Assembly Line Balancing Research Articles

Integrating collaboration scenarios and workforce individualization in collaborative assembly line balancing

Integrating collaboration scenarios and workforce individualization in collaborative assembly line balancing

Process time distribution simulation in robotic assembly line balancing

Feedback control systems utilised in car body construction cause process time variance when compensating for external disturbances. By considering these in robotic assembly line balancing, the risk of cycle time violations can be controlled. This requires knowledge of the underlying process time distributions, which are not known in advance. Therefore, a simulation method is proposed to assess the impact of varying process time distributions on the balancing of robotic assembly lines. The initial step involves acquiring the process times of existing production processes. In the subsequent simulation, these are randomly and repeatedly selected as substitutes for the process times in the balancing of a new robotic assembly line. The impact of process time distribution variations on the result is investigated, and a single solution can be selected. The proposed method is evaluated based on the balancing of a robotic assembly line for a body-in-white rear compartment. Results are compared to normally distributed process times, which is a common assumption for modelling uncertain process times. Both approaches are evaluated utilising actual process time distributions. It is demonstrated that the proposed method yields fewer and less severe underestimations of cycle times, thereby reducing the number of uncontrolled violations of cycle times.

An investigation of mixed-model assembly line balancing problem with uncertain assembly time in remanufacturing

In recent decades, remanufacturing has emerged as an effective way to address resource crises and environmental pollution issues. Unlike traditional manufacturing, remanufacturing production is filled with various variable factors, especially in the assembly phase. Due to changes in part types, quality conditions, and assembly methods, the assembly time becomes highly uncertain. Assembly line balancing is a key challenge to achieve the stable operation of remanufacturing system. This study proposes an evaluation method for remanufacturing assembly time and establishes a multi-objective mathematical model for balancing remanufacturing mixed-model assembly (RMMA) line. The evaluation method utilizes the Fuzzy Graphical Evaluation Review Technique (FGERT) network to predict expected assembly time for each operation. The balancing model aims to optimize remanufacturing takt time and comprehensive balance rate (CBR). To effectively solve this model, an adaptive double-layer genetic algorithm (ADGA) is designed, where layer I ensures production efficiency and layer II optimizes assembly line balance. Finally, an assemble example of high-pressure common rail fuel pumps (HCRFP) is used to validate the effectiveness of the proposed method. The results demonstrate notable improvements compared to traditional single-product assembly (TSPA) line in scenarios with workstation numbers 4, 5, 6, and 7. Specifically, the production takt time is reduced by 4.19% to 9.56%, and CBR is enhanced by approximately 50%. Further comparison with three other classic algorithms confirms the superiority of ADGA. Additionally, it is observed that remanufacturability (proportion of remanufactured parts) has a significant impact on assembly performance. As remanufacturability increases, both takt time and CBR increase, reaching their maximum values when remanufacturability is around 0.5.

Augmented multi-agent algorithm utilizing intelligent search range detection heuristic to solve assembly line sequencing problem: A case study in the truck industry

Car sequencing is a widely adopted approach to implicitly enhance the short-term balancing of mixed-model assembly lines in practical applications. In this research, we investigate this problem in the context of a real-world case taken from a truck final assembly line. Our study considers cross-ratio constraints that address limitations related to dependent features of the products. Additionally, to manage the distribution of violation errors in cases where predefined congestion level thresholds (soft rules) exist, we propose a piecewise linear penalty function. To tackle these considerations, we formulate a mathematical programming model aimed at minimizing the total weighted penalty incurred from violating both independent and dependent sequencing rules. As the problem falls into the NP-hard category, we develop a multi-start parallel local search algorithm based on multi-agent approach to efficiently solve it. The algorithm is augmented by incorporating a self-adaptive diversification mechanism and a novel intelligent search range detection heuristic. The proposed heuristic enables the searching agents to intelligently explore the solution space rather than relying on a purely random search. The numerical experiments are conducted using 30 real-world instances from three truck assembly lines to validate the quality of the proposed algorithm. The comparison of the results indicates that our algorithm outperforms state-of-the-art algorithms. Furthermore, computational experiments demonstrate the effectiveness of each proposed mechanism in enhancing the algorithm's performance.

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.

Human-Robot Collaboration in Mixed-Flow Assembly Line Balancing under Uncertainty: An Efficient Discrete Bees Algorithm

In the evolving landscape of manufacturing and remanufacturing, assembly lines play a crucial role. Within the context of Industry 5.0, human workers are seen as a valuable and irreplaceable resource. Human-robot collaboration is a promising production model that combines the strengths of human workers and robots, thereby enhancing production efficiency while reducing occupational risks related to ergonomics. Despite these advancements, inherent uncertainties within assembly processes, the integration of human-robot partnerships, and the dynamic nature of market demands pose significant challenges to traditional assembly methods. To address these challenges, this research introduces a novel modelling approach through a mixed-flow assembly line balancing problem designed for uncertain environments, fostering collaboration between humans and robots. The primary goal is to facilitate efficient collaboration within a type-I assembly line balancing problem framework, where predefined assembly beats guide the workflow. In this research, the use of interval type-2 fuzzy sets capabilities was investigated to address uncertainties in the assembly process. Furthermore, the potential of pairing human operators of different abilities with robots of different models for collaborative tasks at workstations was explored, enhancing flexibility and adaptability in the assembly line. In response to the complexity of the problem, this research proposes an efficient multiobjective discrete bees algorithm that incorporates innovative operators and search strategies. Rigorously tested across diverse case studies, this algorithm consistently outperforms other comparator algorithms. This research not only offers novel perspectives on addressing assembly line balancing challenges but also provides valuable insights for the effective implementation of human-robot collaborative assembly in uncertain environments.

Green Supply Chain Management Practices and Productivity of Manufacturing Firms in Port Harcourt

This study examines the relationship between green supply chain management practices and productivity of manufacturing firms in Port Harcourt. Correlational survey design was adopted for this study. The population of this study is thirty-two (32) manufacturing companies in Rivers State which are registered with the Rivers State branch of Manufacturers Association of Nigeria (MAN). The study adopted the census population. 3 managers were selected from each firm multiplied by 32 firms gives us a total of ninety-two (92) for the study. Structured questionnaire instrument title the application of linear programming in assembly-line balancing of manufacturing firms in Port Harcourt questionnaire was developed on five-point likert scale. The result of the Cronbach's Alpha reliability test indicates .769 which is above .70 which implies that the items are reliable. PPMC (person product moment correlation) was used to test hypotheses on SPSS version 25. There is a significant relationship between green Design and productivity of manufacturing firms in Port Harcourt. There is a significant relationship between green Production and productivity of manufacturing firms in Port Harcourt. The study concluded that green supply chain management practices have a significant relationship with productivity of manufacturing firms in Port Harcourt. The study recommended Manufacturing firms in Port Harcourt should integrate sustainability considerations into their overall supply chain strategies to ensure that environmental concerns are addressed at every stage of the production process.

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.

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.

Assembly line balancing and optimal scheduling for flexible manufacturing workshop

Assembly line balancing and optimal scheduling for flexible manufacturing workshop

Balancing assembly lines with industrial and collaborative robots: Current trends and future research directions

Assembly-line balancing is a significant issue in production systems. Employing industrial robots as the main production resource was a milestone in developing assembly lines, and emerging Industry 4.0 led industries to build collaborative assembly lines by combining robots and human operator skills. Recently, the majority of research on assembly line balancing has contributed to addressing aspects of utilizing robots in assembly lines and how they can increase line performance. Various models and methods are developed, considering different objectives and performance indicators. Despite the increasing number of studies in this area, a thorough literature review is lacking in identifying gaps, shedding light on research directions, and facilitating future development. This study systematically reviews assembly-line balancing studies targeted at assembly lines with industrial and collaborative robots. Studies are classified based on their objectives and reviewed for their solution method, line layout, and other essential specifications. A descriptive analysis is provided to assist researchers and practitioners in linking different properties of assembly lines to the objectives and applied methodologies. The results show that most studies developed models and solution methods that focused on simultaneously optimizing more than one objective. The review reveals that minimizing the cycle time is the most popular objective, and meta-heuristic algorithms are the dominant solution approaches. It is also observed that balancing assembly lines with collaborative robots has received more attention in the last five years with the emergence of Industry 4.0. The review also highlights gaps in the related literature and provides promising insights for future research.

Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study

PurposeThe purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.Design/methodology/approachThe mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.FindingsThe developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.Originality/valueThis study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.

Model and heuristics for the multi-manned assembly line worker integration and balancing problem

This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products.

A Comprehensive Review: Analysing the Pros and Cons of Assembly Line Balancing Methods

Assembly line balancing plays a crucial role in optimizing production efficiency and resource utilization in manufacturing processes. Assembly line balancing helps to counter problems that occurred in the production industry such as uneven task distribution, bottlenecks, idle time, complexity, and time constraints. This review paper provides a comprehensive analysis of different methods used in assembly line balancing. The study includes exact methods, heuristics methods, meta-heuristics methods, simulation approach and queuing concept. The advantages and disadvantages of each method are examined, taking into account factors such as solution quality, computational complexity, flexibility, and applicability to different production environments. The review highlights the strengths and limitations of each method, allowing researchers and practitioners to gain insight into their suitability for specific scenarios depending on different criteria. Overall, this review serves as a valuable resource for understanding the different approaches and choosing appropriate methods to optimize assembly line balance in different manufacturing environments.

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.

Reinforcement Learning-Based Multiobjective Evolutionary Algorithm for Mixed-Model Multimanned Assembly Line Balancing Under Uncertain Demand.

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The Q -learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.

A Benders’ Decomposition Algorithm for Balancing and Sequencing of the Mixed-Model Multi-Manned Assembly Lines

A Benders’ Decomposition Algorithm for Balancing and Sequencing of the Mixed-Model Multi-Manned Assembly Lines

Balance of mixed flow assembly line based on industrial engineering mathematics and simulated annealing improved algorithm

The frequent changes in customer orders have led to the need for production line rebalancing and design. The increasing complexity of mixed assembly of multiple products makes it an urgent issue to perfect the balance of mixed flow assembly lines. This study proposes an improved simulated annealing algorithm by combining industrial engineering mathematical methods with intelligent optimization algorithms. The purpose is to achieve balanced optimization of mixed flow assembly lines by minimizing production rhythm, balancing load between workstations, and minimizing loss time within workstations. Firstly, industrial engineering methods are used to analyze the mixed flow assembly line, and then a mathematical optimization model is established by combining simulated annealing algorithm and genetic algorithm. Finally, through example simulation, the effectiveness of the algorithm is verified, and the superiority of its performance is verified through algorithm comparison. The experiment shows that the balance rate of the optimized assembly line for a total of 60 processes in 12 workstations has increased by about 6%. The smoothness index decreases by about 4. The loss coefficient has decreased by approximately 0.06. The overall efficiency has increased by about 12%. The smoothness index decreases by 0.3 in comparison with the solution results of Jackson's law in a case of 11 processes in 5 workstations. This fully demonstrates that the hybrid model has higher optimization efficiency, achieves balanced optimization of the mixed flow assembly line, improves production efficiency, and reduces equipment idle rate.

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.

Human-robot collaboration assembly line balancing considering cross-station tasks and the carbon emissions

Human-robot collaboration assembly line balancing considering cross-station tasks and the carbon emissions