Multimanned disassembly line balancing optimization considering walking workers and task evaluation indicators

Tuo, Yang; Zhang, Zeqiang; Wu, Tengfei; Zeng, Yanqing; Zhang, Yu; Junqi, Liu

doi:10.1016/j.jmsy.2023.11.011

Cited by 9 publications

(3 citation statements)

References 55 publications

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“…Economic, technical, environmental and social factors were used by Sherif et al [47] for the selection of battery recycling plant locations in sustainable environments. Tuo et al [48] used AHP to evaluate the retention attributes of tasks based on the problem characteristics and the high-value attributes of the task (appearance and mechanical and electrical components). Disassembly time, disassembly energy consumption and disassembly profit were used by many researchers as the criteria for selecting the optimal disassembly depth for EoL smartphones.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Chen,

Li,

Chu

et al. 2024

Sustainability

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The disassembly of end-of-life (EoL) products is of high concern in sustainability research. It is important to obtain reasonable disassembly depth during the disassembly process. However, the overall safety of the disassembly process is not considered during the disassembly depth optimization process, which leads to an inability to accurately obtain a reasonable disassembly depth. Considering this, a multi-objective disassembly depth optimization method for EoL smartphones considering the overall safety of the disassembly process is proposed to accurately determine a reasonable disassembly depth in this study. The feasible disassembly depth for EoL smartphones is first determined. The reasonable disassembly process for EoL smartphones is then established. A multi-objective function for disassembly depth optimization for EoL smartphones is established based on the disassembly profit per unit time, the disassembly energy consumption per unit time and the overall safety rate of the disassembly process. In order to increase solution accuracy and avoid local optimization, an improved teaching–learning-based optimization algorithm (ITLBO) is proposed. The overall safety of the disassembly process, disassembly time, disassembly energy consumption and disassembly profit are used as the criteria for the fuzzy analytic hierarchy process (AHP) to evaluate the disassembly depth solution. A case of the ‘Xiaomi 4’ smartphone is used to verify the applicability of the proposed method. The results show that the searchability of the non-inferior solution and the optimal solution of the proposed method are improved. The convergence speeds of the ITLBO algorithm are 50.00%, 33.33% and 30.43% higher than those of the TLBO algorithm, and the optimal solution values of the ITLBO algorithm are 3.91%, 5.10% and 3.45% higher than those of the TLBO algorithm in three experiments of single objective optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Chen,

Li,

Chu

et al. 2024

Sustainability

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“…In the complete disassembly of obsolete products, the number of stations, parts demand, disassembly cost, idle time equilibrium index, hazard degree and smoothing rate are taken as the main objectives [3][4][5][6][7][8][9][10]. These studies ignore the carbon emission generated in the operation, whereas excess carbon emission is an important factor contributing to the global temperature rise.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for a Partial Disassembly Line Balancing Problem Considering Profit and Carbon Emission

Yang,

Li,

Zheng

et al. 2024

Mathematics

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Disassembly lines are widely utilized to disassemble end-of-life products. Most of the research focuses on the complete disassembly of obsolete products. However, there is a lack of studies on profit and on carbon emission saved. Hence, this study considers the multi-objective partial disassembly line balancing problem with AND/OR precedence relations to optimize profit, saved carbon emission and line balance simultaneously. Firstly, a multi-objective mixed-integer programming model is formulated, which could optimally solve the small number of instances with a single objective. Meanwhile, an improved multi-objective artificial bee colony algorithm is developed to generate a set of high-quality Pareto solutions. This algorithm utilizes two-layer encoding of the task permutation vector and the number of selected parts, and develops two-phase decoding to handle the precedence relation constraint and cycle time constraint. In addition, the modified employed bee phase utilizes the neighborhood operation, and the onlooker phase utilizes the crossover operator to achieve a diverse population. The modified scout phase selects a solution from the Pareto front to replace the abandoned individual to obtain a new high-quality solution. To test the performance of the proposed algorithm, the algorithm is compared with the multi-objective simulated annealing algorithm, the original multi-objective artificial bee colony algorithm and the well-known fast non-dominated genetic algorithm. The comparative study demonstrates that the proposed improvements enhance the performance of the method presented, and the proposed methodology outperforms all the compared algorithms.

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“…Through careful design and optimisation of disassembly lines, disassembly and recycling work can be systematised and proceduralised, thereby reducing production time and costs [4]. Therefore, the disassembly-line balancing (DLB) problem has become an important issue that urgently needs to be resolved [5,6]. This problem involves the rational allocation of disassembly tasks to various workstations, aiming to optimise efficiency standards while satisfying multiple constraints, such as task priority [5].…”

Section: Introductionmentioning

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

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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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Multimanned disassembly line balancing optimization considering walking workers and task evaluation indicators

Cited by 9 publications

References 55 publications

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Multi-Objective Optimization for a Partial Disassembly Line Balancing Problem Considering Profit and Carbon Emission

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

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