Human-robot collaborative disassembly line balancing considering the safe strategy in remanufacturing

The recycling of discarded products is an integral part of resource utilization. A disassembly line balancing problem (DLBP) concerns the recycling and remanufacturing process of end-of-life (EOL) products. Disassembly time is affected by many factors, e.g., the precedence relationships among disassembly tasks, and the skills and learning abilities of disassembly workers. In this paper, we use Petri nets to specify a disassembly process and establish a mixed integer programming model to describe DLBP that considers the learning effect of human workers. We then propose a discrete fruit fly optimization algorithm to solve the proposed problem. By comparing its experimental results with other intelligent optimization algorithms’, its efficiency is verified.

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set.

The circular economy follows the principle of reducing resource usage and energy consumption, reusing usable resources including subassemblies and components in discarded or used products, and recycling usable materials. It is guided by saving resources, improving the utilization rate of resources, reducing pollution, and protecting an ecological environment. Effective product disassembly planning methods can improve recovery efficiency and promote the circular economy. However, the existing studies pay little attention to sequential dependency disassembly, which makes it difficult to implement the existing planning methods under the constraints of limited disassembly methods and tools. In this paper, a single-objective sequence-dependent disassembly line balancing problem (SDLB) is studied. This problem requires that disassembly tasks are assigned to a group of orderly disassembly workstations to obtain the near optimal solution while meeting a disassembly priority constraint. Because solution complexity increases with the number of parts in a product, an improved artificial bee colony method (IABC) is proposed to solve the problem. Through experiments and compared with a genetic algorithm, the effectiveness of the proposed algorithm is verified.

Generative Design (GD) is a design approach that uses algorithms to generate designs. This paper investigates the role of optimisation algorithms in GD process. We study how Pareto Fronts – a classical optimization algorithm output – help designers to browse the variety associated with a design problem. Thanks to the “splitting condition” from design theory, we show that valuable Pareto Fronts for designers are those that allow the exploration of a variety of design parameters without modifying substantially the performance of the designed solution. We call “Splitting Pareto Front” the Pareto Fronts that display this property and investigate how to generate them. We compare, on an electrical battery design problem, two optimization algorithms – NSGA-II and MAP-Elites – based on the design parameters variety they generate. Our results show that MAP-Elites generates Pareto Fronts that are more splitting than those generated by NSGA-II. We then discuss this result in term of the design process: which algorithm is best suited for which design task? We conclude with the importance for future research on Generative Design Algorithms (GDA) to study jointly the functioning of GDA and their expected contribution to the design process.

Software defined network (SDN) has shown significant advantages in numerous real-life aspects with separating the control plane from the data plane that provides programmable management for networks. However, with the increase in the network size, a single controller of SDN imposes considerable limitations on various features. Therefore, in networks with immense scalability, multiple controllers are essential. Specifying the optimal number of controllers and their deployment place is known as the controller placement problem (CPP), which affects the network's performance. In the present paper, a novel controller placement algorithm has been introduced using the advantages of nature-inspired optimization algorithms and network portioning. Firstly, the Manta Ray Foraging Optimization (MRFO) and Salp Swarm Algorithm (SSA) have been discretized to solve CPP. Three new operators comprising a two-point swap, random insert, and half points crossover operators were introduced to discretized the algorithms. Afterward, the resulting discrete MRFO and SSA algorithms were hybridized in a promoting manner. Next, the proposed discrete algorithm has been evaluated on six well-known software-defined networks with a different number of controllers. In addition, the networks have been chosen from various sizes to evaluate the scalability of the proposed algorithm. The proposed algorithm has been compared with several other state-of-the-art algorithms regarding network propagation delay and convergence rate in experiments. The findings indicated the effectiveness of the contributions and the superiority of the proposed algorithm over the competitor algorithms.

As resources become increasingly scarce and environmental demands grow, the recycling of products at the end of their lifecycle becomes crucial. Disassembly, as a key stage in the recycling process, plays a decisive role in the sustainability of the entire operation. Advances in automation technology and the integration of Industry 5.0 principles make the balance of human–robot collaborative disassembly lines an important research topic. This study uses disassembly-precedence graphs to clarify disassembly-task information and converts it into a task-precedence matrix. This matrix includes both symmetry and asymmetry, reflecting the dependencies and independencies among disassembly tasks. Based on this, we develop a multiobjective optimisation model that integrates disassembly-task allocation, operation mode selection, and the use of collaborative robots. The objectives are to minimise the number of workstations, the idle rate of the disassembly line, and the energy consumption. Given the asymmetry in disassembly-task attributes, such as the time differences required for disassembling various components and the diverse operation modes, this study employs an evolutionary algorithm to address potential asymmetric optimisation problems. Specifically, we introduce an advanced multi-objective discrete bee algorithm and validate its effectiveness and superiority for solving the disassembly-line balancing problem through a comparative analysis with other algorithms. This research not only provides innovative optimisation strategies for the product-recycling field but also offers valuable experience and reference for the further development of industrial automation and human–robot collaboration.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) increases blood low-density lipoprotein (LDL) cholesterol by acting as an LDL receptor antagonist, thereby impairing LDL particle clearance. Since genetic disruption of PCSK9 is linked to reduced risk of coronary heart disease (CHD), our recent work has sought to permanently knock out the gene by using new genome editing technology. Recently reported “base editors” introduce point mutations at specific locations in the genome without the need for DNA double-strand breaks and, thus, with a lowered incidence of off-target effects. These base editors build on the CRISPR-Cas9 system by tethering an RNA-editing domain to a nickase version of Cas9, allowing for specific CT and/or GA base alterations. In this study, we used the “BE3” base editor (which uses the APOBEC-1 RNA-editing domain) to specifically target codons encoding tryptophans (TGG) or glutamines (CAG or CAA) to introduce nonsense mutations (producing stop codons TAG, TGA, or TAA) into human PCSK9 . The number of testable targets was increased by introducing specific point mutations into the BE3 construct (D1135V, R1335Q, T1337R in the Cas9 portion of BE3) to alter the protospacer adjacent motif (PAM) from NGG to NGA. Using HEK293 cells, we individually targeted a number of codons spanning the first seven exons in PCSK9 and identified several efficient targets. Most notably, the codons encoding glutamine 278 and glutamine 302 could be altered to stop codons in ~50% of alleles, as determined by the CEL-I nuclease mismatch assay and Sanger sequencing. We then targeted glutamine 278 in human induced pluripotent stem cells (iPSCs) and demonstrated base editing to introduce nonsense mutations into PCSK9 , albeit at a lower efficiency than in HEK293 cells. As a next step towards translation to human patients, we are targeting Pcsk9 in the mouse liver in vivo with BE3. Base editing may prove to be an efficient, safer strategy than standard CRISPR-Cas9 genome editing and holds promise as a strategy for the prevention of CHD.

A ring-hierarchy-based evolutionary algorithm for multimodal multi-objective optimization

A circular economy is an alternative to the traditional linear economy. The concept reduces waste by recovering resources at the end of a product’s life cycle and repurposing them to reduce environmental impact. The repurposing of machines is a model of production and consumption that involves re-pairing, refurbishing, and recycling to reduce waste. This project aims to improvise and repurpose existing mangosteen peeling machines into another suitable manufacturing process. The designed potato-cutting machine uses the existing conveyor system of a customised mangosteen peeling machine. The cutting mechanism incorporates safety and automation systems using through-beam infrared and capacitive proximity sensors. For the safety system, detecting objects within a range of 2.0 cm to the conveyor region indicates that the machine is unsafe to operate. The system will stop the machine and send audio and visual warnings to notify the users. Based on the trial runs the machine cuts the potato with approximately 1-tenth of the time needed in manual labour. The most significant benefit, however, is the overall savings in production costs, which are more than 85%. This is achieved as the machine re-purposes more than 50% of the components from the existing mangosteen peeling machine, such as sensors, control panel boxes, and PLC system. Consequently, this has helped minimise the overall cost of new equipment while encouraging a circular economy approach to engineering practices.

Remanufacturing has enormous economic and environmental benefits in terms of resource conservation and environmental protection. Disassembly, as an essential step in remanufacturing, is always manually executed, it has the disadvantages of high labor intensive, time consuming and low efficiency while robotic disassembly can cover the shortages of manual disassembly. During the robotic disassembly process, considering the structure and movement characteristics of the industrial robot, the industrial robot need to perform collision avoidance movements considering the obstacle caused by the End-of-Life (EoL) product. The moving time considering the robotic collision avoidance trajectory is a non-negligible part of total disassembly time. In this paper, robotic disassembly sequence planning (RDSP) considering robotic collision avoidance trajectory is proposed. This method is used to obtain the collision avoidance trajectory and the moving time between different disassembly points by the robotic collision avoidance model established in this paper. Afterwards, an optimized discrete bee algorithm (ODBA) is used to generate the optimal disassembly sequence to minimize the total disassembly time. Finally, case studies based on a gear pump verify the effectiveness of proposed methods.

A mesh adaptive direct search algorithm for multiobjective optimization

The signed network has received widespread attention because it can well reflect the cooperation and conflict relationship. Structural balance is an important global feature in signed networks, which can well reflect the structural characteristics of the network. Existing structural balance calculation algorithms define the global and local balance computation problems as an optimization problem, and then optimize their respective objective functions through optimization algorithms, but these algorithms ignore the correlation between the two problems. In this paper, we combine the multifactorial evolutionary algorithm and the discrete particle swarm optimization algorithm, and further propose the multifactorial discrete particle swarm optimization algorithm (MFDPSO). This algorithm designs the knowledge transfer function and optimization algorithm based on the correlation of the strong and weak structure balance and optimizes the two problems at the same time. The experimental results on 8 real networks demonstrate the effectiveness of the MFDPSO.

Design and multi-objective optimization of hairpin motor cooling system based on orthogonal design method and composite algorithm

Applying multi-objective ant colony optimization algorithm for solving the unequal area facility layout problems

This paper presents an extension of fuzzy-multi-objective genetic algorithm (MOGA) optimization methodology that could effectively be used to find the overall satisfaction of objective functions (selecting the design variables) in the early stages of design process. The coupling of objective functions due to design variables in an engineering design process will result in difficulties in design optimization problems. The primary application of this methodology is the design of a liquid propellant engine with the maximum specific impulse and the minimum weight. The independent design variables in this model are combustion chamber pressure, exit pressure, oxidizer to fuel mass flow rate. To handle the mentioned problems, a fuzzy-multi-objective genetic algorithm optimization methodology is developed based on Pareto optimal set. Liquid propellant engine, F-1 is modeled to illustrate accuracy and efficiency of proposed methodology.