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A successful adaptive reuse process relies heavily on the strong performance of disassembly sequence planning (DSP), yet the studies in the field are limited to sequential disassembly planning (SDP). Since in sequential disassembly, one component or subassembly is removed with only one manipulator at a time, it can be a relatively inefficient and lengthy process for large or complex assemblies and cannot fully utilize the DSP benefits for adaptive reuse of buildings. This study aims to present a new hybrid method for the single-target selective DSP that supports both sequential and parallel approaches.

This study uses asynchronous parallel selective disassembly planning (aPDP) method, one of the newest and most effective parallel approaches in the manufacturing industry, to develop a parallel approach toward DSP in adaptive reuse of buildings. In the proposed method, three objectives (i.e. disassembly sequence time, cost and environmental impacts) are optimized using the Non-dominated Sorting Genetic Algorithm (NSGA-II).

The proposed method can generate feasible sequential solutions for multi-objective DSP problems as the sequence disassembly planning for buildings (SDPB) method, and parallel solutions lead to 17.6–23.4% time reduction for understudy examples. Moreover, in disassembly planning problems with more complex relations, the parallel approach generates more effective and time-efficient sequences.

This study introduces the parallel approach for the first time in this field. In addition, it supports both sequential and parallel approaches as a novel strategy that enables the decision-makers to select the optimum approach (i.e. either the parallel or the sequential approach) for DSP. Moreover, a metaheuristic method (i.e. NSGA-II) is adopted as the optimization tool with robust results in the field in which those heuristic methods have only been employed in the past.

This paper aims to develop path‐planning techniques that support a general selective disassembly planner in a virtual reality environment.

The paper presents an automatic selective disassembly planning and two path‐planning techniques that support it. The first one is based on single translations, while the second is based on the generation of a random search tree. The methods used have been adapted and modified from available robotic path‐planning methods for their use in disassembly path planning.

The paper finds that the proposed techniques are applicable to the automatic generation of disassembly sequences.

The paper provides an automatic tool that can be integrated in simulation software for the analysis and validation of disassembly operation.

Maintenance operations have a great impact in the security and life expectancy of any product. This is especially true for some applications such as aerospace that must pass rigorous security checking procedures. Geometric reasoning and virtual reality can help in reducing costs and design time by moving testing from physical mock‐ups to virtual ones.

The paper shows the integration of path‐planning techniques in automatic disassembly‐planning methods.

The purpose of this paper is to improve the automation of selective disassembly sequence planning (SDSP) and generate the optimal or near-optimal disassembly sequences.

The 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.

The 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.

All 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.

The 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.

This paper aims to develop methods for generating disassembly tasks for selective disassembly. The disassembly task contains the disassembly information, namely, disassembly direction, disassembly tool and selective disassembly sequence.

Ontology is adopted to represent the product, and ontology rules are used to represent the disassembly knowledge. A product ontology model (POM) is introduced on the basis of material, connection matrix and interference matrix. Two types of disassembly knowledge are taken into account, one is the disassembly knowledge of disassembly tool selection and the other is the disassembly knowledge of special connections. Based on the POM and the disassembly knowledge, decision support methods are designed to generate disassembly tasks.

A centrifugal pump is used to demonstrate the proposed methods, and the result shows that the methods work well.

The methods developed in this study are fundamental approaches. The ontology and the ontology rules can be extended with more disassembly knowledge.

The main contribution of this research is the development of methods for representing disassembly knowledge based on ontology rules and the decision support methods for generating disassembly tasks.

To improve the efficiency of end-of-life product’s disassembly process, this paper aims to propose a disassembly sequence planning (DSP) method to reduce additional efforts of removing parts when considering the changes of disassembly directions and tools.

The methodology has three parts. First, a disassembly hybrid graph model (DHGM) was adopted to represent disassembly operations and their precedence relations. After representing the problem as DHGM, a new integer programming model was suggested for the objective of minimizing the total disassembly time. The objective takes into account several criteria such as disassembly tools change and the change of disassembly directions. Finally, a novel hybrid approach with a chaotic mapping-based hybrid algorithm of artificial fish swarm algorithm (AFSA) and genetic algorithm (GA) was developed to find an optimal or near-optimal disassembly sequence.

Numerical experiment with case study on end-of-life product disassembly planning has been carried out to demonstrate the effectiveness of the designed criteria and the results exhibited that the developed algorithm performs better than other relevant algorithms.

More complex case studies for DSP problems will be introduced. The performance of the CAAFG algorithm can be enhanced by improving the design of AFSA and GA by combining them with other search techniques.

DSP of an internal gear hydraulic pump is analyzed to investigate the accuracy and efficiency of the proposed method.

This paper proposes a novel CAAFG algorithm for solving DSP problems. The implemented tool generates a feasible optimal solution and the considered criteria can help the planer obtain satisfactory results.

Maintenance disassembly that involves separating failed components from an assembly or system plays a vital role in line maintenance of civil aircraft, and it is necessary to have an effective and optimal sequence planning in order to reduce time and cost in maintenance. The purpose of the paper is to develop a more effective disassembly sequence planning method for maintenance of large equipment including civil aircraft systems.

The methodology involves the following steps: a component‐fastener graph is built to describe the equipment in terms of classifying components into two categories that are functional components and fasteners; interference matrix is developed to determine the removable component, and a disassembly sequence planning of functional components is proposed based on Dijkstra's algorithm; the disassembly sequence planning including fasteners is presented based on particle swarm optimization.

An application case, which takes the nose landing gear system of a regional jet as a study object, shows that the disassembly sequence planning method proposed in the paper can reduce the calculation complexity greatly, and its effectiveness is greater than that of a genetic algorithm‐based method, in most situations.

The method proposed herein can acquire the optimal maintenance disassembly sequence, which can reduce the cost and time for maintenance of large equipment.

A novel and effective disassembly sequence planning solution for maintenance of large equipment is presented, which can be applied to the line maintenance of civil aircraft.

To improve the efficiency and economy of electro-mechanical product's recycle process, this paper aims to propose a disassembly sequence planning (DSP) method to reduce additional efforts of removing extra parts in selectable disassembly.

The methodology has three parts, which includes a disassembly hybrid graphic model to describe the product disassembly information, an object inverse-directed method to optimize the disassembly design and a model reconstruction method to achieve a better DSP.

According to the disassembly cost criteria and the parameters of disassembly tools, the disassembly efficiency increases and the disassembly cost decreases due to the use of partial destructive mode compared with non-destructive mode. The proposed partial destructive DSP is more efficient and economical.

Partial destructive disassembly mode cannot be used for the flammable or explosive component in the procedure of the DSP optimization algorithm.

DSP of an electric corkscrew is analyzed to investigate the accuracy and efficiency of the proposed method.

This paper proposes a partial destructive disassembly based DSP method for product disassembly, which provides a new approach for the disposal of end-of-life products.

Design for adaptability and disassembly (DfAD) is an effective method to reduce construction and demolition waste generation, landfill loads and greenhouse gas generation; preserve natural resources; and increase environmental awareness in the construction industry. However, it is an underexplored strategy due to a lack of information about projects and a set of agreed guidelines to guide buildings deconstruction. This study aims to understand how DfAD can support the sector’s transition toward circularity.

Through an integrative literature review, this study analyzed the current publications and terminologies used, identified the main themes discussed and described the key criteria for integrating deconstruction in the building design stage.

The results showed that the term DfAD encompasses different ecodesign strategies and is concentrated in six major thematic categories (design and construction principles, tools for DfAD, components and connections for DfAD, barriers, drivers and guidelines for DfAD, existing building stock potential and selective deconstruction process). In total, 60 criteria were presented to guide the deconstruction of buildings, emphasizing standardization, modularization and prefabrication of materials and components as fundamental requirements.

The study highlighted the need to expand the knowledge and training of the design team, establish public policies and tax incentives and develop tools, methods, and circular indicators to enable the implementation of deconstruction strategies for buildings.

The purpose of this paper is to enhance the method developed by previous researchers. In addition to using the combined interference matrix, the combined connection matrix and the combined contact matrix of product components, the disassembly sequence matrix and the combined instability matrix with platform to evaluate instability of sub‐assemblies are built, and effects of changes of sub‐assembly disassembly directions or tools and the effect of gravity are considered to obtain the best disassembly sequence for a product with many components. A computer program is generated and results of two cases are compared with those of the available studies.

The methodology includes the combined interference matrix, the combined contact matrix and the combined connection matrix of components for a product. The combined instability matrix of sub‐assemblies, changes of sub‐assembly disassembly direction or tools, and the effect of gravity during operation are considered. The binary number system is used to simplify relations among components of a product.

This methodology enhances the existing method and software is generated. Results of two cases are compared and show the same optimum disassembly processes as those obtained from other researchers.

All matrices are defined by the directions of x, y and z with three axes perpendicular to each other. The computer program generated cannot be used for a product with components that must be disassembled in the directions different from the axes.

Two cases are used to investigate feasibility of the proposed methodology with the computer program generated. The first one is an electric drill, and the second one is a flash lighter.

The methodology described in this paper is feasible for study of disassembly processes of products. The software generated can be used to obtain the optimum disassembly process of products.

This paper aims to explore the emerging relationship between Industry 4.0 (I4.0) digital technologies (e.g. blockchain, Internet of Things (IoT) and artificial intelligence (AI)) and the construction industry’s gradual transition into a circular economy (CE) system to foster the adoption of circular economy in the construction industry.

A critical and thematic analysis conducted on 115 scientific papers reveals a noticeable growth in adopting digital technologies to leverage a CE system. Moreover, a conceptual framework is developed to show the interrelationship between different I4.0 technologies to foster the implantation of CE in the construction industry.

Most of the existing bodies of research provide conceptual solutions rather than developing workable applications and the future of smart cities. Moreover, the coalescence of different technologies is highly recommended to enable tracking of building assets’ and components’ (e.g. fixtures and fittings and structural components) performance, which enables users to optimize the salvage value of components reusing or recycling them just in time and extending assets’ operating lifetime. Finally, circular supply chain management must be adopted for both new and existing buildings to realise the industry's CE ambitions. Hence, further applied research is required to foster CE adoption for existing cities and infrastructure that connects them.

This paper investigates the interrelationships between most emerging digital technologies and circular economy and concludes with the development of a conceptual digital ecosystem to integrate IoT, blockchain and AI into the operation of assets to direct future practical research applications