Journal articles: 'Disassembly line balancing'

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Hu, Youxi, Chao Liu, Ming Zhang, Yu Jia, and Yuchun Xu. "A Novel Simulated Annealing-Based Hyper-Heuristic Algorithm for Stochastic Parallel Disassembly Line Balancing in Smart Remanufacturing." Sensors 23, no. 3 (February 2, 2023): 1652. http://dx.doi.org/10.3390/s23031652.

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Remanufacturing prolongs the life cycle and increases the residual value of various end-of-life (EoL) products. As an inevitable process in remanufacturing, disassembly plays an essential role in retrieving the high-value and useable components of EoL products. To disassemble massive quantities and multi-types of EoL products, disassembly lines are introduced to improve the cost-effectiveness and efficiency of the disassembly processes. In this context, disassembly line balancing problem (DLBP) becomes a critical challenge that determines the overall performance of disassembly lines. Currently, the DLBP is mostly studied in straight disassembly lines using single-objective optimization methods, which cannot represent the actual disassembly environment. Therefore, in this paper, we extend the mathematical model of the basic DLBP to stochastic parallel complete disassembly line balancing problem (DLBP-SP). A novel simulated annealing-based hyper-heuristic algorithm (HH) is proposed for multi-objective optimization of the DLBP-SP, considering the number of workstations, working load index, and profits. The feasibility, superiority, stability, and robustness of the proposed HH algorithm are validated through computational experiments, including a set of comparison experiments and a case study of gearboxes disassembly. To the best of our knowledge, this research is the first to introduce gearboxes as a case study in DLBP which enriches the research on disassembly of industrial equipment.

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Altekin, F. Tevhide, Levent Kandiller, and Nur Evin Ozdemirel. "Profit-oriented disassembly-line balancing." International Journal of Production Research 46, no. 10 (May 15, 2008): 2675–93. http://dx.doi.org/10.1080/00207540601137207.

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Xu, Gongdan, Zhiwei Zhang, Zhiwu Li, Xiwang Guo, Liang Qi, and Xianzhao Liu. "Multi-Objective Discrete Brainstorming Optimizer to Solve the Stochastic Multiple-Product Robotic Disassembly Line Balancing Problem Subject to Disassembly Failures." Mathematics 11, no. 6 (March 22, 2023): 1557. http://dx.doi.org/10.3390/math11061557.

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Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, which can further improve the efficiency. Moreover, uncertainty such as the depreciation of EOL products, may result in disassembly failure. In this research, a stochastic multi-product robotic disassembly line balancing model is established using an AND/OR graph. It takes the precedence relationship, cycle constraint, and disassembly failure into consideration to maximize the profit and minimize the energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with stochastic simulation is proposed to solve the problem. Furthermore, by conducting experiments on some real cases and comparing with four state-of-the-art multi-objective optimization algorithms, i.e., the multi-objective discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition, this paper validates its excellent performance in solving the concerned problem.

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Zhang, Qi, Yang Xing, Man Yao, Jiacun Wang, Xiwang Guo, Shujin Qin, Liang Qi, and Fuguang Huang. "An Improved Discrete Bat Algorithm for Multi-Objective Partial Parallel Disassembly Line Balancing Problem." Mathematics 12, no. 5 (February 28, 2024): 703. http://dx.doi.org/10.3390/math12050703.

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Product disassembly is an effective means of waste recycling and reutilization that has received much attention recently. In terms of disassembly efficiency, the number of disassembly skills possessed by workers plays a crucial role in improving disassembly efficiency. Therefore, in order to effectively and reasonably disassemble discarded products, this paper proposes a partial parallel disassembly line balancing problem (PP-DLBP) that takes into account the number of worker skills. In this paper, the disassembly tasks and the disassembly relationships between components are described using AND–OR graphs. In this paper, a multi-objective optimization model is established aiming to maximize the net profit of disassembly and minimize the number of skills for the workers. Based on the bat algorithm (BA), we propose an improved discrete bat algorithm (IDBA), which involves designing adaptive composite optimization operators to replace the original continuous formula expressions and applying them to solve the PP-DLBP. To demonstrate the advantages of IDBA, we compares it with NSGA-II, NSGA-III, SPEA-II, ESPEA, and MOEA/D. Experimental results show that IDBA outperforms the other five algorithms in real disassembly cases and exhibits high efficiency.

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Filip, Florin Gheorghe. ""The Disassembly Line: Balancing and Modeling" - Book Review." International Journal of Computers Communications & Control 6, no. 3 (September 1, 2011): 581. http://dx.doi.org/10.15837/ijccc.2011.3.2137.

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This book is about the disassembly of end-of-life products with particular emphasis on methods and techniques for solving the Disassembly Line Balancing Problem.Disassembly is viewed as "the systematic separation and extracting valuable entities for possible future re-usage". In fact, disassembly is a distinct phase of the product lifecycle. It follows the "before life" phases (such as design and economical evaluation), "useful period" phases (such as manufacturing, distribution, usage and maintenance) and "end of life" phases (such as collecting, sorting). Disassembly might represent the essential first phase of the future activities, such as re-use and re-manufacturing and recycle. Due to the ever higher public awareness, the more and more strict regulations concerning environment quality preservation and increasing economic effectiveness and attractiveness for industry, the activities of recovering valuable parts and subassemblies have become a desirable alternative to the old fashioned disposal processes of end-of-life products.The authors state, in the preface of the book, that the "disassembly line seems to be the most efficient way to disassemble a product". Consequently, the primary concern of the book is "the complete disassembly of [end-of-life] products on a paced disassembly line for component/material recovery purposes". The authors aim at investigating "the qualitative and quantitative aspects of multi-criteria solution sequences using the various combinatorial optimization techniques" (page 16) to solve the Disassembly Line Balancing Problem (DLPB). The DLBP consists in finding a disassembly feasible solution sequence which preserves precedence constraints and aims at attaining several objectives, such as minimizing the number of work stations and total idle time, ensuring similar idle time at each work station, while attempting to remove hazardous parts and materials and extracting highly demanded product components at the earliest moments possible and minimizing the number of direction changes required for disassembly (removing parts with similar part removal directions together), (page 102).The book is composed of 29 chapters grouped into three parts entitled "Disassembly Background", "Disassembly-Line Balancing" and "Further Disassembly-Line Considerations" which address general aspects concerning disassembly processes, variations of methods and techniques to solve the DLBP, and other problems related to the disassembly line, respectively. Part I comprises six chapters which are meant to set the stage for the subsequent chapters. Various information concerning disassembly processes, assembly lines, disassembly lines, other related researches, graphical representations and computational complexity of combinatorial problems are provided. Part II is made up of 20 chapters and addresses the statement and analysis of the DLBP and several specific variations of methods and techniques which were adapted for solving the problem, tested on four application cases exprimental instances and compared. The objectives of this part of the book are: stating the mathematical model of DLBP, establishing the difficulty of the problem by using the complexity theory and determining the data sets and evaluation criteria to be used in analyzing the problem and solving techniques which are selected (page 99). It is demonstrated (in chapter 9) that the DLBP is a complex NP complete problem in the strong sense and necessitates specialized solution techniques. Accordingly, authors plea for combinatorial optimization approaches and select several algorithms to solve the problem. The techniques to be utilized to solve the DLBP are introduced in chapter 10 and their usage and performances in solving the problem are presented in chapters 12 through 19. There are seven techniques which are adapted, tested and compared. The exhaustive search is used to provide the optimal solution. Two metaheuristic approaches (genetic algorithms and ant colony optimization) are next studied. Two purely deterministic searches (the greedy algorithm and the "hunter-killer" search) and two 2-phase hybrid methods are adapted and tested.The four experimental instances (the eight-part personal computer, the enhanced 10-part DLBP case, the 25-part cellular instance, and the size independent "a priori" benchmark with a known optimal solution) are described in chapter 11. Chapter 20 contains a detailed comparison of the six heuristic and metaheuristic techniques as applied to the DLBP with respect to several performance measures. Several complementary research results are reviewed in chapter 21 together with future research directions. Disassembly processes interact with other "before life", "useful", and "after life" periods of product usage and recovery. As a result, to make the picture complete, Part III addresses other areas of disassembly research such as product planning, line and facility design, operations scheduling and sequencing, inventory, "just-in-time", revenue and unbalanced lines (chapter 22 through 29). The authors of the book form a team who may be viewed as a fine and synergic combination of two complementary experiences and backgrounds from academia and industry. Seamus McGovern, an Electronics Engineer at the Volpe National Transportation System Center, holds a commission in the US Navy as an aerospace duty engineer as well as a part-time industrial engineering faculty appointment at the Northeastern University. Surendra M. Gupta is a professor of Mechanical and Industrial Engineering and a director of the Laboratory for Responsible Manufacturing at the Northeastern University. He has authored/co-authored over 400 technical papers and is a pioneer in the domain of the book. This book represents a very valuable work in a rather young research domain, which may be viewed as opened by the pioneering paper of Güngör and Gupta entitled "Disassembly Line in product Recovery (International Journal of Production Recovery, 40 (11), 2002). The volume mainly reflects the original studies of authors and their colleagues. It also makes an exhaustive and systematic review of the results which are reported in the domain scientific literature and are due to other scientists. The organization of the document is well thought and the presentation style is rigorous and clear. Subsequently, though information content is very dense and diverse, the book is accessible and its study is scientifically rewarding. Special remarks can be made to the uniform and coherent notation which is used throughout the book and to graphical illustrations. A final remark of appreciation is to be made to the excellent quality of editing and printing of the book due to the staff of McGraw-Hill Companies. In conclusion, the book is a timely work which contains relevant, inspiring and challenging information. Therefore, this reviewer warmly recommends it to the readers of academia and industry as well who are interested in modern manufacturing issues and combinatorial optimization methods and software.

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Paprocka, Iwona, and Bożena Skołud. "A Predictive Approach for Disassembly Line Balancing Problems." Sensors 22, no. 10 (May 22, 2022): 3920. http://dx.doi.org/10.3390/s22103920.

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In selective serial disassembly sequence planning, when the target node (component) is reached, the selective disassembly task is completed and the refurbished component is repaired, reused or remanufactured. Since the efficient utilization of existing resources is necessary, it is crucial to predict disassembly operation times and the condition of joints for recycling, reusing or remanufacturing. The method of estimating the disassembly times of a joint if it is intended for remanufacturing, recycling and reuse is an important and urgent requirement for research development and results. The aim of the paper is to investigate the disassembly system with predicted operation times and the quality of product connections (joints) in order to balance the line smoothness index, to minimize a line time factor, line efficiency and profit and minimize an ex post error. Disassembly times for remanufacturing, recycling and reuse are estimated separately based on the historical data of disassembly times and the quality of joints. The presented estimation method of disassembly operation times increases the reliability and efficiency of elaborated balances of tasks in lines. Underestimated disassembly operation times can be compensated for during the idle points in the successive cycles, provided that the transport operations are performed manually and that travel time determines the cycle time.

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Mete, Süleyman, Zeynel Abidin Çil, Eren Özceylan, and Kürşad Ağpak. "Resource Constrained Disassembly Line Balancing Problem." IFAC-PapersOnLine 49, no. 12 (2016): 921–25. http://dx.doi.org/10.1016/j.ifacol.2016.07.893.

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Kalayci, Can B., Olcay Polat, and Surendra M. Gupta. "A variable neighbourhood search algorithm for disassembly lines." Journal of Manufacturing Technology Management 26, no. 2 (March 2, 2015): 182–94. http://dx.doi.org/10.1108/jmtm-11-2013-0168.

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Purpose – The purpose of this paper is to efficiently solve disassembly line balancing problem (DLBP) and the sequence-dependent disassembly line balancing problem (SDDLBP) which are both known to be NP-complete. Design/methodology/approach – This manuscript utilizes a well-proven metaheuristics solution methodology, namely, variable neighborhood search (VNS), to address the problem. Findings – DLBPs are analyzed using the numerical instances from the literature to show the efficiency of the proposed approach. The proposed algorithm showed superior performance compared to other techniques provided in the literature in terms of robustness to reach better solutions. Practical implications – Since disassembly is the most critical step in end-of-life product treatment, every step toward improving disassembly line balancing brings us closer to cost savings and compelling practicality. Originality/value – This paper is the first adaptation of VNS algorithm for solving DLBP and SDDLBP.

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Jin, Liang, Xiao Zhang, Yilin Fang, and Duc Truong Pham. "Transfer Learning-Assisted Evolutionary Dynamic Optimisation for Dynamic Human-Robot Collaborative Disassembly Line Balancing." Applied Sciences 12, no. 21 (October 30, 2022): 11008. http://dx.doi.org/10.3390/app122111008.

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In a human-robot collaborative disassembly line, multiple people and robots collaboratively perform disassembly operations at each workstation. Due to dynamic factors, such as end-of-life product quality and human capabilities, the line balancing problem for the human-robot collaborative disassembly line is a dynamic optimisation problem. Therefore, this paper investigates this problem in detail and commits to finding the evolutionary dynamic optimisation. First, a task-based dynamic disassembly process model is proposed. The model can characterise all feasible task sequences of disassembly operations and the dynamic characteristics of tasks affected by uncertain product quality and human capabilities. Second, a multiobjective optimisation model and a feature-based transfer learning-assisted evolutionary dynamic optimisation algorithm for the dynamic human-robot collaborative disassembly line balancing problem are developed. Third, the proposed algorithm uses the balanced distribution adaptation method to transfer the knowledge of the optimal solutions between related problems in time series to track and respond to changes in the dynamic disassembly environment. Then, it obtains the optimal solution sets in a time-varying environment in time. Finally, based on a set of problem instances generated in this study, the proposed algorithm and several competitors are compared and analysed in terms of performance indicators, such as the mean inverted generational distance and the mean hypervolume, verifying the effectiveness of the proposed algorithm on dynamic human-robot collaborative disassembly line balancing.

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Xia, Xuhui, Wei Liu, Zelin Zhang, Lei Wang, Jianhua Cao, and Xiang Liu. "A Balancing Method of Mixed-model Disassembly Line in Random Working Environment." Sustainability 11, no. 8 (April 17, 2019): 2304. http://dx.doi.org/10.3390/su11082304.

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Disassembly is a necessary link in reverse supply chain and plays a significant role in green manufacturing and sustainable development. However, the mixed-model disassembly of multiple types of retired mechanical products is hard to be implemented by random influence factors such as service time of retired products, degree of wear and tear, proficiency level of workers and structural differences between products in the actual production process. Therefore, this paper presented a balancing method of mixed-model disassembly line in a random working environment. The random influence of structure similarity of multiple products on the disassembly line balance was considered and the workstation number, load balancing index, prior disassembly of high demand parts and cost minimization of invalid operations were taken as targets for the balancing model establishment of the mixed-model disassembly line. An improved algorithm, adaptive simulated annealing genetic algorithm (ASAGA), was adopted to solve the balancing model and the local and global optimization ability were enhanced obviously. Finally, we took the mixed-model disassembly of multi-engine products as an example and verified the practicability and effectiveness of the proposed model and algorithm through comparison with genetic algorithm (GA) and simulated annealing algorithm (SA).

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Wang, Y. J., N. D. Wang, S. M. Cheng, X. C. Zhang, H. Y. Liu, J. L. Shi, Q. Y. Ma, and M. J. Zhou. "Optimization of disassembly line balancing using an improved multi-objective Genetic Algorithm." Advances in Production Engineering & Management 16, no. 2 (June 25, 2021): 240–52. http://dx.doi.org/10.14743/apem2021.2.397.

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Disassembly activities take place in various recovery operations including remanufacturing, recycling, and disposal. Product disassembly is an effective way to recycle waste products, and it is a necessary condition to make the product life cycle complete. According to the characteristics of the product disassembly line, based on minimizing the number of workstations and balancing the idle time in the station, the harmful index, the demand index, and the number of direction changes are proposed as new optimization objectives. So based on the analysis of the traditional genetic algorithm into the precocious phenomenon, this paper constructed the multi-objective relationship of the disassembly line balance problem. The disassembly line balance problem belongs to the NP-hard problem, and the intelligent optimization algorithm shows excellent performance in solving this problem. Considering the characteristics of the traditional method solving the multi-objective disassembly line balance problem that the solution result was single and could not meet many objectives of balance, a multi-objective improved genetic algorithm was proposed to solve the model. The algorithm speeds up the convergence speed of the algorithm. Based on the example of the basic disassembly task, by comparing with the existing single objective heuristic algorithm, the multi-objective improved genetic algorithm was verified to be effective and feasible, and it was applied to the actual disassembly example to obtain the balance optimization scheme. Two case studies are given: a disassembly process of the automobile engine and a disassembly of the computer components.

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Wang, Y. J., N. D. Wang, S. M. Cheng, X. C. Zhang, H. Y. Liu, J. L. Shi, Q. Y. Ma, and M. J. Zhou. "Optimization of disassembly line balancing using an improved multi-objective Genetic Algorithm." Advances in Production Engineering & Management 16, no. 2 (June 25, 2021): 240–52. http://dx.doi.org/10.14743/apem2021.2.397.

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Disassembly activities take place in various recovery operations including remanufacturing, recycling, and disposal. Product disassembly is an effective way to recycle waste products, and it is a necessary condition to make the product life cycle complete. According to the characteristics of the product disassembly line, based on minimizing the number of workstations and balancing the idle time in the station, the harmful index, the demand index, and the number of direction changes are proposed as new optimization objectives. So based on the analysis of the traditional genetic algorithm into the precocious phenomenon, this paper constructed the multi-objective relationship of the disassembly line balance problem. The disassembly line balance problem belongs to the NP-hard problem, and the intelligent optimization algorithm shows excellent performance in solving this problem. Considering the characteristics of the traditional method solving the multi-objective disassembly line balance problem that the solution result was single and could not meet many objectives of balance, a multi-objective improved genetic algorithm was proposed to solve the model. The algorithm speeds up the convergence speed of the algorithm. Based on the example of the basic disassembly task, by comparing with the existing single objective heuristic algorithm, the multi-objective improved genetic algorithm was verified to be effective and feasible, and it was applied to the actual disassembly example to obtain the balance optimization scheme. Two case studies are given: a disassembly process of the automobile engine and a disassembly of the computer components.

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Cao, Jianhua, Xuhui Xia, Lei Wang, Zelin Zhang, and Xiang Liu. "A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem." Sustainability 11, no. 24 (December 6, 2019): 6969. http://dx.doi.org/10.3390/su11246969.

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Disassembly is an indispensable part in remanufacturing process. Disassembly line balancing and disassembly mode have direct effects on the disassembly efficiency and resource utilization. Recent researches about disassembly line balancing problem (DLBP) either considered the highest productivity, lowest disassembly cost or some other performance measures. No one has considered these metrics comprehensively. In practical production, ignoring the ratio of resource input and value output within remanufacturing oriented disassembly can result in inefficient or pointless remanufacturing operations. To address the problem, a novel multi-efficiency DLBP optimization method is proposed. Different from the conventional DLBP, destructive disassembly mode is considered not only on un-detachable parts, but also on detachable parts with low value, high energy consumption, and long task time. The time efficiency, energy efficiency, and value efficiency are newly defined as the ultimate optimization objectives. For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed. The proposed model and algorithm are validated by different scales examples and applied to an automotive engine disassembly line. The results show that the proposed model is more efficient, and the algorithm is well suited to the multi-objective optimization model.

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Diri Kenger, Zülal, Çağrı Koç, and Eren Özceylan. "Integrated disassembly line balancing and routing problem." International Journal of Production Research 58, no. 23 (March 23, 2020): 7250–68. http://dx.doi.org/10.1080/00207543.2020.1740346.

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Ilgin, Mehmet Ali, Hakan Akçay, and Ceyhun Araz. "Disassembly line balancing using linear physical programming." International Journal of Production Research 55, no. 20 (May 10, 2017): 6108–19. http://dx.doi.org/10.1080/00207543.2017.1324225.

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Bentaha, Mohand Lounes, Olga Battaïa, and Alexandre Dolgui. "Disassembly Line Balancing and Sequencing under Uncertainty." Procedia CIRP 15 (2014): 239–44. http://dx.doi.org/10.1016/j.procir.2014.06.016.

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Duta, L., I. Caciula, and P. C. Patic. "Column generation approach for disassembly line balancing." IFAC-PapersOnLine 49, no. 12 (2016): 916–20. http://dx.doi.org/10.1016/j.ifacol.2016.07.892.

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McGovern, Seamus M., and Surendra M. Gupta. "A balancing method and genetic algorithm for disassembly line balancing." European Journal of Operational Research 179, no. 3 (June 2007): 692–708. http://dx.doi.org/10.1016/j.ejor.2005.03.055.

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McGovern, Seamus M., and Surendra M. Gupta. "Unified assembly- and disassembly-line model formulae." Journal of Manufacturing Technology Management 26, no. 2 (March 2, 2015): 195–212. http://dx.doi.org/10.1108/jmtm-11-2013-0169.

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Purpose – There is a rich body of literature on sequencing assembly and on sequencing disassembly, but little that either fuses or contrasts the two, which may be valuable for long-range planning in the closed-loop supply chain and simply convenient in terms of consistency in nomenclature and mathematical formulations. The purpose of this paper is to concisely unify and summarize assembly and disassembly formulae – as well as to add new formulations for completeness – and then demonstrate the similarities and differences between assembly and disassembly. Design/methodology/approach – Along with several familiar assembly-line formulae which are adapted here for disassembly, five (two specific and three general) metrics and a comparative performance formula from disassembly-line balancing are proposed for use in assembly- and disassembly-line sequencing and balancing either directly, through generalization, or with some extension. The size of assembly and disassembly search spaces are also quantified and formulated. Three new metrics are then developed from each of the general metrics to demonstrate the process of using these general formulae as prototypes. Findings – The three new metrics along with several of the original metrics are selectively applied to a simple, notional case study product to be sequenced on an assembly line and then on a disassembly line. Using these analytical results, the inherent differences between assembly and disassembly, even for a seemingly trivial product, are illustrated. Originality/value – The research adds several new assembly/disassembly metrics, a case study, unifies the evaluation formulae that assembly and disassembly hold in common as well as structuring prototype formulae for flexibility in generating new evaluation criteria for both, and quantifies (using the case study) how assembly and disassembly – while certainly possessing similarities – also demonstrate measurable differences that can be expected to affect product design, planning, production, and end-of-life processing.

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Xu, ZhenYu, Yong Han, ZhenXin Li, YiXin Zou, and YuWei Chen. "A New Pareto Discrete NSGAII Algorithm for Disassembly Line Balance Problem." International Journal of Intelligent Systems 2023 (December 18, 2023): 1–47. http://dx.doi.org/10.1155/2023/8847164.

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With the increasing variety and quantity of end-of-life (EOL) products, the traditional disassembly process has become inefficient. In response to this phenomenon, this article proposes a random multiproduct U-shaped mixed-flow incomplete disassembly line balancing problem (MUPDLBP). MUPDLBP introduces a mixed disassembly method for multiple products and incomplete disassembly method into the traditional DLBP, while considering the characteristics of U-shaped disassembly lines and the uncertainty of the disassembly process. First, mixed-flow disassembly can improve the efficiency of disassembly lines, reducing factory construction and maintenance costs. Second, by utilizing the characteristics of incomplete disassembly to reduce the number of dismantled components and the flexibility and efficiency of U-shaped disassembly lines in allocating disassembly tasks, further improvement in disassembly efficiency can be achieved. In addition, this paper also addresses the characteristics of EOL products with heavy weight and high rigidity. While retaining the basic settings of MUPDLBP, the stability of the assembly during the disassembly process is considered, and a new problem called MUPDLBP_S, which takes into account the disassembly stability, is further proposed. The corresponding mathematical model is provided. To obtain high-quality disassembly plans, a new and improved algorithm called INSGAII is proposed. The INSGAII algorithm uses the initialization method based on Monte Carlo tree simulation (MCTI) and the Group Global Crowd Degree Comparison (GCDC) operator to replace the initialization method and crowding distance comparison operator in the NSGAII algorithm, effectively improving the coverage of the initial population individuals in the entire solution space and the evenness and spread of the Pareto front. Finally, INSGAII’s effectiveness has been affirmed by tackling both current disassembly line balancing problems and the proposed MUPDLBP and MUPDLBP_S. Importantly, INSGAII outshines six comparison algorithms with a top rank of 1 in the Friedman test, highlighting its superior performance.

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Lambert, A. J. D. "A HEURISTIC FOR ASSEMBLY AND DISASSEMBLY LINE BALANCING." IFAC Proceedings Volumes 40, no. 2 (2007): 69–74. http://dx.doi.org/10.3182/20070523-3-es-4907.00013.

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Özceylan, Eren, and Turan Paksoy. "Reverse supply chain optimisation with disassembly line balancing." International Journal of Production Research 51, no. 20 (October 15, 2013): 5985–6001. http://dx.doi.org/10.1080/00207543.2013.784405.

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BEDiR, Neşet, Hacı Mehmet ALAĞAŞ, and Tamer EREN. "A Disassembly Line Balancing with Multicriteria Decision Making." Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 9, no. 1 (January 15, 2017): 11–18. http://dx.doi.org/10.29137/umagd.346165.

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Bentaha, Mohand Lounes, Olga Battaïa, and Alexandre Dolgui. "Lagrangian Relaxation for Stochastic Disassembly Line Balancing Problem." Procedia CIRP 17 (2014): 56–60. http://dx.doi.org/10.1016/j.procir.2014.02.049.

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Wang, Jiacun, XiWang Guo, Shujin Qin, Qi Zhang, and Fuguang Huang. "Discrete Bat Optimizer for Disassembly Line Balancing Problem." JOURNAL OF Cyber-Physical-Social Intelligence 1 (December 15, 2022): 13–25. http://dx.doi.org/10.61702/biqd5086.

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The recycling of end-of-life (EOL) products is the primary link in the remanufacturing process. EOL products rely on disassembly lines to retain valuable parts for remanufacturing. In this work, a disassembly line balancing model is established based on an AND/OR graph. It takes precedence relation, cycle time restriction, failure risk, and time uncertainty into consideration and aims to maximize the dismantling proﬁt and minimize the energy consumption. Then, a multi-objective discrete bat optimizer based on Pareto rules is designed according to the problem model, and a precedence preserving crossover operator, a single point mutation operator and a 2-optimization operator are used to simulate the ﬂight strategy of bats to satisfy the search of feasible solutions. To speed up the convergence, we propose an elite strategy to maintain the non-dominate solutions in the external ﬁles. By decomposing products of different sizes and analyzing the experimental results, the proposed algorithm is evaluated with the existing multi-objective discrete gray wolf optimizer, artiﬁcial bee colony optimizer, non-dominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition. The effectiveness of the proposed algorithm in solving this problem is veriﬁed.

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Wang, Jiacun, GuiPeng Xi, XiWang Guo, Shixin Liu, ShuJin Qin, and Henry Han. "Reinforcement learning for Hybrid Disassembly Line Balancing Problems." Neurocomputing 569 (February 2024): 127145. http://dx.doi.org/10.1016/j.neucom.2023.127145.

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Wang, Jiacun, XiWang Guo, Shujin Qin, Qi Zhang, and Fuguang Huang. "Discrete Bat Optimizer for Disassembly Line Balancing Problem." JOURNAL OF Cyber-Physical-Social Intelligence 1 (December 15, 2022): 13–25. http://dx.doi.org/10.61702/tdns2312.

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The recycling of end-of-life (EOL) products is the primary link in the remanufacturing process. EOL products rely on disassembly lines to retain valuable parts for remanufacturing. In this work, a disassembly line balancing model is established based on an AND/OR graph. It takes precedence relation, cycle time restriction, failure risk, and time uncertainty into consideration and aims to maximize the dismantling proﬁt and minimize the energy consumption. Then, a multi-objective discrete bat optimizer based on Pareto rules is designed according to the problem model, and a precedence preserving crossover operator, a single point mutation operator and a 2-optimization operator are used to simulate the ﬂight strategy of bats to satisfy the search of feasible solutions. To speed up the convergence, we propose an elite strategy to maintain the non-dominate solutions in the external ﬁles. By decomposing products of different sizes and analyzing the experimental results, the proposed algorithm is evaluated with the existing multi-objective discrete gray wolf optimizer, artiﬁcial bee colony optimizer, non-dominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition. The effectiveness of the proposed algorithm in solving this problem is veriﬁed.

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Kazancoglu, Yigit, and Yesim Deniz Ozkan-Ozen. "Sustainable disassembly line balancing model based on triple bottom line." International Journal of Production Research 58, no. 14 (August 13, 2019): 4246–66. http://dx.doi.org/10.1080/00207543.2019.1651456.

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29

Qin, Shujin, Jiaxin Wang, Jiacun Wang, Xiwang Guo, Liang Qi, and Yaping Fu. "Linear Disassembly Line Balancing Problem with Tool Deterioration and Solution by Discrete Migratory Bird Optimizer." Mathematics 12, no. 2 (January 20, 2024): 342. http://dx.doi.org/10.3390/math12020342.

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In recent years, the global resource shortage has become a serious issue. Recycling end-of-life (EOL) products is conducive to resource reuse and circular economy and can mitigate the resource shortage issue. The disassembly of EOL products is the first step for resource reuse. Disassembly activities need tools, and tool deterioration occurs inevitably during the disassembly process. This work studies the influence of tool deterioration on disassembly efficiency. A disassembly line balancing model with the goal of maximizing disassembly profits is established, in which tool selection and assignment is a critical part. A modified discrete migratory bird optimizer is proposed to solve optimization problems. The well-known IBM CPLEX optimizer is used to verify the correctness of the model. Six real-world products are used for disassembly experiments. The popular fruit fly optimization algorithm, whale optimization algorithm and salp swarm algorithm are used for search performance comparison. The results show that the discrete migratory bird optimizer outperforms all three other algorithms in all disassembly instances.

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30

Sotskov, Yuri N. "Assembly and Production Line Designing, Balancing and Scheduling with Inaccurate Data: A Survey and Perspectives." Algorithms 16, no. 2 (February 10, 2023): 100. http://dx.doi.org/10.3390/a16020100.

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Assembly lines (conveyors) are traditional means of large-scale and mass-scale productions. An assembly line balancing problem is needed for optimizing the assembly process by configuring and designing an assembly line for the same or similar types of final products. This problem consists of designing the assembly line and distributing the total workload for manufacturing each unit of the fixed product to be assembled among the ordered workstations along the constructed assembly line. The assembly line balancing research is focused mainly on simple assembly line balancing problems, which are restricted by a set of conditions making a considered assembly line ideal for research. A lot of published research has been carried out in order to describe and solve (usually heuristically) more realistic generalized assembly line balancing problems. Assembly line designing, balancing and scheduling problems with not deterministic (stochastic, fuzzy or uncertain) parameters have been investigated in many published research works. This paper is about the design and optimization methods for assembly and disassembly lines. We survey the recent developments for designing, balancing and scheduling assembly (disassembly) lines. New formulations of simple assembly line balancing problems are presented in order to take into account modifications and uncertainties characterized by real assembly productions.

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31

Yang, Wanlin, Zixiang Li, Chenyu Zheng, Zikai Zhang, Liping Zhang, and Qiuhua Tang. "Multi-Objective Optimization for a Partial Disassembly Line Balancing Problem Considering Profit and Carbon Emission." Mathematics 12, no. 8 (April 18, 2024): 1218. http://dx.doi.org/10.3390/math12081218.

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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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32

Yang, Jing. "Review of Research on Human-Machine Collaboration in Disassembly Line Balancing Problem." International Journal of Global Economics and Management 2, no. 3 (April 25, 2024): 37–43. http://dx.doi.org/10.62051/ijgem.v2n3.04.

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This paper aims to explore the research on the human-machine collaboration in the disassembly line balancing problem, in response to the importance of dismantling and reusing waste products. Human-machine collaboration in disassembly, as a method combining human intelligence with machine power, has the potential to enhance disassembly efficiency, reduce resource wastage, and minimize human exposure to hazards. However, due to the NP-hard nature of this problem, traditional exact algorithms perform poorly when facing large-scale and complex problems. Therefore, this paper explores the application of intelligent algorithms such as genetic algorithms, wolf pack algorithms, etc., and proposes novel and efficient solutions to the human-machine collaboration in disassembly line balancing problem. Additionally, to address the challenge of the large-scale problem state space, this paper also discusses the potential application of emerging technologies such as reinforcement learning. Through these studies, this review aims to promote the development of the remanufacturing industry and facilitate resource utilization and environmental protection.

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33

Chao, Bao, Peng Liang, Chaoyong Zhang, and Hongfei Guo. "Multi-Objective Optimization for Mixed-Model Two-Sided Disassembly Line Balancing Problem Considering Partial Destructive Mode." Mathematics 11, no. 6 (March 8, 2023): 1299. http://dx.doi.org/10.3390/math11061299.

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Large-volume waste products, such as refrigerators and automobiles, not only consume resources but also pollute the environment easily. A two-sided disassembly line is the most effective method to deal with large-volume waste products. How to reduce disassembly costs while increasing profit has emerged as an important and challenging research topic. Existing studies ignore the diversity of waste products as well as uncertain factors such as corrosion and deformation of parts, which is inconsistent with the actual disassembly scenario. In this paper, a partial destructive mode is introduced into the mixed-model two-sided disassembly line balancing problem, and the mathematical model of the problem is established. The model seeks to comprehensively optimize the number of workstations, the smoothness index, and the profit. In order to obtain a high-quality disassembly scheme, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The proposed model and algorithm are then applied to an automobile disassembly line as an engineering illustration. The disassembly scheme analysis demonstrates that the partial destructive mode can raise the profit of a mixed-model two-sided disassembly line. This research has significant application potential in the recycling of large-volume products.

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34

Qin, Shujin, Xiaofei Zhang, Jiacun Wang, Xiwang Guo, Liang Qi, Jinrui Cao, and Yizhi Liu. "An Improved Q-Learning Algorithm for Optimizing Sustainable Remanufacturing Systems." Sustainability 16, no. 10 (May 16, 2024): 4180. http://dx.doi.org/10.3390/su16104180.

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In our modern society, there has been a noticeable increase in pollution due to the trend of post-use handling of items. This necessitates the adoption of recycling and remanufacturing processes, advocating for sustainable resource management. This paper aims to address the issue of disassembly line balancing. Existing disassembly methods largely rely on manual labor, raising concerns regarding safety and sustainability. This paper proposes a human–machine collaborative disassembly approach to enhance safety and optimize resource utilization, aligning with sustainable development goals. A mixed-integer programming model is established, considering various disassembly techniques for hazardous and delicate parts, with the objective of minimizing the total disassembly time. The CPLEX solver is employed to enhance model accuracy. An improvement is made to the Q-learning algorithm in reinforcement learning to tackle the bilateral disassembly line balancing problem in human–machine collaboration. This approach outperforms CPLEX in both solution efficiency and quality, especially for large-scale problems. A comparative analysis with the original Q-learning algorithm and SARSA algorithm validates the superiority of the proposed algorithm in terms of convergence speed and solution quality.

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35

Zeng, Yanqing, Zeqiang Zhang, Wei Liang, and Yu Zhang. "Balancing optimization for disassembly line of mixed homogeneous products with hybrid disassembly mode." Computers & Industrial Engineering 185 (November 2023): 109646. http://dx.doi.org/10.1016/j.cie.2023.109646.

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36

Bentaha, M. L., O. Battaïa, and A. Dolgui. "L-shaped Algorithm for Stochastic Disassembly Line Balancing Problem." IFAC Proceedings Volumes 46, no. 9 (2013): 407–11. http://dx.doi.org/10.3182/20130619-3-ru-3018.00500.

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37

Liu, Ming, Xin Liu, Feng Chu, Feifeng Zheng, and Chengbin Chu. "Robust disassembly line balancing with ambiguous task processing times." International Journal of Production Research 58, no. 19 (August 29, 2019): 5806–35. http://dx.doi.org/10.1080/00207543.2019.1659520.

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38

Paksoy, Turan, Aşkıner Güngör, Eren Özceylan, and Arif Hancilar. "Mixed model disassembly line balancing problem with fuzzy goals." International Journal of Production Research 51, no. 20 (October 15, 2013): 6082–96. http://dx.doi.org/10.1080/00207543.2013.795251.

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39

Duta, L., F. Gh Filip, and J. M. Henrioud. "APPLYING EQUAL PILES APPROACH TO DISASSEMBLY LINE BALANCING PROBLEM." IFAC Proceedings Volumes 38, no. 1 (2005): 152–57. http://dx.doi.org/10.3182/20050703-6-cz-1902.01450.

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40

Ren, Yaping, Leilei Meng, Chaoyong Zhang, Qi Lu, and Guangdong Tian. "Multi-criterion decision making for disassembly line balancing problem." Procedia CIRP 80 (2019): 542–47. http://dx.doi.org/10.1016/j.procir.2019.01.008.

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41

Ming, Hao, Quan Liu, and Duc Truong Pham. "Multi-Robotic Disassembly Line Balancing with Uncertain Processing Time." Procedia CIRP 83 (2019): 71–76. http://dx.doi.org/10.1016/j.procir.2019.02.140.

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42

Kalaycılar, Eda Göksoy, Sakine Batun, and Meral Azizoğlu. "A Disassembly Line Balancing Problem with Hazardous Task Failures." IFAC-PapersOnLine 55, no. 10 (2022): 458–63. http://dx.doi.org/10.1016/j.ifacol.2022.09.436.

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43

Altekin, F. Tevhide. "A Piecewise Linear Model for Stochastic Disassembly Line Balancing." IFAC-PapersOnLine 49, no. 12 (2016): 932–37. http://dx.doi.org/10.1016/j.ifacol.2016.07.895.

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44

Cheng, Chen-Yang, Yin-Yann Chen, Pourya Pourhejazy, and Chih-Yu Lee. "Disassembly Line Balancing of Electronic Waste Considering the Degree of Task Correlation." Electronics 11, no. 4 (February 10, 2022): 533. http://dx.doi.org/10.3390/electronics11040533.

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With growing concerns about the depletion of rare-earth elements, managing End-of-Life products has become a key sustainability initiative in the supply chains of global corporations. Recycling, the process of dismantling, separating, and recovery of components and raw materials from wastes, is technologically challenging and should be planned in such a way as to ensure operational efficiency as well as safety. This study explores the Disassembly Line Balancing Problem with Correlated Tasks (DLBP-CT), which is prevalent in the recycling of the Waste of Electrical and Electronic Equipment (WEEE). For this purpose, an original Integer Nonlinear Programming (INLP) model is proposed to find the optimal configuration for the disassembly lines. Given the NP-hard nature of this problem, the Adaptive Genetic Algorithm (AGA) is developed to solve the problem, minimizing the number of workstations and maximizing the relationship between the disassembly tasks. A case example from electronic waste is provided to test the practicality of the developed optimization approach. Sensitivity analysis is conducted to explore the impact of parameter changes in the optimization outcomes. Results are supportive of the applicability of the developed approach and show that it can serve as a strong decision aid tool when selecting the best disassembly process, workstations, and task assignments.

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45

Zhao, Ziyan, Pengkai Xiao, Jiacun Wang, Shixin Liu, Xiwang Guo, Shujin Qin, and Ying Tang. "Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time." Mathematics 12, no. 1 (December 19, 2023): 9. http://dx.doi.org/10.3390/math12010009.

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Disassembling discarded electrical products plays a crucial role in product recycling, contributing to resource conservation and environmental protection. While disassembly lines are progressively transitioning to automation, manual or human–robot collaborative approaches still involve numerous workers dealing with hazardous disassembly tasks. In such scenarios, achieving a balance between low risk and high revenue becomes pivotal in decision making for disassembly line balancing, determining the optimal assignment of tasks to workstations. This paper tackles a new disassembly line balancing problem under the limitations of quantified penalties for hazardous component disassembly and the switching time between adjacent tasks. The objective function is to maximize the overall profit, which is equal to the disassembly revenue minus the total cost. A mixed-integer linear program is formulated to precisely describe and optimally solve the problem. Recognizing its NP-hard nature, a metaheuristic algorithm, inspired by human idea generation and population evolution processes, is devised to achieve near-optimal solutions. The exceptional performance of the proposed algorithm on practical test cases is demonstrated through a comprehensive comparison involving its solutions, exact solutions obtained using CPLEX to solve the proposed mixed-integer linear program, and those of competitive peer algorithms. It significantly outperforms its competitors and thus implies its great potential to be used in practice. As computing power increases, the effectiveness of the proposed methods is expected to increase further.

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46

Zhang, Xuesong, Jing Yuan, Xiaowen Chen, Xingqin Zhang, Changshu Zhan, Amir M. Fathollahi-Fard, Chao Wang, Zhiming Liu, and Jie Wu. "Development of an Improved Water Cycle Algorithm for Solving an Energy-Efficient Disassembly-Line Balancing Problem." Processes 10, no. 10 (September 21, 2022): 1908. http://dx.doi.org/10.3390/pr10101908.

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Nowadays, there is a great deal of interest in the development of practical optimization models and intelligent solution algorithms for solving disassembly-line balancing problems. Based on the importance of energy efficiency of product disassembly and the trend for green remanufacturing, this paper develops a new optimization model for the energy-efficient disassembly-line balancing problem where the goal is to minimize the energy consumption generated during the disassembly-line operations. Since the proposed model is a complex optimization problem known as NP-hard, this study develops an improved metaheuristic algorithm based on the water cycle algorithm as a recently developed successful metaheuristic inspired by the natural water cycle phenomena of diversion, rainfall, confluence, and infiltration operations. A local search operator is added to the main algorithm to improve its performance. The proposed algorithm is validated by the exact solver and compared with other state-of-the-art and recent metaheuristic algorithms. A case study in a turbine reducer with different parameters is solved to show the applicability of this paper. Finally, our results confirm the high performance of the proposed improved water cycle algorithm and the efficiency of our sensitivity analyses during some sensitivity analyses.

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47

Qin, Shujin, Xinkai Xie, Jiacun Wang, Xiwang Guo, Liang Qi, Weibiao Cai, Ying Tang, and Qurra Tul Ann Talukder. "An Optimized Advantage Actor-Critic Algorithm for Disassembly Line Balancing Problem Considering Disassembly Tool Degradation." Mathematics 12, no. 6 (March 12, 2024): 836. http://dx.doi.org/10.3390/math12060836.

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The growing emphasis on ecological preservation and natural resource conservation has significantly advanced resource recycling, facilitating the realization of a sustainable green economy. Essential to resource recycling is the pivotal stage of disassembly, wherein the efficacy of disassembly tools plays a critical role. This work investigates the impact of disassembly tools on disassembly duration and formulates a mathematical model aimed at minimizing workstation cycle time. To solve this model, we employ an optimized advantage actor-critic algorithm within reinforcement learning. Furthermore, it utilizes the CPLEX solver to validate the model’s accuracy. The experimental results obtained from CPLEX not only confirm the algorithm’s viability but also enable a comparative analysis against both the original advantage actor-critic algorithm and the actor-critic algorithm. This comparative work verifies the superiority of the proposed algorithm.

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48

Zhang, Qi, Bin Xu, Man Yao, Jiacun Wang, Xiwang Guo, Shujin Qin, Liang Qi, and Fayang Lu. "An Improved Moth-Flame Algorithm for Human–Robot Collaborative Parallel Disassembly Line Balancing Problem." Mathematics 12, no. 6 (March 11, 2024): 816. http://dx.doi.org/10.3390/math12060816.

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In the context of sustainable development strategies, the recycling of discarded products has become increasingly important with the development of electronic technology. Choosing the human–robot collaborative disassembly mode is the key to optimizing the disassembly process and ensuring maximum efficiency and benefits. To solve the problem of human–robot cooperative parallel dismantling line balance, a mixed integer programming model is established and verified by CPLEX. An improved Moth-Flame Optimization (IMFO) algorithm is proposed to speed up convergence and optimize the disassembly process of various products. The effectiveness of IMFO is evaluated through multiple cases and compared with other heuristics. The results of these comparisons can provide insight into whether IMFO is the most appropriate algorithm for the problem presented.

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Guo, XiWang, Shujin Qin, Liang Qi, Qi Zhang, Weishuang Bai, and Jiacun Wang. "An Improved Adaptive Genetic Algorithm for U-shaped Disassembly Line Balancing Problem Subject to Area Resource Constraint." JOURNAL OF Cyber-Physical-Social Intelligence 1 (December 15, 2022): 47–59. http://dx.doi.org/10.61702/vlzk2105.

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The disassembly, recovery, and reuse of waste products are attracting more and more attention. It not only saves resources and protects the environment but also promotes economic development. In a disassembly process, the disassembly line balancing problem is one of the most important problems.At present, the consideration of the space area of workstations is relatively small, and the relatively large use of the area of workstations can also better reduce costs. Aiming at the balancing problem of u-shaped disassembly line, a single-objective optimization mathematical model with area constraints is established with the goal of maximizing proﬁts. In order to solve this problem, we refer to Adaptive Genetic Algorithm and improve its crossover and mutation operator. We adopt elite strategy to avoid premature convergence and improve the global search ability. Its effectiveness is proved by comparison with the optimization results of CPLEX. Experimental results also verify the feasibility of the proposed model and the superiority of the improved Adaptive Genetic Algorithm when solving large-scale instances over another algorithm. At the same time, the experimental results also verify the superiority and effectiveness of the improved Adaptive Genetic Algorithm algorithm by comparing with Random Search.

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50

Guo, XiWang, Shujin Qin, Liang Qi, Qi Zhang, Weishuang Bai, and Jiacun Wang. "An Improved Adaptive Genetic Algorithm for U-shaped Disassembly Line Balancing Problem Subject to Area Resource Constraint." JOURNAL OF Cyber-Physical-Social Intelligence 1 (December 15, 2022): 47–59. http://dx.doi.org/10.61702/zlas2069.

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The disassembly, recovery, and reuse of waste products are attracting more and more attention. It not only saves resources and protects the environment but also promotes economic development. In a disassembly process, the disassembly line balancing problem is one of the most important problems.At present, the consideration of the space area of workstations is relatively small, and the relatively large use of the area of workstations can also better reduce costs. Aiming at the balancing problem of u-shaped disassembly line, a single-objective optimization mathematical model with area constraints is established with the goal of maximizing proﬁts. In order to solve this problem, we refer to Adaptive Genetic Algorithm and improve its crossover and mutation operator. We adopt elite strategy to avoid premature convergence and improve the global search ability. Its effectiveness is proved by comparison with the optimization results of CPLEX. Experimental results also verify the feasibility of the proposed model and the superiority of the improved Adaptive Genetic Algorithm when solving large-scale instances over another algorithm. At the same time, the experimental results also verify the superiority and effectiveness of the improved Adaptive Genetic Algorithm algorithm by comparing with Random Search.

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Journal articles on the topic 'Disassembly line balancing'

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