Disassembly Line Balancing Research Articles

Discrete Bat Optimizer for Disassembly Line Balancing Problem

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.

Discrete Bat Optimizer for Disassembly Line Balancing Problem

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.

Mathematical formulation and an improved moth–flame optimization algorithm for parallel two-sided disassembly line balancing based on fixed common stations

Abstract Nowadays, rapid product iterations result in large quantities of end-of-life products. To meet the fast-growing demand for remanufacturing engineering, companies have quickened the standardization and industrialization of waste dissembling. Two-sided disassembly lines can effectively disassemble large-sized products on both sides of the lines, and parallel disassembly lines can disassemble multiple products simultaneously with fewer workstations and higher production efficiency. Combining the two types of disassembly can effectively increase the disassembly efficiency of large-sized products. However, the parallel two-sided disassembly line has not been fully investigated because of the essential complexity of the problem. Therefore, this research introduced the parallel two-sided disassembly line balancing problem with fixed common stations. First, a multi-objective mixed-integer programming model is established to solve the problem for the first time. The model is proved to be correct through small-scale numerical examples. Second, a multi-objective improved moth–flame optimization algorithm is implemented to solve the proposed large-scale problems. The proposed algorithm employs a two-phase decoding approach to design the scheme and a discrete moth for fire operation to search and replace new individuals, and then a restart strategy is introduced to reduce the probability of the population falling into a local optimum. Finally, the algorithm solved extensive disassembly line balancing problems with different layouts, including the straight-line, two-sided, and parallel two-sided, and case studies demonstrated the reliability and validity of the proposed method.

Multimanned partial disassembly line balancing optimization considering end-of-life states of products and skill differences of workers

End-of-life states of waste products and skill differences of workers are the important factors affecting the production efficiency of disassembly lines and the workload balance of workers. The comprehensive effect of these two factors is considered for the first time in this study. The existing multimanned disassembly line balancing studies have focused on complete disassembly, i.e., all parts must be removed, while this study proposes a multimanned partial disassembly line balancing problem (MP-DLBP) to allow the worthless parts not to be removed and thus avoid invalid work and labour waste. Then, a mixed-integer programming (MIP) model is established to minimize cycle time and idle balancing index and maximize disassembly profit under the fixed number of open workstations. To obtain excellent solutions, an improved NSGA-II based on an incentive strategy (INSGA-II) is proposed to optimize the large-scale MP-DLBPs. Correctness of the model and INSGA-II are verified by comparing the experimental results of a demo product solved by CPLEX and INSGA-II. Superior performance of the INSGA-II for other DLBPs is verified by comparing the results of two actual disassembly instances and 19 generated instances of different scales solved by various algorithms in the existing literature. Finally, the INSGA-II is applied to the MP-DLBP of scrap printers. The superiority of INSGA-II for the printer case and the enhancement effect of incentive strategy are verified by comparing the optimization results with other two algorithms and their improved algorithms. Further, the relative conflict relationship between disassembly profit and idle balancing index is revealed, and the Pareto optimal solution set is provided for decision-makers.

Robotic disassembly line balancing and sequencing problem considering energy-saving and high-profit for waste household appliances

Waste household appliances contain hazardous substances and valuable components. The timely disposal of these waste products can avoid environmental pollution and improve the recycling rate of resources. Robotic disassembly can realize efficient and precise disassembly to achieve sustainable and clean production. Reasonable allocation of robots and disassembly tasks is the key to achieving efficient production. Reducing energy consumption and improving profits are the focus of the government and enterprises. The tool replacement of the robot needs to be considered in the actual disassembly process, which increases the difficulty of the problem. This study establishes a multi-objective robotic disassembly line balancing and sequencing model to quantify the problem. The model aims to shorten the disassembly completion time and perform disassembly operations with energy-saving and high-profit. An improved genetic simulated annealing algorithm based on problem characteristics is designed. The superiority of the proposed algorithm is verified by solving 21 benchmarks with the number of tasks ranging from 7 to 148 and comparing with five algorithms. In a disassembly case of waste household appliances represented by a microwave oven, 66 disassembly schemes are obtained for decision makers to choose from different emphases. The results show that these schemes can not only achieve efficient and green disassembling but also obtain higher disassembly profits.

Modeling and Balancing for Disassembly Lines Considering Workers With Different Efficiencies.

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

Transfer Learning-Assisted Evolutionary Dynamic Optimisation for Dynamic Human-Robot Collaborative Disassembly Line Balancing

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.

Multi-mechanism-based modified bi-objective Harris Hawks optimization for sustainable robotic disassembly line balancing problems

Due to the increasingly severe resource scarcity and environmental pollution, appropriate recovery of end-of-life (EOL) products has gained significant importance in recent years. The disassembly lines are one of the vital steps during the entire recovery process. However, considering the disadvantages of manual disassembly lines such as high labor cost, low efficiency, and harm to workers’ health, the transition to robotic disassembly lines is underway. Therefore, in this paper, a sustainable robotic disassembly line balancing problem (RDLBP) is proposed to improve the disassembly efficiency (cycle time) and environmental friendliness (total energy consumption) simultaneously. To narrow the gap between theory and practice, uncertain processing time, sequence-based tool changeover, and robots with different efficiencies and energy consumption rates are considered, which requires adding the steps of resequencing the tasks and allocating the most suitable robots within each workstation on the basis of assigning the tasks to workstations as evenly as possible. To solve this problem, the epsilon constraint algorithm is used to obtain the exact solutions for small scale cases and verify the validity of the proposed model. Simultaneously, due to the NP-hard nature, a modified bi-objective Harris Hawks optimization (MBOHHO) algorithm is developed to solve the line balancing problem for the first time, which combines opposition-based learning, differential evolution, and Gaussian mutation mechanisms, and modifies the renewal strategy of two vital parameters based on bioenergy consumption pattern, contributing to improve the diversity and help jump out of local optimum. Finally, computational experiments are performed to evaluate the performance of the MBOHHO algorithm by comparing it with four other outstanding optimization algorithms, and the results reveal its effectiveness and superiority in various metrics.

Modeling and solving the parallel mixed-flow remanufacturing disassembly line balancing problem for multi-variety products

The types and numbers of components in end-of-life (EOL) products are often uncertain during remanufacturing, leading to low disassembly efficiencies for traditional remanufacturing disassembly lines. To address this problem, a parallel mixed-flow workstation layout was designed, and a novel parallel mixed remanufacturing disassembly line balancing optimization method for multi-variety products was proposed. A mixed-flow product disassembly task hierarchical assignment matrix was constructed to perform disassembly task allocations for similar components. Furthermore, a parallel mixed-flow remanufacturing disassembly line balancing (PMRDLB) optimization model was developed with the optimization objectives of minimizing the number of workstations, the disassembly line balancing rate, and the remanufacturing value indexes of the components. Furthermore, the multi-objective non-dominated genetic optimization method (NSGA-III) was improved, in which a chromosome construction method, based on the parallel mixed-flow disassembly task allocation matrix, was proposed to conduct mapping between the chromosomes and the PMRDLB model. In addition, non-dominated solution sorting was performed based on a Pareto hierarchy, which increased the searching rate of the algorithm during optimization. Finally, a case study verified the effectiveness and feasibility of the proposed method.

A Discrete Artificial Bee Colony Algorithm for Multiobjective Disassembly Line Balancing of End-of-Life Products.

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

An integrated approach for a new flexible multi-product disassembly line balancing problem

Flexible disassembly line design for end-of-life (EOL) products is a key issue in the remanufacturing industry. However, existing studies for disassembly line balancing have not simultaneously considered multiple EOL products, the identical parts of these products and uncertainty during disassembly, which are important characteristics of flexible disassembly lines. The present study addresses a new flexible multi-product disassembly line balancing problem in which (1) disassembly schemes need to be selected, (2) a workstation can disassemble multiple EOL products, (3) identical parts of multiple products can be treated as identical tasks, and (4) only partial probability distribution information of processing times is known. For the problem, an integrated approach is developed, which is composed of a chance-constrained program, a distribution-free model, efficient valid inequalities and an exact lifted cut-and-solve method. Numerical experiments are conducted on an illustrative example, 10 instances based on realistic products and 480 randomly generated instances with up to 20 products, 400 tasks and 86 workstations. Computational results show that the proposed valid inequalities can reduce about 75% computational time of the original model, and the lifted cut-and-solve method needs only 17.53% and 40.65% of the computational times required by the CPLEX and the classic cut-and-solve method, respectively.

Stochastic Hybrid Discrete Grey Wolf Optimizer for Multi-Objective Disassembly Sequencing and Line Balancing Planning in Disassembling Multiple Products

Recycling, reusing, and remanufacturing of end-of-life (EOL) products have been receiving increasing attention. They effectively preserve the ecological environment and promote the development of economy. Disassembly sequencing and line balancing problems are indispensable to recycling and remanufacturing EOL products. A set of subassemblies can be obtained by disassembling an EOL product. In practice, there are many different types of EOL products that can be disassembled on a disassembly line, and a high-level uncertainty exists in the disassembly process of those EOL products. Hence, this paper proposes a stochastic multi-product multi-objective disassembly-sequencing-line-balancing problem aiming at maximizing disassembly profit and minimizing energy consumption and carbon emission. A simulated annealing and multi-objective discrete grey wolf optimizer with a stochastic simulation approach is proposed. Furthermore, real cases are used to examine the efficiency and feasibility of the proposed algorithm. Comparisons with multi-objective discrete grey wolf optimization, non-dominated sorting genetic algorithm II, Multi-population multi-objective evolutionary algorithm, and multi-objective evolutionary algorithm demonstrate the superiority of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Disassembly line balancing has been widely recognized as the most ecological way of retrieving EOL products. Through in-depth research, we present a Stochastic Multi-product Multi-objective Disassembly-sequencing-line-balancing Problem. Furthermore, we consider that the uncertainty of products might cause disassembly failure. To solve this problem effectively and quickly, we combine the simulated annealing algorithm with the Grey Wolf Optimizer. The results show that the algorithm can effectively solve the proposed problem. The disassembly scheme provided by the obtained solution set offers a variety of options for decision-makers.

A novel constraint programming and simulated annealing for disassembly line balancing problem with AND/OR precedence and sequence dependent setup times

This study considers recycling the products to reuse or reproduce the precious parts of these products to provide environmental sustainability and for companies to adapt to competitive conditions. These recycling processes include the dismantling of the products using the disassembly lines. Disassembly line balancing problems contribute to these processes by ensuring that products are disassembled efficiently and at minimum cost. Therefore, these problems have started to attract more attention in the literature. This study considers the disassembly line balancing problem, with sequence-dependent setup time and complex AND/OR precedence relations between tasks. The managerial impacts highlighted by the proposed problem are of great importance both from environmental and industrial sustainability due to the recycling process of the life-threatening wastes. The study aims to disassemble the products using a minimum number of workstations. Mixed-integer linear programming and constraint programming models have been developed for the considered problem in question. As far as is known, the sequence-dependent disassembly line balancing problem, which has complex precedence relations, has not been studied in the literature before, and the constraint programming model developed is also novel. The study also compares the constraint programming model with/without an initial solution. In addition, a simulated annealing metaheuristic is proposed to solve the problem, and its results are compared with the results of the models. Setup times were generated for well-known instances obtained from the literature. The results obtained using these instances showed that the constraint programming model successfully solved the problem under consideration.

A note on integrated disassembly line balancing and routing problem

This note comments on the study of [Diri Kenger, Zülal, Çağrı Koç, and Eren Özceylan. 2020. “Integrated Disassembly Line Balancing and Routing Problem.” International Journal of Production Research 58 (23): 7250–7268.] which studies an integrated disassembly line balancing and routing problem and develops two mixed integer linear programming (MILP) models and three mixed integer nonlinear programming (MINLP) models to handle five different scenarios, respectively. The purpose is twofold. First, we demonstrate that the two MILP models can be separated into two parallel subproblems whose optimal solutions can be combined to obtain the optimal solution of the original models. Second, we show that the three MINLP models can be linearised and propose two different linearisation techniques to reformulate them as equivalent MILP models. Computational results indicate that the linearised model outperforms the original nonlinear model in most cases.

Disassembly Line Balancing of Electronic Waste Considering the Degree of Task Correlation

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.

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

Remanufacturing is an important method to realize resource saving and circular economy. Disassembly is a vital step of remanufacturing and it is always finished by manual labor which is low efficiency. Although robotic disassembly covers the shortages of manual disassembly, it is still difficult for the robots to independently finish the disassembly process without the operator's assistance. The human-robot collaborative disassembly is being paid increasing attention and the human-robot collaborative disassembly line balancing is to assign suitable disassembly tasks to each human-robot collaborative disassembly workstation in a balanced manner. In human-robot collaborative disassembly, the operator's safety should be considered when the robot collaboratively executes the disassembly tasks with the operator. In this paper, disassembly information model of human-robot collaborative disassembly considering the safe strategy is studied. After that, this paper solves the human-robot collaborative disassembly line balancing problem using the improved discrete bees algorithm. To verify the proposed method, case study based on a bearing coupler is carried out. The results show the proposed improved discrete bees algorithm can generate the optimal solutions of human-robot collaborative disassembly line balancing problem considering the safe strategy and finds better solutions compared with the other optimization algorithms in terms of running time, average generational distance and Pareto fronts.

Mixed integer programming approaches to partial disassembly line balancing and sequencing problem

Product recovery has received greater attention in recent years mainly due to increased environmental awareness of consumers and stricter environmental regulations imposed by governments. In product recovery, disassembly of the product into its constituent parts is the most significant activity and generally performed on a disassembly line. During disassembly, a complete or partial disassembly of the product may be preferred. In complete disassembly, all parts must be disassembled, while partial disassembly allows to disassemble a subset of parts (e.g., the ones with relatively high revenues). This study deals with a partial disassembly line balancing and sequencing (PDLBS) problem considering revenues of parts to be disassembled, general workstation cost, additional cost of workstation(s) with hazardous parts, and cost of direction changes. For the PDLBS problem, a generic mixed integer programming (MIP) model, with the aim of maximizing total profit, is developed. To strengthen the MIP formulation, two sets of valid inequalities are proposed. The computational results show that the MIP model with valid inequalities is able to provide optimal solutions for the PDLBS problems with up to 30 tasks. To obtain near-optimal solutions for large-sized problems, a MIP-based solution approach is proposed. The proposed approach decomposes the entire MIP model into selection and assignment (SA) and sequencing (SEQ) models. The SA model is an exact relaxation of the MIP model (with valid inequalities) obtained by removing all the sequencing variables and constraints. Hence, SA model also produces an efficient upper bound for the PDLBS problem. The SEQ model, accordingly, aims to find an optimal sequence of tasks subject to the fixed selection and assignment of tasks provided by the SA model. The computational results show that the proposed MIP-based solution approach provides efficient solutions with small optimality gaps for large-sized problems.

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.

An exact solution method for multi-manned disassembly line design with AND/OR precedence relations

Specific government regulations for waste products and increasing environmental awareness are concerns for companies. Disassembly operations have become essential tools for green manufacturing and for reducing ecological hazards from waste electrical and electronic equipment. An effective and efficient line design may be vital to encourage companies to establish a disassembly center. The line-balancing problem is a critical issue in a disassembly center. Large products may allow simultaneous disassembly of parts using more than one operator at a workstation. However, multi-manned disassembly line balancing creates a more complicated problem. Thus, an efficient solution technique based on a constraint programming (CP) approach is proposed for multi-manned disassembly line balancing with AND/OR precedence relations to minimize cycle time as a primary objective and the total number of workers as a secondary objective. A mixed-integer linear programming (MILP) model based on previous studies was proposed to define the problem. A CP approach was developed for the first time to solve this problem. A genetic algorithm was used to show the relative performance of the CP method for large problems. The proposed CP approach demonstrates superior performance.

Disassembly Line Balancing by Using Simulation Optimization

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.