Disassembly Line Balancing by Using Simulation Optimization

Human-robot collaborative disassembly lines are widely used by remanufacturing companies to disassemble end-of-life (EOL) products. When disassembling large-sized EOL products, each workstation on a disassembly line is generally divided into multiple operating positions, so that different operators can disassemble the same product at their respective positions at the same time, thereby greatly improving efficiency. This paper focuses on a position-constrained human-robot collaborative disassembly planning (PC-HRCDP) problem for the above-mentioned lines, including three subproblems of disassembly sequence planning, disassembly line balancing and robot path planning. A multi-objective mixed integer programming model for PC-HRCDP is developed to solve small-scale instances. Furthermore, a multi-objective multi-fidelity optimisation (MO-MFO) algorithm is proposed to solve large-scale instances. Comprehensive experiments are conducted based on 10 problem instances generated in this study. Experimental results show that the proposed MO-MFO is better than a high-fidelity optimisation algorithm in terms of running time. In addition, benefiting from the strategy of MO-MFO to allocate the limited high-fidelity computational budget to solutions in the two stages of multi-objective optimisation and optimal sampling, MO-MFO is significantly better than the existing representative multi-fidelity optimisation algorithms in terms of the hyper-volume and the inverted generational distance.

Disassembly operations are inevitable elements of product recovery with the disassembly line as the best choice to carry out the same. The product recovery operations are fully based on disassembly line balancing and because of this, disassembly lines have become the chaise of automated disassembly of returned product. It is difficult to find the optimal balance of a disassembly line because of its N-P hard nature. In this paper a new heuristic is proposed to assign the parts to the disassembly workstations under AND/OR precedence constraints. The heuristic solutions are known as intrinsically optimal/suboptimal solutions of the N-P hard problems. The solution obtained by proposed heuristic has been compared with other heuristic solutions. The heuristic tries to minimize the number of workstations and the cycle time of the line while addressing the different criteria. The methodology of the proposed heuristics has been illustrated with the help of examples and it has been observed that the heuristic generates significantly better result.

PurposeThe main objective of this paper is to integrate quality with triple bottom line dimensions as well as economic, social and environmental concerns within a disassembly line balancing problem.Design/methodology/approachThis study presents a new disassembly line balancing model to maintain the triple bottom line. This proposed model applies a hybrid MCDM considering social, environmental, quantitative and qualitative business and quality dimensions. In this research, fuzzy AHP prioritizes the dimensions and fuzzy TOPSIS ranks the disassembly operations.FindingsThe findings show that the disassembly line’s cycle time decreased from 36 s to 32 s, with the main and sub-criteria considered. Therefore, this study reveals that integrating sustainability dimensions with quality may not result in sacrificing cycle time.Originality/valueGlobalization, the increase in demand, the complexity of supply chains and the environmental, economic and social dimensions should be examined together for these operations to be sustainable. Not only these dimensions but also the perception of quality, an essential aspect of production, must be reflected in the disassembly line balancing. There is a need for sustainability issues in disassembly line balancing, which is becoming increasingly important due to environmental concerns, resource conservation, corporate social responsibility and potential cost savings. However, a significant gap in the literature points to the need to cover all three dimensions of the triple bottom line and other production-related dimensions in the disassembly line balancing problem.

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.

With the advancement of science and technology in recent years, the rate of product upgrading by end users has increased, resulting in a high number of End-Of-Life (EOL) products. For environmental protection and economic benefits, disassembly lines are set up to recycle them. A disassembly line balancing problem arises and has attracted widespread attention. In this paper, we design a model to address a class of disassembly line balancing problems in which the main optimization objective is to maximize profits, while keeping carbon emission in a tolerable range. A discrete whale optimization algorithm is proposed to search their high-quality solutions. Its efficiency is verified by comparing its solutions with their analytic solutions obtained from a commercial optimization solver and other swarm intelligence algorithms. Experimental results show that it is significantly faster than CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while guaranteeing its solutions quality.

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing.

With the sharp increase in the number of products and the development of the remanufacturing industry, disassembly lines have become the mainstream recycling method. In view of the insufficient research on the layout of multi-form disassembly lines and human factors, we previously proposed a linear-U-shaped hybrid layout considering the constraints of employee posture and a Duel-DQN algorithm assisted by Large Language Model (LLM). However, there is still room for improvement in the utilization efficiency of workstations. Based on this previous work, this study proposes an innovative layout of U-shaped and circular disassembly lines and retains the constraints of employee posture. The LLM is instruction-fine-tuned using the Quantized Low-Rank Adaptation (QLoRA) technique to improve the accuracy of disassembly sequence generation, and the Dueling Deep Q-Network(Duel-DQN) algorithm is reconstructed to maximize profits under posture constraints. Experiments show that in the more complex layout of U-shaped and circular disassembly lines, the iterative efficiency of this method can still be increased by about 26% compared with the traditional Duel-DQN, and the profit is close to the optimal solution of the traditional CPLEX solver, verifying the feasibility of this algorithm in complex scenarios. This study further optimizes the layout problem of multi-form disassembly lines and provides an innovative solution that takes into account both human factors and computational efficiency, which has important theoretical and practical significance.

Multi-objective partial parallel disassembly line balancing problem using hybrid group neighbourhood search algorithm

In order to protect the environment, an increasing number of people are paying attention to the recycling and remanufacturing of EOL (End-of-Life) products. Furthermore, many companies aim to establish their own closed-loop supply chains, encouraging the integration of disassembly and assembly lines into a unified closed-loop production system. In this work, a hybrid production line that combines disassembly and assembly processes, incorporating human–machine collaboration, is designed based on the traditional disassembly line. A mathematical model is proposed to address the human–machine collaboration disassembly and assembly hybrid line balancing problem in this layout. To solve the model, an evolutionary learning-based whale optimization algorithm is developed. The experimental results show that the proposed algorithm is significantly faster than CPLEX, particularly for large-scale disassembly instances. Moreover, it outperforms CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while maintaining high solution quality.

This paper addresses the problem of profit-oriented disassembly line design and balancing considering partial disassembly, presence of hazardous parts and uncertainty of task processing times. Few papers have studied the stochastic disassembly line balancing problem and existing approaches have focused on heuristic and metaheuristic methods. Most existing work has concentrated on complete disassembly where task times are assumed to be normal random variables and where AND/OR graphs are not considered. The objective of this paper is the design of a serial line that obtains the maximum revenue and then balances the workload under uncertainty. The processing time of a disassembly task is assumed to be a random variable with any known probability distribution. An AND/OR graph is used to model the precedence relationships among tasks. Stochastic programming models and exact-based solution approaches combining the L-shaped algorithm and Monte Carlo sampling techniques are proposed. The relevance and applicability of the proposed models and solution methods are shown by solving efficiently a set of disassembly problem instances from the literature.

Electricity production via wind power is a significant manner for both local and global considerations by reduced carbon emission, falling energy cost, job creation, full energy access, and improved energy security. Wind power is currently the most desirable way to produce electricity and its usage rapidly increases worldwide. When the given proportions are considered in the total mass of the wind turbine, the utilized materials indicate a considerable number of raw materials. With respect to the aforementioned facts, the question arises as to the recycling process of the wind turbines. By considering the average life span of wind turbines (20-25 years), wind turbine recycling can be expressed as an inevitable issue that will be confronted in a mid-term timescale globally. Modeling and balancing the disassembly line occupies a crucial step in the recycling of wind turbines. In this study, an integer programming approach of the disassembly line balancing model is applied to end-of-life wind turbines. Moreover, different scenarios are performed by altering the cycle times to observe its effect on the number of open workstations. The results show that end-of-life wind turbines can be disassembled systematically and optimally.

Column generation approach for disassembly line balancing

Selection of an optimal disassembly sequence is essential for the efficient processing of a product at the end of its life. Disassembly sequences are listings of disassembly actions (such as the separation of an assembly into two or more subassemblies, or removing one or more connections between components). Disassembly takes place in remanufacturing, recycling, and disposal with a disassembly line being the best choice for automation. In this paper, the disassembly sequencing problem is solved for a cell phone case on a disassembly line, seeking a sequence which is feasible, minimizes the number of workstations (and hence idle times), provides for early removal of high demand/value parts, provides the removal of parts that lead to the access of greatest number of still-installed parts, and early removal of hazardous parts as well as for the grouping of parts for removal having identical part removal directions. Since finding the optimal sequence is computationally intensive due to factorial growth, a heuristic method is used taking into account various disassembly-specific matters. Using the experimentally determined precedence relationships and task times of a real-world cell phone, a MATLAB program is designed and a sequencing solution is generated. Finally, Design for Disassembly (DFD) improvements are recommended with respect to environmentally conscious manufacturing.

Disassembly lines have become one of the most suitable ways for the disassembly of large products or small products in large quantities for efficient working of disassembly line; its design and balancing is prudent. In disassembly lines, task assignment in appropriate schedule is necessary for designing and balancing the line. In this paper, a heuristic based on multi criteria decision-making (MCDM) technique has been proposed for assignment of tasks to the disassembly workstations. In the proposed heuristic, Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been used for the prioritizing of task for the assignment to workstations. The proposed heuristic has been compared to other heuristic and it has been found that it performs well and gives sufficiently better results.

In present era of environmentally conscious manufacturing, End-of-life (EOL) product disassembly is increasingly used for the product recovery process. However, the low efficiency in disassembly process put a great challenge in front of manufacturers to achieve the environmental goals set for the product. To achieve the efficient utilization in part recycling processes at the end of its life, disassembly line has important role to play. Thus, much emphasis is given by the research community to reduce the company reliance on the availability of human experts to improve the intelligence in the disassembly process. This research aims to optimize the number of machine required to disassemble the parts in a disassembly line. The complexity while disassembly increases in case of defects in the tasks to be allocated on the workstation. In this paper an evolutionary approach named Psycho-Clonal is proposed to solve the disassembly line-balancing problem in the presence of task failure. The proposed algorithm is inspired by artificial immune systems (AIS), which is further enhanced by the authors by incorporating Maslow’s need hierarchy theory work as a meta-heuristic. The main attributes of the algorithm are the various need levels, immune memory, and affinity maturation. Need levels incorporated in the algorithm help in satisfying the model constraints, thus empowering the algorithm to work for a multi-objective, multi-constraints problem. Immune memory helps in preserving the best solution and affinity maturation helps in exploring and exploiting the search space.Copyright © 2005 by ASME