End-of-life (EOL) products contain many valuable parts and materials. The timely disassembly and recycling of EOL products can bring considerable economic benefits and reduce environmental pollution. To reduce the cost of manual disassembly, robotic disassembly has become one of the main methods used to dismantle EOL products. In addition, parallel disassembly can shorten the makespan of EOL products. Therefore, this article focuses on a new problem involving robotic parallel disassembly sequence planning for EOL products and establishes a corresponding multiobjective model to minimize the makespan and energy consumption. To obtain high-quality disassembly schemes, a discrete artificial bee colony algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by solving 32 benchmark problems and a comparison with three well-known multiobjective algorithms. The proposed model and method are applied to a real-world LCD TV disassembly case, and multiple preferred disassembly schemes are obtained. The results show that the proposed model and method can effectively shorten the makespan and reduce energy consumption.Note to Practitioners—Parallel disassembly is one of the most efficient disassembly methods, and robotic disassembly will be one of the main disassembly methods in the future. Therefore, this article focuses on the robotic parallel disassembly planning to achieve an efficient and environmentally friendly treatment of end-of-life products. With the importance of production efficiency and energy saving, it is essential to simultaneously assess the makespan and energy consumption. The results demonstrate that the proposed artificial bee colony algorithm can provide multiple disassembly schemes that balance the makespan and energy consumption. The obtained disassembly schemes can provide references for the disassembly planning of the disassembly enterprise and expand the decision-making space for decision-makers.

To achieve sustainable manufacturing of large-scale end-of-life products, disassembly for recycling and remanufacturing has been widely adopted by industries. Disassembly line balancing becomes an important and challenging issue. The disassembly efficiencies of workers are different in the actual disassembly line due to some factors, including disassembly environment, skill level, work enthusiasm, etc. However, efficiency differences are often ignored in previous studies, which ultimately lead to unbalanced workloads among stations. Therefore, this article establishes a disassembly line balancing model that considers workers with different efficiencies and introduces the bucket brigade model into the disassembly line. Its optimization objectives include workload smoothness, cost of workers, disassembly risk, and disassembly demand. To obtain high-quality solutions, a discrete flower pollination algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by comparing it with 11 algorithms. Finally, the proposed model and algorithm are applied to an actual television disassembly case considering workers with different efficiencies, and provide decision makers with multiple disassembly schemes.

Disassembly lines are the most effective way to address large-scale value recovery from end-of-life (EOL) products. Disassembly line balancing (DLB) greatly affects the economics and throughput of EOL product processing. Complete disassembly is generally not suitable for disassembly enterprises; most often, the maximum profit is realized through partial disassembly. Thus, this article proposes a partial disassembly method and establishes a new DLB model that addresses both economic benefits and environmental impacts. The objective of the model is to maximize the effectiveness of workers, increase profit, reduce energy consumption, and balance the loads of workers. Moreover, the model considers the impact of disassembly face and tool changes on the disassembly process. A discrete multiobjective artificial bee colony (MOABC) algorithm is developed, and it takes the precedence constraints into account to obtain the Pareto solutions. The MOABC algorithm is applied to the disassembly lines of two real-world EOL products, including those of an LCD TV and a refrigerator. Experiments show that the performance of the MOABC algorithm is better than those of five well-known multiobjective algorithms. The proposed model and method can provide multiple disassembly schemes for decision makers of disassembly enterprises based on their preferences.

In this paper, an isogeometric density field method is proposed for the design of micro-architected materials with the specific mechanical properties, consisting of the maximum bulk and shear modulus and the Negative Poisson's ratio (NPR). Firstly, the non-uniform rational B-splines (NURBS) basis functions are employed to construct the density field function (DFF), where the Shepard function is used to improve the smoothness of the nodal densities assigned to control points. The NURBS basis functions and the Shepard function can ensure the sufficient continuity and smoothness of the DFF, owing to their significant properties. The optimization formulation for micro-architected materials is developed using the DFF, where the isogeometric analysis (IGA) is applied to evaluate the unknown structural responses. Effective macroscopic properties of materials are predicted by the asymptotic homogenization method. The same NURBS basis functions used in the IGA and the DFF can keep the consistency of the geometric model and analysis model, which provides the unique benefits. Numerical examples are used to demonstrate the effectiveness of the proposed topology optimization approach for micro-architected materials.

Disassembly planning aims to search the best disassembly sequences of a given obsolete/used product in terms of economic and environmental performances. A practical disassembly process may face great uncertainty owing to various unpredictable factors. To handle it, researchers have addressed the stochastic cost and time problems of product disassembly. In reality, the uncertain environment of product disassembly is associated with both randomness and fuzziness. Besides uncertain disassembly cost and time, the quality of disassembled components/parts in a process has uncertainty and thus needs to be assessed via expert opinions/subjects. To do so, this paper presents a new AND/OR-graph-based disassembly sequence planning problem by considering uncertain component quality and varying disassembly operational cost. Important disassembly planning models are built on the basis of different disassembly criteria. A novel hybrid intelligent algorithm integrating fuzzy simulation and artificial bee colony is proposed to solve them. Its effectiveness is well illustrated through several numerical cases and comparison with a prior method, i.e., fuzzy-simulation-based genetic algorithm.

Three dimensional (3D) point clouds are typically used in computer vision and pattern recognition areas. In general, the raw point cloud has large numbers of redundant points which require excessively large storage space and lots of time for post-processing. This paper presents a synthetic point cloud simplification method to obtain computationally manageable point sets. First, a coarse-to-fine feature extraction manner is designed with normal vectors deviation and k-means clustering methods, which can concentrate more sample points in regions of high curvature. Additionally, the directed Hausdorff distance is performed directly on the point cloud which samples the point cloud judiciously with an edge-preserving manner. Experimental results demonstrate that the proposed method is effective for point cloud simplification, and it exhibited superior performance compared to existing techniques.

Range image registration is a popular problem in pattern recognition and computer vision, and it has a wide range of applications in real life. The objective of registration is to match two models as close as possible. In this area, the best known iterative closest point (ICP) method is sensitive to the initial position of two models and it is easy stuck in local minima. In recent years, heuristic algorithms have been used for registration with good ability for global searching. However, the features of models are ignored generally in the iterative evolution process, so the tailored methods are lack of versatility for different models registration. In this paper, normal angle histogram is added into the fruit fly optimization algorithm for registration. The searching step of each individual is relative to the initial position of two models. The versatility and effectiveness of proposed algorithm are illustrated by a series of experiments.

Range image registration is used to align two or more three dimensional (3D) point sets into a common coordinate system. In the matching process, however, the influence of the resolution of 3D scans is ignored generally. In this paper, an enhanced differential evolution (DE) algorithm is proposed to align two different scaling 3D point sets. Specifically, Generalized Procrustes Analysis (GPA) method is employed to accelerate the DE in population initialization. In addition, a novel mutation technique is introduced to improve the accuracy for registration. The proposed method can evaluate all of the transformation parameters synchronously and it is effective for both isotropic and anisotropic scaling registration problem. Experiment results reveal that the proposed algorithm is much superior to other methods in terms of accuracy and robustness for scaling registration problem.

Deciding an optimal location of a transportation facility and automotive service enterprise is an interesting and important issue in the area of facility location allocation (FLA). In practice, some factors, i.e., customer demands, allocations, and locations of customers and facilities, are changing, and thus, it features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic time and cost issues of FLA. A new FLA research issue arises when decision makers want to minimize the transportation time of customers and their transportation cost while ensuring customers to arrive at their desired destination within some specific time and cost. By taking the vehicle inspection station as a typical automotive service enterprise example, this paper presents a novel stochastic multiobjective optimization to address it. This work builds two practical stochastic multiobjective programs subject to stochastic demand, varying velocity, and regional constraints. A hybrid intelligent algorithm integrating stochastic simulation and multiobjective teaching-learning-based optimization algorithm is proposed to solve the proposed programs. This approach is applied to a real-world location problem of a vehicle inspection station in Fushun, China. The results show that this is able to produce satisfactory Pareto solutions for an actual vehicle inspection station location problem.

3D parts inspection can be conducted by comparing the ideal design geometry with the real measurement points. Since the design coordinate system is different from the measurement coordinate system, these measurement points should be registered to the design coordinate system first. In this research area, iterative closest point (ICP) is the best-known algorithm, however, in order to converge to the global minima, ICP needs the good initial parameter which is hard to get in the actual inspection process. In this research, a hybrid Cuckoo Search (CS) method is proposed to solve the registration problem and two different optimizing strategies based on CS are described. The proposed algorithm seems much superior to other algorithms in terms of accuracy and robustness. Experiment results show that the proposed algorithm is effective.

In this paper, we consider a dynamic job shop scheduling problem (DJSSP) with job release dates. In such a problem, jobs arrive over time and are unknown in advance and they can not be scheduled before their arrivals. We apply gene expression programming (GEP), a new search technique based on evolutionary principle, on the scheduling problem to automatically construct efficient scheduling rules (SRs), which can generate high-quality schedules for the problem. A novel encoding scheme is proposed which prevents the length of chromosomes from increasing dramatically with the increase of the size of scheduling problems. And a new decoding scheme is also proposed which transfers a GEP chromosome into a schedule for each problem instance. The proposed GEP-based method is valuated for its solution quality. According to computational experiment results, the method is proved to be able to construct effective SRs for DJSSP with job release dates.

Inter-enterprises workflow is by no means rare recently, which is more complex and dynamic than the intra-enterprise workflow. The Timed Colored Petri Nets (TCPN)-enhanced process-view approach implemented in our previous work provides an effective way to facilitate inter-enterprises workflow management. In this paper, the collaborative execution mechanisms have been developed for the TCPN-enhanced process-view approach based inter-enterprises workflow. The state dependencies mechanism is proposed to facilitate the collaborative execution between TCPN workflow models and process-view workflow models in participating enterprises. The Synchronization Points (SPs) mechanism is proposed to coordinate the execution between process-view workflow models of participating enterprises and the integrated process-view workflow models of the enterprise alliance.

Collaborative design is a complex process being carried out by distributed teams. Collaboration requires successful and efficient sharing of knowledge, negotiation, coordination and management of activities. In distributed environments, design members are moving to network forms through alliances and other collaborative relationships as a social network. This paper explores collaborative design process from a sociological viewpoint. A social factor-based architecture for collaborative design is investigated, with special attention given to patterns of social behavior and the organizations of those who participate in the design. Social factors thought to have played a role include perspectives, preference, attitudes, cognition, etc. The main contribution of this paper is to introduce a methodology for analyzing the social aspects of collaborative design. The paper concludes with directing attention to current challenges and recommendations for research.

A test-bed (HUST-CDMS) for collaborative product design and manufacturing is described in this paper. This test-bed is constructed by using a CORBA and MAS based system architecture, where diverse and heterogeneous design and manufacturing resources are MAS-based agentified and CORBA-based encapsulated as resource agents and subsequently integrated in the system. These resource agents communicate and interact with each other by means of agent interoperation and human-machine interaction to achieve a collaborative design and manufacturing environment, aiming at reducing the lead time and costs

Collaborative design can be characterized by different level constraints that describe basic elements of design process such as human, information and resource. In design, satisfied solutions to constraints must occur to arrive at a final design result. Few collaborative design systems exist for adequately representing constraints, and formally combining them from different disciplines. The main contribution of this paper is to introduce a methodology for analyzing the constraints in collaborative design. A hierarchy of fuzzy constraint networks is presented along with a methodology for creating interlinks between different levels of the hierarchy. The solution methodology is illustrated with an example. The paper concludes with directing attention to current challenges and recommendations for research