Line Balancing Problems Research Articles

Exploring engineering applications of two-sided partial destructive disassembly line balancing problems under electrical limiting and time-of-use pricing

With the rising demand for energy and resource shortages, disassembly as energy-intensive industry should be more attention to energy conservation. However, the non-disassemblability of components (CND) and fixed-constant disassembly costs make this process relatively expensive compared to the potential profit. This paper introduces a two-sided partial destructive disassembly model to solve problem of CND. To more accurately reflect real-world conditions, this study incorporates into the model, which was not considered in previous work. A mixed integer planning model is developed by exploring the impact of time-of-use tariffs on energy consumption and profit, and its correctness is verified in a small-scale case. Subsequently, we propose an improved water cycle algorithm (IWCA) to increase the model solving efficiency. Using non-destructive, destructive, and human-robot hybrid disassembly as benchmark cases, we compare 11 algorithms from existing research, and verify the superiority of IWCA in solving different cases. Finally, the proposed method is validated using automotive engine as an example. The results show that: (1) IWCA is superior to the four latest algorithms in solving the two-sided partially destructive disassembly line balancing problem under electrical limiting and time-of-use electricity pricing. (2) The two-sided partial destructive disassembly effectively addresses the CND issue, reducing the smoothing index by 10.24% and increasing disassembly profit by 3.19% compared to other algorithms. (3) The partial destructive disassembly is better suited for addressing issues in real production processes than non-destructive or complete destructive disassembly methods.

A line balancing problem with parallel workers and cycle time minimization

We study the problem of balancing assembly lines with parallel workers, motivated by features observed at a third-party logistics provider. The assembly line differs from the two known types of line balancing problems in the literature since it determines simultaneously the line cycle time and the number of workers per station. Furthermore, each station can be occupied by more than one worker and a restriction on the minimum number of stations is imposed. This new type of line balancing problem minimizes the line cycle time, where two types of decisions must be made: how to group the tasks into stations and how many workers to assign to each station. We adapt mathematical models based on assembly line balancing problems with parallel stations. Furthermore, we propose a new model based on network flow formulations for the special case of lines with a serial task structure, frequently observed at the company. We perform an extensive computational study with realistic instances in order to compare the speed of solving different formulations, which is important as such a problem is at the operational level. We also provide a sensitivity analysis of key parameters aiming to better understand the trade-offs and provide meaningful managerial insights.

Multi-manned disassembly line balancing problems for retired power batteries based on hyper-heuristic reinforcement

Considering the complexity and uncertainty of retired power batteries, worker disassembly can reduce the cost and increase the flexibility of the disassembly process. To address the problem of considerable waste products, we investigate a multi-manned disassembly line balancing problem with different work modes (MWM-DLBP). In addition, we explore the impact of variability in penalizing factors (earliness and tardiness) on costs when products of different scales need to be disassembled. Given the NP-hard of the problem and unpredictable situations, we propose a reinforcement learning-based Hyper-Heuristics (RLHHs) algorithm to solve the practical issues. A Markov model is constructed to generate a sequence of low-level metaheuristic algorithms, and a multi-armed bandit (MAB) method is designed to tune different critical parameters intelligently. The superiority of RLHHs is verified by comparing the optimization results with other algorithms. Further, results reveal that the new disassembly scheme can save an average cost of 9.81% compared to the original system design. Finally, we provide manufacturers with the specific workforce planning and working mode for disassembly tasks under different scenarios.

An integrated ergonomic risk assessment framework based on fuzzy logic and IVSF-AHP for optimising ergonomic risks in a mixed-model assembly line

This study proposes a systematic approach to address ergonomic factors, including physical, environmental and psychosocial aspects, in solving assembly line balancing problems. A three-stage framework is developed, starting with determining weights for ergonomic risk assessment methods using the interval-valued spherical fuzzy analytical hierarchy process. In the second stage, a fuzzy logic model for integrated ergonomic risk assessment is constructed based on these weights, and the integrated ergonomic risk score is determined. In the third stage, a mathematical model is formulated to minimise the cycle time while balancing the ergonomic risk level. A case study conducted in a wire harness factory validated the effectiveness of the proposed approach, showing a 10–11% improvement in line efficiency and a 12–25% enhancement in ergonomic risk balancing performance. These findings underscore the potential benefits of implementing this approach, which can significantly improve occupational safety and overall performance.

Human–Robot Collaborative Disassembly Line Balancing Problem With Stochastic Operation Time and a Solution via Multi-Objective Shuffled Frog Leaping Algorithm

Human–Robot Collaborative Disassembly Line Balancing Problem With Stochastic Operation Time and a Solution via Multi-Objective Shuffled Frog Leaping Algorithm

An improved ant colony optimization algorithm for two-sided U-type assembly line balancing problems

Aiming at the improvement of production system, a new method of two-sided U-type assembly line balance is proposed. Define the partition of a two-sided U-type assembly line and derive the formula for the task start time. The multi-objective model of two-sided U-type assembly line balance is established by minimizing a smoothing index, the number of positions and stations. Considering the dual influence of heuristic factors and pheromone information, the improved ant colony optimization algorithm is proposed. Combined with 37 examples, the improved ant colony optimization algorithm is used to solve the balancing problem of a two-sided straight line and a two-sided U-type assembly line, respectively. Compared with the results of other algorithms, it verifies that the model is reasonable and the improved ant colony optimization algorithm is effective. The balance effect of a two-sided U-type assembly line is better than that of a two-sided straight line, which means the length of the assembly line is shortened.

A knowledge-guided Estimation of Distribution Algorithm for energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem

The assembly line serves as a fundamental system in discrete production. To address the challenges in balancing robotic assembly lines, timely part feeding, and the need for sustainable manufacturing, this paper studies an energy-efficient Joint Robotic Assembly Line Balancing and Feeding Problem (JRALB-FP) with the criteria of minimizing both cycle time and total fuel consumption cost. Considering the complexity of the multi-problem and multi-objective optimization, a knowledge-guided Estimation of Distribution Algorithm (KEDA) is proposed to solve energy-efficient JRALB-FP. First, a probability model of EDA for task-workstation allocation paired with a heuristic method-based sampling mechanism is created. Using this probability model, a specific encoding mechanism is designed for part-trailer allocation, and good initial solutions are produced. Second, several properties of the bi-objective problem are analyzed to guide the design of local search operators for both objectives optimization. Third, the updating mechanism of the probability model is designed to learn from the elite solutions. Fourth, two knowledge-guided local search operators are designed and implemented to exploit better non-dominated solutions sufficiently. A design of experiment is carried out to determine the parameters. Extensive computational tests and comparisons with the state-of-the-art multi-objective algorithms are carried out, which verify the effectiveness of the knowledge-guided local search operators, the problem-oriented heuristic-based sampling mechanism, and the special designs of the KEDA in solving the energy-efficient JRALB-FP.

MULTI-OBJECTIVE GENETIC ALGORITHM FOR THE ASSEMBLY LINE WORKER ASSIGNMENT AND BALANCING PROBLEM: A CASE STUDY IN THE AUTOMOTIVE SUPPLY INDUSTRY

The primary challenge in assembly line design is the need for more appropriately allocating tasks and workers to workstations. This study addresses the problem of line balancing and worker assignments, considering the performance disparities among workers during the line balancing process. In the relevant literature, this problem is known as the Assembly Line Worker Assignment and Balancing (ALWAB) problem. This research examines a multi-objective ALWAB Type-2 problem, simultaneously evaluating cycle time and squared load assignment objectives. The study is conducted based on a real-life scenario in a sub-industry automotive industry that manufactures cable equipment. To solve this problem, a multi-objective genetic algorithm approach is proposed. Recognising that the selection of parameter values will influence the algorithm’s performance, parameter calibration has been performed. A full factorial experimental design and the irace method have been utilised for this purpose. The results are compared with those using parameter values utilised for similar problems in the literature. Furthermore, a sensitivity analysis has been carried out to examine the impact of various relative weight values of the objectives on the result. The results indicate that the experimental design generally yields superior results compared to other methods.

Generally Applicable Q-Table Compression Method and Its Application for Constrained Stochastic Graph Traversal Optimization Problems

We analyzed a special class of graph traversal problems, where the distances are stochastic, and the agent is restricted to take a limited range in one go. We showed that both constrained shortest Hamiltonian pathfinding problems and disassembly line balancing problems belong to the class of constrained shortest pathfinding problems, which can be represented as mixed-integer optimization problems. Reinforcement learning (RL) methods have proven their efficiency in multiple complex problems. However, researchers concluded that the learning time increases radically by growing the state- and action spaces. In continuous cases, approximation techniques are used, but these methods have several limitations in mixed-integer searching spaces. We present the Q-table compression method as a multistep method with dimension reduction, state fusion, and space compression techniques that project a mixed-integer optimization problem into a discrete one. The RL agent is then trained using an extended Q-value-based method to deliver a human-interpretable model for optimal action selection. Our approach was tested in selected constrained stochastic graph traversal use cases, and comparative results are shown to the simple grid-based discretization method.

An Improved Moth-Flame Algorithm for Human–Robot Collaborative Parallel Disassembly Line Balancing Problem

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.

Novel mathematical modelling approaches and a new lower bounding scheme for multi-manned assembly line balancing problems with walking workers

This article suggests two novel mathematical modelling approaches and a lower bound calculation method for the number of workstations to balance multi-manned assembly lines with walking workers. While new formulations include more variables, they require fewer constraints than the existing one. The performances of all formulations are compared each other using benchmark test instances from the literature as well as the instances produced in this study. Comparison results showed that one of novel modelling approaches exhibits superior performance than the existing one in all aspects. It has been demonstrated that the proposed model can be applied to practical-sized instances of the problem. Also, the advantages of the proposed lower bounding scheme have been shown.

Solution approach using heuristic and artificial neural networks methods in assembly line balancing problems: A case study in the lighting industry

Assembly line efficiency is one of the most important parameters that determine the overall efficiency of a manufacturing company. The production of a product under optimum conditions is ensured by a balanced assembly. With a balanced assembly line, machinery, material and labour costs are reduced. Within the scope of this research, real data about the daily production capacity and assembly line efficiency of a company producing Emergency Luminaire were taken, the same assembly line was balanced with 4 different Heuristic ALB methods and the results were compared. According to the results obtained, a high line efficiency of 93.955% was achieved using the Hoffman, Comsoal and Moodie&Young (M&Y) methods, and 84.414% was achieved with the Ranked Positional Weight (RPW) method. As a result of this, it was observed that the daily production capacity increased from 250 units to 375 units. As a result of the study, it was revealed that the efficiency of the existing assembly line and accordingly the daily production capacity increased. In addition, the study results of this assembly line were taught to an artificial neural network model for training purposes, and the work station results of the operations of a different assembly line were obtained with 99.940 accuracy. In this context, it has been revealed that the artificial neural networks method can be used in addition to the use of the heuristic method in the solution of ALB problems.

A grey wolf optimization algorithm for solving partial destructive disassembly line balancing problem consider feasibility evaluation and noise pollution

Partial destructive disassembly (PDD) is a crucial tool for enhancing efficiency and reducing disassembly costs when recycling complex end-of-life (EOL) products. Evaluating the feasibility of PDD is crucial for determining its implementation. However, the existing partial destructive disassembly line balancing (PDDLB) problem prioritizes profitability and randomly selects components for destructive, disregarding the feasibility of PDD and the noise pollution resulting from destructive operations. To address this concern, this paper introduces the integration of feasibility evaluation and noise considerations into the PDDLB problem for the first time. Subsequently, a mathematical model is established for the PDDLB problem, aiming to minimize disassembly costs, achieve smoothness balance, minimize the number of stations, and mitigate the adverse effects of noise. Then, an improved grey wolf optimization algorithm (IGWOA) considering feasibility evaluation and noise limitations is developed. It non-linearizes the convergence factor to enhance accuracy in the search for optimality, introduces a Lévy flight operation to further expand the search solution space, and incorporates an interference factor to prevent local optimality. In addition, the superiority of the proposed algorithm in solving different cases is verified by comparing five algorithms for the 25-task example and seven algorithms for the 52-task example in the latest literature. Finally, the proposed method is applied to a real engine case. The obtained results are compared with several classical Swarm Intelligence methods. The comparison results show that IGWOA has advantages due to other group intelligence methods in solving small, medium, and large-scale example. Moreover, the disassembly cost is comparatively low.

Solving line balancing and AGV scheduling problems for intelligent decisions using a Genetic-Artificial bee colony algorithm

Due to the rapid advancement of technology, the demand for electronic devices in various sectors such as consumer electronics, automotive, telecommunications, healthcare, and industrial applications, as well as customized printed circuit board (PCB) products, has significantly increased. Balancing the PCB assembly line is crucial for improving productivity to meet market demand, which means balancing the workload distributed among various assembly stations on the assembly line. However, the workload balancing depends on material-handling systems in production environments that facilitate the transportation of raw materials to the buffer storage of the assembly stations. In the PCB assembly system, the material handling is carried out using automated guided vehicles (AGVs). This paper studies the assembly line balancing and AGV scheduling collectively because of their dependency on each other. A mixed integer linear programming (MILP) model is formulated to balance the workload distribution among stations and schedule the AGVs. An existing intelligent platform of the real-life PCB industry is considered to solve this integrated problem with intelligent decisions. The platform includes three layers modules: physical layer, data management layer and application service layer. A novel genetic artificial bee colony (GABC) algorithm is proposed and embedded with an application service layer to optimize the current problem and give optimum solutions for the real-life physical layer. The proposed GABC algorithm incorporates the operators of the genetic algorithm (GA) i.e., crossover and mutation into the search process of the ABC algorithm. Additionally, the greedy selection feature is employed in GABC which significantly enhances the exploitation capabilities, leading to faster convergence, higher-quality solutions, and improved robustness. The performance of the proposed GABC algorithm is tested based on different parameters by comparing the results with GA, ABC and PSO algorithms. Experimental analysis indicates that the proposed GABC algorithm outperforms the compared algorithms. Therefore, the yielded solutions in terms of prompt responses help managers to expedite the production through intelligent decision-making based on customer demands.

Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm

AbstractAssembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to $$\sim $$ ∼ 2.75$$\times $$ × , reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.

Data-driven analysis and human-centric assignment for manual assembly production lines

Flow production-oriented assembly production lines have become crucial in various industries with the development of Industry 4.0, leading to mass customization and personalized production. In addition, Industry 5.0 has emerged, which emphasizes human-centric design. Manual assembly production line balancing, considering worker ergonomics, has also gained substantial attention. This study aims to solve the line balancing problem through combination optimization considering ergonomic risks based on many studies on human-centric production lines. A framework of the data-driven analysis and assignment for manual assembly production lines is proposed herein. The proposed framework defines and analyzes the process difficulty and the worker’s process ability and assigns a skilled worker to a process through the presented heuristic algorithm. To demonstrate the applicability of the proposed framework, a case study was conducted on an experiment imitating a home appliance assembly line in the United States. This paper provides empirical evidence of a framework aimed at realizing human-centric business objectives through case study. Future studies on the data-driven analysis and assignment for manual assembly production lines are anticipated to exhibit a range of variations such as line balancing problems and evaluation methods.

An overview on the disassembly line balancing under uncertainty

In response to the growing emphasis on recycling and reusing end-of-life products, the effective management of disassembly processes under uncertainty has become a significant area of interest for researchers and practitioners alike. This paper offers an initial comprehensive review of the literature concerning the disassembly line balancing under uncertainty. The problem of stochastic line balancing is defined as the assignment of disassembly tasks to workstations in an uncertain environment while achieving specific objectives. Our review examines the diverse uncertainties discussed in the literature. We analyze the modeling techniques, approaches, and objectives associated with each type of uncertainty. Drawing from insights gained through the review of 54 publications, we subsequently engage in a discussion and outline potential directions for future research.

Production line balance problem identification and improvement based on decision tree: A case study of commercial air conditioner production line.

In the production of air conditioners, there are various issues such as complex requirements, redundant stations, excessive man-hours, and low production line balance rate. This paper aims to address these problems by analyzing the historical data of H Company's commercial air conditioner production line. The data is categorized into five aspects: station, working hours, standard working hours, labor capacity, and presence of bottleneck processes. To optimize and improve the second production line, this paper applies the production line balance management method based on data mining. It utilizes the decision tree model in data mining and incorporates lean production knowledge from industrial engineering. The goal is to identify crucial factors that affect the balance of the production line and address the issues caused by these factors. The aim is to reduce and eliminate redundant working hours and enhance the balance rate of the production line. By implementing the approach outlined in this paper, the bottleneck time of the second production line was reduced from 96.67 s to 74.6 s, and the production line balance rate increased from 68% to 85%.

Assembly Line Balancing and Sensitivity Analysis of a Single-Model Stochastic Sewing Line Using Arena Simulation Modelling

Assembly line balancing is always a critical responsibility for manufacturers as it controls the efficiency and productivity of the assembly line. There are many techniques to solve line balancing problems, some of which are revealed in the literature review section, but computer-aided simulation modelling is prevalent among them. This study aims to analyze an assembly line balancing problem using a discrete event simulation software (Arena) for the optimal solution and sensitivity analysis of the solution. The empirical study was carried out at Arunima Sportswear Limited garment factory, and a garment style (kid’s pants) with 21 operations was taken into account. The computer model was verified by line supervisors and validated by a statistical hypothesis test (t-test). Then, using the Arena OptQuest tool, an optimal solution to the model is achieved. The average throughput of 904 pieces per day was achieved in the proposed model, which was 163 pieces higher than the existing model’s output. The line efficiency of the current model (75.76%) was also increased in the proposed model, which was 92.43%. Finally, a sensitivity analysis is performed by varying the values of some key factors (i.e., entities per arrival, process failure time, and operators’ absenteeism) to determine the level of uncertainty of the model.

A New Pareto Discrete NSGAII Algorithm for Disassembly Line Balance Problem

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.