A balanced-quantum inspired evolutionary algorithm for solving disassembly line balancing problem

Singh, Rakshit Kumar; Singh, Avinash; Yadav, Ravindra Kumar

doi:10.1016/j.asoc.2022.109840

Cited by 17 publications

(4 citation statements)

References 27 publications

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“…Ren et al, based on the gravitational search algorithm, proposed solutions for a profit-oriented partial DLB problem [13]. Singh et al introduced a quantum heuristic algorithm to optimise the profitability and workload balance of the disassembly line [14]. Wang et al aimed to maximise workers' efficiency, increase profits, reduce energy consumption, and balance the workers' load; established a new DLB model based on economic benefits and environmental impact; and proposed a discrete multiobjective artificial bee colony algorithm to solve the proposed model [15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this section, we conduct an in-depth examination of the performance of MODBA, comparing it with other advanced algorithms from the literature that have been proven effective in solving line balancing and other discrete location-based optimisation problems. These include the non-dominated sorting genetic algorithm II (NSGA-II) [35], the multiobjective novel immune clonal algorithm (NICA-II) [37], the MOPSO algorithm [11], the improved gravitational search algorithm (GSA) [13], and the balanced-quantum inspired evolutionary algorithm (QEA) [14]. We perform a comprehensive analysis of the performances of different algorithms using three well-known multiobjective evaluation metrics: the number of Pareto solutions (NPS), inverted generational distance (IGD), and hypervolume (HV).…”

Section: Algorithm Performance Analysismentioning

confidence: 99%

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Human–Robot Collaboration on a Disassembly-Line Balancing Problem with an Advanced Multiobjective Discrete Bees Algorithm

Shen,

Lu,

Sheng

et al. 2024

Symmetry

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Algorithm Performance Analysismentioning

confidence: 99%

Human–Robot Collaboration on a Disassembly-Line Balancing Problem with an Advanced Multiobjective Discrete Bees Algorithm

Shen,

Lu,

Sheng

et al. 2024

Symmetry

View full text Add to dashboard Cite

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“…( Uses methods, such as artificial bee colony optimization, and so forth, that may be tailored to a specific class of DSP issues. (Gao et al, 2020;Singh et al, 2023) complex structures. In the matrix-based method, an object's structure is represented by a matrix, with each element standing in for both a component of the object and the connections between those components.…”

Section: Low-carbon Manufacturingmentioning

confidence: 99%

Product low‐carbon design, manufacturing, logistics, and recycling: An overview

Yuan

Qian

et al. 2023

WIREs Energy & Environment

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Due to the worsening world climate crisis, reducing carbon emissions throughout product operations has become an issue that the manufacturing industry must face. In order to gain competitive advantage, this paper aims to explore low carbon design techniques for product life cycle: including low carbon design, low carbon manufacturing, low carbon logistics and low carbon recycling, as they are highly relevant to reducing carbon emissions. This paper proposes a portfolio structure for low carbon operations, consisting of a target layer, a strategy layer, and a support layer, aimed at improving energy efficiency and reducing carbon emissions. To emphasize the maximization of economic, environmental, and social advantages, the objective layer establishes a system of goals for low‐carbon product design. The strategy layer proposes four low‐carbon product design technologies: low‐carbon product design, manufacturing, transportation, and recycling. The support layer emphasizes the importance of operational systems and support platforms for low‐carbon products, such as low‐carbon design systems, energy information collection systems, energy optimization support systems, carbon emission monitoring and control systems, and low‐carbon management systems. However, successful implementation of low carbon manufacturing is challenging due to the complexity of low carbon manufacturing and the uniqueness of each company. Therefore, this paper suggests applying a life‐cycle‐oriented low‐carbon operation model to various manufacturing enterprises and exploring models that reflect actual manufacturing practices to achieve the goal of low‐carbon manufacturing.This article is categorized under: Sustainable Development > Goals

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“…Most previous research on disassembly line balancing has been oriented toward maximising profit [58][59][60][61][62][63], the same with robotic disassembly line balancing. However, recent developments in robotic disassembly line balancing have incorporated sustainability as one of the objectives.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Product Recovery Options in End-of-Life Product Disassembly by Robots

Hartono,

Ramírez,

Pham

2023

Automation

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In a circular economy, strategies for product recovery, such as reuse, recycling, and remanufacturing, play an important role at the end of a product’s life. A sustainability model was developed to solve the problem of sequence-dependent robotic disassembly line balancing. This research aimed to assess the viability of the model, which was optimised using the Multi-Objective Bees Algorithm in a robotic disassembly setting. Two industrial gear pumps were used as case studies. Four objectives (maximising profit, energy savings, emissions reductions and minimising line imbalance) were set. Several product recovery scenarios were developed to find the best recovery plans for each component. An efficient metaheuristic, the Bees Algorithm, was used to find the best solution. The robotic disassembly plans were generated and assigned to robotic workstations simultaneously. Using the proposed sustainability model on end-of-life industrial gear pumps shows the applicability of the model to real-world problems. The Multi-Objective Bees Algorithm was able to find the best scenario for product recovery by assigning each component to recycling, reuse, remanufacturing, or disposal. The performance of the algorithm is consistent, producing a similar performance for all sustainable strategies. This study addresses issues that arise with product recovery options for end-of-life products and provides optimal solutions through case studies.

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A balanced-quantum inspired evolutionary algorithm for solving disassembly line balancing problem

Cited by 17 publications

References 27 publications

Human–Robot Collaboration on a Disassembly-Line Balancing Problem with an Advanced Multiobjective Discrete Bees Algorithm

Human–Robot Collaboration on a Disassembly-Line Balancing Problem with an Advanced Multiobjective Discrete Bees Algorithm

Product low‐carbon design, manufacturing, logistics, and recycling: An overview

Optimisation of Product Recovery Options in End-of-Life Product Disassembly by Robots

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