of nine meta-heuristics through 22 test cases. Next, the proposed model and algorithm are applied to a real CRT TV disassembly case. It is shown that the new disassembly scheme can reduce two stations, improve smoothness by 99.83%, increase profits by 89.75%, and reduce energy consumption by 24.66% compared to the original scheme. Finally, a DLBSP optimization system is developed, which can reduce the application difficulty of the proposed model and algorithm.

Multi-Objective Disassembly Sequence Planning in Uncertain Industrial Settings: An Enhanced Water Wave Optimization Algorithm

Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes.

An improved disassembly hybrid graph model for selective disassembly sequence planning

Disassembly is an inevitable step in the recovery of valuable materials from end-of-life (EoL) mobile phones. Although a Hybrid Graph is one of the prevalent method for disassembly modeling, it cannot accurately describe the subassembly disassembling process. In this paper, an improved disassembly hybrid graph model (IDHGM) for selective disassembly sequence planning is proposed for the limitations of the subassembly disassembling process of EoL mobile phones. The containment, exclusion, and subassembly are considered in view of the close connection of mobile phone parts and the occurrence of interlocking between parts. Based on the different disassembly sequences of the same part, a time cell array is used to calculate the disassembly time. The disassembly sequence is optimized by the ant colony algorithm and disassembly time is set as the optimization objective. “Honor 6” mobile phone was selected as a case study to demonstrate the feasibility of the proposed model. The results show that the disassembly sequence generated by IDHGM takes less disassembly time and can accurately describe the subassembly disassembling process.

Multi-objective multi-fidelity optimisation for position-constrained human-robot collaborative disassembly planning

ABSTRACT Human-robot collaborative disassembly lines are widely used by remanufacturing companies to disassemble end-of-life (EOL) products. When disassembling large-sized EOL products, each workstation on a disassembly line is generally divided into multiple operating positions, so that different operators can disassemble the same product at their respective positions at the same time, thereby greatly improving efficiency. This paper focuses on a position-constrained human-robot collaborative disassembly planning (PC-HRCDP) problem for the above-mentioned lines, including three subproblems of disassembly sequence planning, disassembly line balancing and robot path planning. A multi-objective mixed integer programming model for PC-HRCDP is developed to solve small-scale instances. Furthermore, a multi-objective multi-fidelity optimisation (MO-MFO) algorithm is proposed to solve large-scale instances. Comprehensive experiments are conducted based on 10 problem instances generated in this study. Experimental results show that the proposed MO-MFO is better than a high-fidelity optimisation algorithm in terms of running time. In addition, benefiting from the strategy of MO-MFO to allocate the limited high-fidelity computational budget to solutions in the two stages of multi-objective optimisation and optimal sampling, MO-MFO is significantly better than the existing representative multi-fidelity optimisation algorithms in terms of the hyper-volume and the inverted generational distance.

Equipment Disassembly and Maintenance in an Uncertain Environment Based on a Peafowl Optimization Algorithm

Disassembly sequence planning (DSP) is a key approach for optimizing various industrial equipment-maintenance processes. Finding fast and effective DSP solutions plays an important role in improving maintenance efficiency and quality. However, when disassembling industrial equipment, there are many uncertainties that can have a detrimental impact on the disassembly and subsequent maintenance work. Therefore, this paper proposes a multi-objective DSP problem in an uncertain environment that addresses the uncertainties in the disassembly process through stochastic planning, with the objectives of minimizing disassembly time and enhancing responsiveness to priority maintenance components. Due to the complexity of the problem, an improved peafowl optimization algorithm (IPOA) is proposed for efficient problem-solving. The algorithm is specifically designed and incorporates four customized optimization mechanisms: peafowls’ courtship behavior, the adaptive behavior of female peafowls in proximity, the adaptive search behavior of peafowl chicks, and interactive behavior among male peafowls. These mechanisms enable effective search for optimal or near-optimal solutions. Through comparisons with a real-world industrial case and other advanced algorithms, the superiority of the IPOA in solving DSP problems is demonstrated. This research contributes to improving maintenance efficiency and quality, bringing positive impacts to industrial equipment maintenance.

A multiobjective decision-making approach for modelling and planning economically and environmentally sustainable robotic disassembly for remanufacturing

Adopting initiatives to extend the useful life of products or recovering their components for reuse, remanufacturing or recycling is a key issue in the attempt to protect the environment and minimise the demand for natural resources. To contribute to the performance of automated disassembly practices, this paper presents a multiobjective decision-making approach based on the optimisation of three goals in a robotic disassembly cell framework: enhancing the economic performance of the process, reducing energy consumption and mitigating the environmental impact. Two real-use cases are presented as demonstrators, supported by appropriate, updated information from industry. The design model allowed the authors to obtain the best robotic disassembly sequence plan, the correct disassembly direction, the best recovery option for the disassembled components – reuse, remanufacturing, recycling or disposal – and the most appropriate disassembly tools, finding the optimal or near-optimal solution that best balances the three sustainability goals. An Enhanced Discrete Bees Algorithm with a mutation operator was employed to find the solution for the optimisation. Moreover, a multiobjective Bees Algorithm, a Non-dominated Sorting Genetic Algorithm II and a Pareto Envelope-based Selection Algorithm II were adopted to solve the multiobjective optimisation problem using different iteration numbers and population sizes. The results provide insights into robotic disassembly processes, encouraging firms to adopt more automated and sustainable remanufacturing strategies.

Integrating X-reality and lean into end-of-life aircraft parts disassembly sequence planning: a critical review and research agenda.

In parallel with the fast growth of the second-hand aviation market, the importance of promoting remanufacturing analytics has increased. However, end-of-life (EoL) aircraft parts remanufacturing operations are still underdeveloped. Disassembly, the most challenging and central activity in remanufacturing, directly affects the EoL product recovery's profitability and sustainability. Disassembly sequence planning (DSP) devises ordered and purposeful parting for all potentially recoverable components before physical separations. However, the complexities and uncertainties of the EoL conditions engender unpredictable DSP decision inputs. The EoL DSP needs emergent evidence of cost-effective solutions in view of Industry 4.0 (I4.0) implications and stakeholders' benefits. Among the I4.0 technologies, X-reality (XR) particularly hits the mainstream as a cognitive and visual tool consisting of virtual reality, augmented reality, and mixed reality. Recently, with the advance of I4.0 phenomenon, lean management has been theorized and tested through complementary collaboration. Since the research of integrating lean and XR into the EoL DSP is underexplored in literature, XR and lean are investigated as assistive enablers in the DSP. This study has a two-fold purpose: (1) identifying the key concepts of DSP, I4.0, XR, and lean, and extending the literature by reviewing the previous efforts of EoL aircraft remanufacturing, XR-assisted DSP, and XR-lean applications; (2) proposing "Smart Disassembly Sequence Planning (SDSP)" as a new EoL decision-support agenda after analyzing relational advantages and evolving adaptability. The barriers and limitations are highlighted from the recent associated topics, concrete academic information for developing digitalized disassembly analytics is provided, and new trends are added for future disassembly research.

Disassembly Sequence Planning of Product Structure With an Improved QICA Considering Expert Consensus for Remanufacturing

Planning a disassembly sequence of product structure in a remanufacturing-oriented manner is a newly emerging and important problem, which has attracted considerable attention owing to high demand for sustainable development. However, current approaches cannot lead to a reliable disassembly plan focused on the product structure itself, starting from functional properties to application demand stimulated by remanufacturing. First, they fail to focus on the role that end-of-life (EOL) products play in the promotion of remanufacturing. Second, determining a disassembly solution for advanced and sophisticated products is complicated and time-consuming. Third, the uncertainty of mixed information triggered by the intercrossing of multiple subjects is neglected. In this study, an improved disassembly sequence planning (DSP) method is firstly proposed by integrating the remanufacturing properties of EOL products, an improved quantum immune clonal algorithm (QICA), and the measure and update of the reliability of experts' decision-making to perform DSP. The functional recoverability of EOL product parts is marked to identify the remanufacturing value of a disassembly plan. The expected reproduction probability is adopted to increase population diversity and improve the ability of QICA to search for feasible solutions. The model and update mechanism of experts' recognition degree are constructed to narrow the score deviation. Finally, we provide a case for a sewing machine to verify the superiority of the improved QICA. The results show that it is capable of obtaining satisfactory solutions and outperforms the quantum genetic algorithm and particle swarm optimizer.

Selective disassembly sequence optimization based on the improved immune algorithm

PurposeThe purpose of this paper is to improve the automation of selective disassembly sequence planning (SDSP) and generate the optimal or near-optimal disassembly sequences.Design/methodology/approachThe disassembly constraints is automatically extracted from the computer-aided design (CAD) model of products and represented as disassembly constraint matrices for DSP. A new disassembly planning model is built for computing the optimal disassembly sequences. The immune algorithm (IA) is improved for finding the optimal or near-optimal disassembly sequences.FindingsThe workload for recognizing disassembly constraints is avoided for DSP. The disassembly constraints are useful for generating feasible and optimal solutions. The improved IA has the better performance than the genetic algorithm, IA and particle swarm optimization for DSP.Research limitations/implicationsAll parts must have rigid bodies, flexible and soft parts are not considered. After the global coordinate system is given, every part is disassembled along one of the six disassembly directions –X, +X, –Y, +Y, –Z and +Z. All connections between the parts can be removed, and all parts can be disassembled.Originality/valueThe disassembly constraints are extracted from CAD model of products, which improves the automation of DSP. The disassembly model is useful for reducing the computation of generating the feasible and optimal disassembly sequences. The improved IA converges to the optimal disassembly sequence quickly.

Environment-oriented disassembly planning for end-of-life vehicle batteries based on an improved northern goshawk optimisation algorithm.

Due to environmental pollution and resource shortages, the electric vehicle industry has been developing swiftly, and the market demand for batteries, as an essential part of electric vehicles, has also surged. Proper disassembly of end-of-life vehicle batteries (ELV batteries) is necessary to achieve the integrity and closure of their life cycle, promote the development of green remanufacturing, effectively reduce the pollution of the environment caused by metal ion leakage, and reduce people's dependence on natural resources to a certain extent. To schedule the disassembly operations of ELV batteries more rationally and further promote their disassembly quality and efficiency, this paper proposes a dual-objective disassembly sequence planning (DSP) optimisation model, which aims to minimise the hazard index and energy cost during ELV battery disassembly operations. Since the proposed model is a complex NP-hard optimisation problem, this study develops an efficient metaheuristic algorithm for solving this model based on the northern goshawk optimisation algorithm. The main algorithm adds two types of discrete recombination operators and a local search operator. At the same time, the predatory behaviour of the goshawk is optimised by combining the characteristics of the disassembly sequence planning problem to improve its performance. Finally, the disassembly of the battery of a Tesla Model 1 is used as a case study to demonstrate the effectiveness and feasibility of the proposed method.

An Efficient Disassembly Sequencing Scheme Using the Shell Structure

This paper presents a part accessibility-based assembly partitioning scheme for efficient disassembly sequence planning. Accessibility of parts is shown to be an important consideration in product disassembly sequence planning (DSP) , which in turn has applications in assembly planning (AP) , maintenance planning (MP) , and end-of-life (EOL) processing. A substantial amount of research exists on both DSP and MP . However, accessibility analysis of parts in an assembly remains largely unexplored. In this paper, we introduce the notion of shell structures associated with 3d CAD assemblies for accessibility analysis of parts. The proposed approach classifies the surface domains of parts as mating or non-mating domains. The non-mating domains are used to construct the shell structure, and the mating domains are used to generate the liaison information of the assembly. The method requires only one-time computation of the shells; throughout the disassembly process, the accessibility information in the shells is updated without any recomputation. Further applications of shell structures, such as in subassembly identification and parallel disassembly, which are important for maintenance planning and end-of-life processing, are also demonstrated. We propose a grid-based method for constructing the shell structure from the tessellated model of the assembly. Finally, results obtained for tessellated models demonstrate the efficacy of the proposed methods. The results show that the accessibility assessment can be done in very little time using the proposed approach. The dynamic update of shells after each disassembly operation results in efficient updating of accessibility information. • A part accessibility-based assembly partitioning scheme is proposed for efficient disassembly sequence planning. • The notion of shell structure is introduced for carrying out accessibility analysis of parts from the CAD file of the assembly. • The application of shell structures for subassembly identification and parallel disassembly is demonstrated. • Methods are presented for constructing the shell structure from the tessellated model of the assembly.

An improved multi-objective evolutionary algorithm for multiple-target asynchronous parallel selective disassembly sequence planning

The product’s service life has been shortened rapidly along with the development of modern technologies and human esthetic evolutions, resulting in a great number of end-of-life (EOL) products. Disassembling renewable parts from EOL products for remanufacturing is of great significance to save resources and protect the environment. The existing studies on selective disassembly sequence planning (SDSP) largely focus on the sequential disassembly planning, which is inefficient especially for complex products because it is a linear process where only one component can be removed at a time. Thus, this work focuses on parallel SDSP and further proposes a multiple-target asynchronous parallel selective DSP (APSDSP) with the objectives of minimizing disassembly time and maximizing disassembly profit simultaneously. In APSDSP, operators can remove multiple components simultaneously as long as disassembly constraints are not violated, and without synchronization requirement between operators. The feasible disassembly sequence and disassembly direction sequence are generated by a space interference matrix method (SIMM) to meet the actual disassembly environment. Based on SIMM, an improved multi-objective evolutionary algorithm based on multiple neighborhood search strategy is developed to create the pareto frontier of the problem. Finally, two cases study are presented to validate the effectiveness of the proposed methodology. It gives enterprises new insights to reduce disassembly time and improve disassembly profit.

Human–robot collaborative partial destruction disassembly sequence planning method for end-of-life product driven by multi-failures

Partial destructive disassembly (PDD) of large complex products often requires human–robot collaborative disassembly (HRCD). Therefore, a human–robot collaboration partial destructive disassembly sequence planning (HRCPDDSP) method of end-of-life products driven by multiple failures is proposed to obtain the optimal disassembly sequence to improve disassembly efficiency and degree of automation. Based on the product disassembly priority and part failure characteristics, an information model of the HRCPDDSP method is constructed. Furthermore, the model is expressed as a constraint matrix and recycling decision matrix. A multi-layer chromosome coding method, including HRCD, destructive constraint, and node layers, is proposed of the characteristics of the HRCPDDSP method. The approximate optimal sequence of human–robot collaboration PDD is achieved by improving evolutionary mechanisms such as selection, crossing, and mutation. Finally, the model and algorithm are applied to solve a case of HRCPDDSP, and the feasibility and effectiveness of the proposed method are further verified by comparison with other disassembly modes.

A two-stage interactive evolutionary algorithm for multi-objective asynchronous parallel selective disassembly sequence planning problem

The existing studies of selective disassembly sequence planning (SDSP) focus on sequential SDSP, which is inefficient especially when disassembling complex products. Moreover, energy efficiency has not received much attention in SDSP, which is the critical manifestation of environmental impact. Thus, this work focuses on parallel SDSP and further proposes a profit-oriented and energy-efficient multi-objective asynchronous parallel SDSP (PEAPSDSP) for optimizing disassembly time, energy consumption, and disassembly profit simultaneously. In PEAPSDSP, multiple components can be removed simultaneously and without synchronization requirement. Then, a two-stage interactive multi-objective evolutionary algorithm (TS-IMOEA) is proposed to address PEAPSDSP. The potential non-dominated solutions (NDSs) are obtained in first stage, and a novel search strategy is designed to further improve the quality of NDSs and population quality in second stage. Numerical results in three different scale cases demonstrate the superiority of TS-IMOEA in solving PEAPSDSP, and the benefit of considering selective disassembly in value recovery.

A self-evolving system for robotic disassembly sequence planning under uncertain interference conditions

• A fuzzification of disassembly sequence planning (FDSP) method is proposed. • FDSP offers the capability for DSP to adapt to failures and re-planing online. • The dual-loop self-evolving framework can cope with uncertain interference conditions. Robotic disassembly sequence planning (DSP) is a research area that looks at the sequence of actions in the disassembly intending to achieve autonomous disassembly with high efficiency and low cost in remanufacturing and recycling applications. A piece of key input information being factored in DSP is the interference condition of a product, i.e., a mathematical representation of the spatial location of components in an assembly, usually in the form of a matrix. An observed challenge in the area is that the interference condition can be uncertain due to variations in the end-of-life conditions, and there is a lack of tools available in DSP under uncertain interference. To address this challenge, this paper proposes a new DSP method that can cope with uncertain interference conditions enabled by the fuzzification of DSP (FDSP) . This new approach in the core is a fuzzy and dynamic modeling method in combination with an iterative re-planning strategy, and FDSP offers the capability for DSP to adapt to failures and self-evolve online. Three products are given to demonstrate FDSP.

Disassembly assessment from CAD-based collision evaluation for sequence planning

Disassembly sequence planning is the foundation for disassembly process planning. Furthermore, integrating efficient geometrical disassembly feasibility of non-destructive operations can contribute to designing robotic disassembly and remanufacturing systems. Non-destructive operations increase the chance of repurposing parts with less cost but require collision-free trajectories between components. Thus, generating geometrically feasible disassembly sequences is critical for determining the optimal disassembly sequencing and disassembly trajectories. In this work, geometric collision data has generated to develop the disassembly precedence matrix that provides fundamental information for disassembly planning methods. Current methods often require significant manual resources to locate geometrically feasible sequences for disassembly of products. This paper describes the results of utilizing disassembly collision tests to automatically extract interference data from STEP CAD assembly files to determine feasible disassembly operations. A method to determine a precedence matrix that describes all geometrically feasible disassembly operations for disassembly of a selected part or a whole assembly to its components has been developed and tested. Extracted data from STEP files is used to characterize the spatial properties of a component in an assembly and is the basis for the collision test to determine the geometrically feasible disassembly operations. The precedence matrix also identifies the feasible disassembly directions for each component based on the outcomes of the collision tests.

Disassembly Sequence Planning for Green Remanufacturing Using an Improved Whale Optimisation Algorithm

Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve its performance. The proposed algorithm is validated with a worm reducer example and compared with other state-of-the-art and recent metaheuristics. Finally, the results confirm the high solution quality and efficiency of the proposed improved whale algorithm.