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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. [ABSTRACT FROM AUTHOR]

The solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALBHWP, tasks differ in terms of the qualification requirements of workers, and the qualification levels of workers are hierarchical. In the hierarchical workforce structure, a lower qualified worker can be replaced by higher qualified ones with higher costs, while the vice versa is not applicable. This problem has only been studied for straight assembly lines so far. In this paper, we introduce the ALBHWP for U-shaped assembly lines. We developed integer and constraint programming models for solving the ALBHWP and compared their effectiveness using an extensive set of benchmark instances. We solved the ALBHWP for straight and U-shaped assembly lines comparatively. Constraint programming models have been statistically proven to provide better quality solutions faster than integer programming models. Besides, the CP model outperforms the only available metaheuristic in the literature for the S-ALBHWP in almost all problem sizes. Another observation is that a U-shaped line design is more cost-effective than a straight line design, but solving the ALBHWP for U-shaped lines is more difficult regarding computational complexity. [ABSTRACT FROM AUTHOR]

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

For over five decades, researchers have presented various assembly line problems. Recently, assembly lines with multiple workers at each workstation have become very common in the literature. These lines are often found in the manufacturing of large vehicles, where workers at a workstation may perform their assigned tasks at the same time. Most research on multi-manned assembly lines focuses on balancing tasks and workers among workstations and scheduling tasks for workers. This study, however, concentrates on assigning tasks to workers already assigned to a specific workstation, rather than balancing the entire line. The problem was identified through an industrial case study at a large vehicle manufacturing company. The study presents two methods, one using mixed integer linear programming and the other using constraint programming, to minimise the number of workers required on a multi-manned assembly line with sequence-dependent setup times. The results of the computational experiments indicate that the constraint programming method performs better than the mixed integer linear programming method on several modified benchmark instances from the literature. The constraint programming model is also tested on the real-world scenario of our industrial case study and leads to significant improvements in the productivity of the workstations. [ABSTRACT FROM AUTHOR]

Issues 1 and 2 of 2024 are composed of papers selected for the special section in memory of Dr. Jean-Marie Proth. Some papers are extended versions of articles presented at the 10th IFAC MIM 2022 conference (hub.imt-atlantique.fr/mim2022/) in the invited sessions in memory of Dr. Proth. [ABSTRACT FROM AUTHOR]

Considering ergonomics at the design phase can lead to assembly lines that protect and improve workers' health and well-being. However, this may reduce efficiency and productivity. Evaluating the potential benefits of incorporating ergonomics into line design versus the additional costs associated with the number of extra stations required or increased idle time, as well as exploring other ergonomic design alternatives, is critical for decision-makers. An assembly line balancing problem considering time, space and ergonomics, is used to evaluate the impact of ergonomic considerations. A model for maximising line efficiency is proposed and linearised for solution by the solver CPLEX. The mixed-integer linear model is compared to an alternative linearisation approach in which a decision variable is parameterised and then iteratively solved. An example and a case study are used to observe the competitiveness of both solution methods, where the iterative approach is shown to be superior for real size problems. The results show that ergonomic evaluation in line design leads to safer but less productive lines, which in turn increases the number of workstations and costs. Specifically, limiting ergonomic risk to a moderate level for the engine assembly line examined in this study, means maximum daily drop of 27 engines. [ABSTRACT FROM AUTHOR]

Manufacturing systems are socio-technical systems, with explicit interactions between humans and technologies in shared workspaces. These shared workspaces could also be called hybrid collaborative manufacturing systems, which involve workers as well as technological equipment and combine the benefits of human workers and new Industry 4.0 technologies, such systems are particularly useful in a context requiring flexibility and adaptability. Furthermore, the new Industry 5.0 approach has the objective to shift toward more human-centric and resilient manufacturing systems. The key problems to solve in the design of collaborative manufacturing systems are the combinatorial assembly line balancing problem and the equipment selection problem. An efficient and sustainable line requires a cost-effective choice of equipment while improving the ergonomics and the safety of workers. Both decisions of balancing workload and the assignment of equipment impact the ergonomics of a collaborative system and present conflicting criteria. To this end, we propose a multi-objective approach, the objectives are the optimisation of the investment costs and the ergonomics with a fatigue and recovery criterion. We propose to linearise the fatigue and recovery to formulate a new Mixed Integer Linear Programming formulation. We developed an exact multi-objective solving algorithm based on the ϵ-constraint to obtain the trade-off between these objectives. We conducted numerical experiments with different instances from the literature with promising results for instances with up to 45 operations. Finally, we discuss insightful managerial conclusions and future research perspectives. [ABSTRACT FROM AUTHOR]

In manual assembly systems, workers' differences in terms of skills, level of expertise and perceived physical effort largely affect the assembly line balancing and system performance. Traditional long-term strategic decisions may not respond to workforce changes and needs, resulting in frequent requests for line rebalancing. In this study, we propose a methodological framework and an easy-to-use Assembly Line Worker Assignment and Rebalancing Problem with different options: workers' assignment considering their performance variability, integration of worker dependent physical exertion constraints and possibility to use trainers to assist inexperienced workers. A bi-objective linear programming model is proposed aiming to minimise the cycle time and the number of reassigned tasks to respect the initial design while integrating new workers with different characteristics. The ϵ -constraint approach is used to build Pareto frontiers for this bi-objective problem. This approach is applied to three real cases. The obtained results show that the developed model can be successfully used in manufacturing companies to help the production managers to deal with workforce turnover and skills heterogeneity. [ABSTRACT FROM AUTHOR]

The objective of Assembly Line Balancing (ALB) is to find the proper assignment of tasks to workstations, taking into consideration various types of constraints and defined management goals. Early research in the field focused on solving the Simple Assembly Line Balancing problem, a basic simplified version of the general problem. As the production environment became more complex, several new ALB problem types appeared, and almost all ALB problems are NP-hard, meaning that finding a solution requires a lot of time, resources, and computational power. Methods with custom-made algorithms and generic approaches have been developed for solving these problems. While custom-made algorithms are generally more efficient, generic approaches can be more easily extended to cover other variations of the problem. Over the past few decades, automation has played an increasingly important role in various operations, although complete automation is often not possible. As a result, there is a growing need for partially automated assembly line balancing models. In these circumstances, the flexibility of a generic approach is essential. This paper compares two generic approaches: mixed integer linear programming (MILP) and constraint programming (CP), for two types of partially automated assembly line balancing problems. While CP is relatively slower in solving the simpler allocation problems, it is more efficient than MILP when an increased number of constraints is applied to the ALB and an allocation and scheduling problem needs to be solved. [ABSTRACT FROM AUTHOR]

In response to the increasing demand for individualized products in the 'small-lot, multi-variety' market, there is an urgent need to enhance the efficiency and intelligence level of assembly lines. To address this, a novel solution method for the mixed-model parallel two-sided assembly lines balancing problem (MMPTSALBP) is proposed. The first step is to define the type and layout of the parallel two-sided assembly lines. Next, a mathematical model is constructed with the objective of minimizing the number of workstations required for the MMPTSALBP, proposing an improved ant colony algorithm that utilizes double string representation for the initial solution encoding method and a double pheromone matrix update to solve the model. Its efficiency is tested on benchmark datasets according to some performance measures and classifications. A study validates its effectiveness on 25 classical arithmetic cases by comparison with the solutions of an heuristic algorithm and an artificial fish swarm algorithm that had been reported to perform well. [ABSTRACT FROM AUTHOR]

Incorporating ergonomic considerations into an assembly‐line balancing problem (ALBP) enhances productivity and minimizes ergonomic concerns. The assembly process, characterized by repetitive motions and handling numerous components, can lead to worker overload. Consequently, the inclusion of ergonomic aspects results in an appropriate distribution of assembly operations and relative workloads. This study investigates a multi‐objective ALBP aimed at minimizing the number of workstations, overall skill level required, and variance in workers' energy expenditure across workstations. To address the ALBP while considering the ergonomic aspects, this study proposes an approach based on the non‐dominated sorting genetic algorithm II (NSGA‐II) and multi‐objective simulated annealing (MOSA) using Pareto optimality. A comparative analysis of the NSGA‐II and MOSA is conducted in single‐ and multiproduct production scenarios, and a computational study involving various factors is performed to identify the dominant algorithm. The computational analysis indicates that the runtime performance of MOSA is 73.287% better than that of NSGA‐II; therefore, MOSA outperforms NSGA‐II. This study aims at applying scientific knowledge concerning manufacturing ergonomics to assist manufacturing industries in enhancing their productivity. [ABSTRACT FROM AUTHOR]

This paper considers the optimisation of Mixed-model assembly lines with continuous paced line control. The two minimisation goals have a mixed-integer linear multi-objective dispute. The paper proposes a criterion-space method to define the Pareto front for this class of problems. The method combines Pareto fronts obtained from integer solutions and gradually refines them until the instance's global front is determined. Comparing paced to unpaced line controls can be challenging, since they can produce the same cycle time given sufficiently long line lengths or buffers. Hence, determining Pareto fronts between cycle time and line length for paced lines allows meaningful comparisons between line controls. An industrial case study shows that line length acts as continuously distributable buffers for paced lines, leading to weaker diminishing returns. This result suggests that paced lines are more efficient than unpaced ones for lower cycle time ranges. [ABSTRACT FROM AUTHOR]

The Joint Assembly Line Balancing and Feeding Problem (JALBFP) assigns a line feeding mode to each component (Assembly Line Feeding Problem) and each task to a workplace of a station (Assembly Line Balancing Problem). Current literature offers numerous optimisation models that solve these problems sequentially. However, only few optimisation models, provide a joint solution. To solve the JALBFP for a multi-manned assembly line, we propose a Mixed Integer Linear Programming (MILP) model and a heuristic that relies on the Adaptive Large Neighborhood Search (ALNS) framework by considering multiple workplaces per station and three different feeding policies: line stocking, travelling kitting and sequencing. The objective function minimises the cost of the whole assembly system which considers supermarket, transportation, assembly operations, and investment costs. Although the JALBFP requires higher computation times, it leads to a higher total cost reduction compared to the sequential approach. Through a numerical study, we validate the heuristic approach and find that the average deviation to the MILP model is around 1%. We also compare the solution of the JALBFP with that of the sequential approach and find an average total cost reduction of 10.1% and a maximum total cost reduction of 43.8%. [ABSTRACT FROM AUTHOR]

In recent years, human workers in manual assembly lines are increasingly being supported by the deployment of complementary technology. Collaborative robots (or cobots) represent a low-threshold opportunity for partial automation and are increasingly being utilised by manufacturing corporations. As collaborative robots can be used to either conduct tasks in parallel to the human worker or collaborate with the worker on an identic task, industrial planners experience an increasingly complex environment of assembly line balancing. This contribution proposes three different decomposition approaches for Benders' decomposition algorithms exploring the multiple possible partitions of the formulation variables. We evaluate the performance of the algorithms by conducting extensive computational experiments using test instances from literature and compare the findings with results generated by a commercial solver and a metaheuristic solution procedure. The results demonstrate the Benders' decomposition algorithms' efficiency of finding exact solutions even for large instances, outperforming the benchmark procedures in computational effort and solution quality. [ABSTRACT FROM AUTHOR]

In the automotive industry, a great challenge of production scheduling is to sequence cars on assembly lines. Among a wide variety of scheduling approaches, academics and manufacturers pay close attention to two specific models: Mixed-Model Sequencing (MMS) and Car Sequencing (CS). Whereas MMS explicitly considers the assembly line balance, CS operates with sequencing rules to find the best car sequence fulfilling the assembly plant requirements, like minimising work overload for assembly workers. Meanwhile, automakers including Renault Group are increasingly willing to consider other requirements, like end-to-end supply chain matters, in production planning and scheduling. In this context, this study compares MMS- and CS-feasible solution spaces to determine which workload-oriented sequencing model would be the most appropriate to later integrate new optimisation. We introduce two exact methods based on Dynamic Programming to assess the gap between both models. Numerical experiments are carried out on real-life manufacturing features from a Renault Group assembly plant. They show that MMS generates more feasible sequences than CS regardless of the sequencing rule calculation method. Only the sequencing rules used by real-life production schedulers result in a higher number of distinct feasible sequences for CS, highlighting that the plant might selecta sequence with work overload situations. [ABSTRACT FROM AUTHOR]

An important and highly complex process in the automotive industry is the balancing of the assembly lines. Optimally distributing jobs among the lines in order to obtain the highest efficiency is mostly done manually, taking a lot of time. This paper aims to automate the process of line balancing fora real-world test case. Automotive assembly lines are highly complex, and multiple factors have to be considered while balancing the lines. All factors relevantin a case study at VDL Nedcar are considered, namely, mixed-model production, sequence-dependent setup times, variable workplaces with multiple operators and multiple assignment constraints. A Genetic Algorithm (GA) is proposed to solve the formulated balancing problem and to act as a decision support system. Results on newly proposed benchmark instances show that the solution is dependent on the relation between the takt time and processing time of jobs, as well as the setup times. In addition, results of a real-life case study show that the proposed GA is effective in balancing a real-world assembly line and that it can both increase the efficiency of the line and decrease the variance in operating time between all model variants when compared to current practice. [ABSTRACT FROM AUTHOR]

Assembly lines are production flow systems wherein activities are organised around a line consisting of various workstations through which the product flows. At each station, the product is assembled through a subset of operations. The assembly line balancing problem (ALBP) consists of allocating operations between stations to maximise the system efficiency. In this study, a variable-depth local search algorithm is proposed for solving simple assembly line balancing problems (SALBPs), which are the most widely studied versions of the ALBP. Although the state-of-the-art techniques for solving the SALBP consist of exact enumeration-based methods or heuristics, this paper proposes a local search-based heuristic using variable-length sequences that allow the solution space to be efficiently explored. The proposed algorithm improves the best solution known for multiple instances reported in the literature, indicating that its efficiency is comparable to those of the state-of-the-art method for solving the SALBP. Moreover, the characteristics of the instances for which the proposed procedure provides a better solution than previously reported construction procedures are investigated. [ABSTRACT FROM AUTHOR]

The Robotic Assembly Line Balancing Problem (RALBP) is a joint optimisation problem that is concerned with assigning both assembly operations and robots to workstations that are placed within a straight line. RALBP-2 is the particular problem where the cycle time, which is the maximum time spent on a workstation by the product being assembled, is minimised while the number of workstations is fixed. Sequence-dependent setup times are considered which raises the problem of sequencing the operations assigned to each workstation. Both the durations of the operations and the setup times depend on the robot. Two different variants are identified from literature. The first variant assumes that, given a set of types of robots, each type of robot can be assigned to multiple workstations without any limitation. Given a set of robots, the second variant forces each robot to be assigned to at most one workstation. Both assumptions are studied in this paper. The particular case of a given giant sequence of operations is solved thanks to a polynomial optimal algorithm. The latter algorithm, called split, is then embedded in a metaheuristic framework that explores the space of giant sequences. Benchmark data sets from literature are considered in the experimental section. A comparative study with other methods from literature shows the competitiveness of the suggested approach. [ABSTRACT FROM AUTHOR]

In the context of the demand for mass customization of products, a trade-off between highly efficient automated systems and flexible manual operators is sought. The linear arrangement of workstations made it possible to divide the process into many simple operations, which increases production efficiency, but also results in an increase in the number of workstations and a significant extension of the line. A human operator is usually treated as a quasi-mechanical object, and a human error is considered, similarly, as a failure of a technical component. However, human behavior is more complex and difficult to predict. A mathematical model of a new production organization is presented, including dividing the traditional production line into shorter sections or replacing the serial assembly line with a U-line with cells. Moreover, the reliability of operator and technical means are distinguished. Work-in-progress inventories are located between line sections to improve system stability. The stability of the assembly line is examined based on the system configuration and probabilistic estimates of human failure. The influence of the symmetry of reliability parameters of people on key performance indicators (KPI (headcount), KPI (surface) and KPI (Overall Equipment Effectiveness) is examined. KPI (solution robustness) and KPI (quality robustness) are also presented in order to evaluate the impact of a disruption on the assembly line performance. New rules for assigning tasks to stations are proposed, taking into account the risk of disruptions in the execution of tasks. For comparison of assembly problems, heuristic methods with newly developed criteria are used. The results show the impact of symmetry/asymmetry on assembly line performance and an asymmetric distribution of manual assembly times that is significantly skewed to the right due to human errors. On the assembly line, the effects of these errors are cumulative and lead to longer assembly times and lower KPIs. [ABSTRACT FROM AUTHOR]

An assembly line requires flexibility due to a shorter product life cycle. A way to increase flexibility is to utilize collaborative robots or cobots. Due to frequent product changes, redesigning an assembly line requires an efficient algorithm. This research aims to develop a genetic algorithm (GA) for solving a human-cobots assembly line design. The setup time of cobots is considered due to the flexibility of conducting multiple tasks by exchanging tools / end-effectors. The main contribution of the research is the efficient GA for solving assembly lines considering setup time. Secondly, the study proposed an upper limit parameter that enables faster computation without sacrificing the quality of the solution. The computational results showed that the algorithm could achieve an optimal solution with the number of tasks less than 35. Experiments of several data prove the proposed GA obtained solutions with an average gap of 3.83% to the optimal solution. Also, a faster computation time with an average difference of 64.66%. The proposed GA obtained a reasonable solution with fast computing time that helps improve efficiency and effectiveness in decision-making related to frequent redesigning of assembly lines. [ABSTRACT FROM AUTHOR]

In the era of Industry 5.0, there is a significant gap in addressing ergonomic risks and imprecise task times in manufacturing systems. This study aims to fill this gap by extending the ergonomic assembly line balancing problem with worker assignment. It employs a novel two-phase framework combining a constructive heuristic for feasibility with a unique ergonomic assessment method developed through a fuzzy expert system. Validated using 96 synthesized numerical instances, the proposed method addresses the scarcity of fuzzy and ergonomic-oriented data in benchmarks. Then, computational results are thoroughly analyzed to evaluate the method’s performance and identify potential areas for further research. The proposed optimization method provided high-quality solutions with a majority demonstrating low ergonomic risk and high worker safety, contributing to an overall improvement in ergonomic conditions. The integration of fuzzy expert system and advanced optimization techniques yields a robust framework for achieving a safer and more efficient manufacturing environment. [ABSTRACT FROM AUTHOR]

The standard time gives a measure of how long it takes a process or workpiece to pass through each operation. It is mainly established by work measurement methodology giving the time allowed to an operator to carry out a specified task under specified conditions and defined level of performance. This definition may be with respect to a task (a job element or an activity) or a product (consisting of many tasks). In the latter case, which is the main thrust of this work, as one job element passes through different resource (work) centres, another job element of subsequent ones will immediately follow and this will continue until the last job element is completed as a whole, this means value added at each work centre. This scenario will affect the level of inventory in the system and the level of inventory in the system depends on the type of production system and configuration (series, parallel or a combination of both) adopted. This too will affect the standard time for a single product (one-off), single product (mass produced), mixed or different products which may utilize assembly line philosophy and products in batches even with backtracking. Different examples have been used to highlight this scenario with different approaches developed which have gone a long way to improve on the solution techniques of the existing models. [ABSTRACT FROM AUTHOR]

This study proposes a dynamic simulation-based framework that utilizes swarm intelligence algorithms to optimize the design of hybrid assembly lines in the automotive industry. Two recent discrete versions of Whale Optimization Algorithm (named VNS-DWOA) and Gorilla Troops Optimizer (named DGTO) were developed to solve the assembly line balancing problem. The effectiveness of these algorithms was compared to six conventional meta-heuristics as well as the solution proposed by process design experts. The experimental results show that our methods outperform the conventional meta-heuristics and achieve comparable or better results than the experts' solution. Particulary, VNS-DWOA, being the top performer, has consistently provided averagely remarkable enhancements of cycle time, ranging from 7% when compared to the process expert's solution to 20% maximum improvement compared to all other methods. The findings of this study highlight the effectiveness of utilizing swarm intelligence algorithms and dynamic simulation-based frameworks as well as the potential benefits of implementing these digital methods in industrial settings, as they can significantly accelerate and enhance the optimization of assembly line design particularly and reduce time to market generally. [ABSTRACT FROM AUTHOR]

Paced mixed-model assembly lines are popular with various manufacturing enterprises. However, they face the weakness that they are at risk of line stoppages owing to the occurrence of workstation work overload situations. Moreover, the consideration of workers' health and ergonomic risk on manual assembly lines is a necessity required by legislation. Therefore, this article addresses mixed-model assembly line balancing and sequencing taking the problem of ergonomic risk into consideration and manages work overloads using a side-by-side policy with utility workers. To solve these problems, this article proposes an hierarchical and two-stage framework. The optimization goals are to minimize the number of workstations and utility workers. Furthermore, this article integrates an iterated greedy algorithm into a genetics algorithm to obtain global exploration and local exploitation ability. Finally, a divide-and-conquer strategy is proposed to meet the challenge of solving a large-scale problem. Experimental results show the effectiveness of the mechanism proposed in this article. [ABSTRACT FROM AUTHOR]

Assembly lines are production lines used to manufacture products, ranging from mass-production products to mass-customisation with low unit products. Assembly lines consume the largest parts of investment funds and involve the largest proportion of companies' labour force. However, workers in assembly lines are exposed to work-related musculoskeletal disorders (MSDs) and ergonomics problems. Poor distribution of workloads reduces the performance of assembly lines and causes workers MSDs and injuries, largely affecting the economics of production systems and resulting in high workers' compensation and absenteeism costs. Furthermore, ergonomics problems and MSDs impact product quality and decrease productivity. We propose a methodology for taking physical ergonomics into account as early as in the design phase of assembly lines. This methodology is based on Integer Linear Programming for the assembly line balancing problem with consideration of ergonomics with a quantitative fatigue and recovery criterion. As solving approach, we develop a dedicated exact algorithm, denoted Iterative Dichotomic Search, to solve low and medium-size instances of the problem. We validate our approach by proposing numerical experiments and analysis on instances from the literature. [ABSTRACT FROM AUTHOR]

The manufacturing industry has been pursuing an efficient and economical assembly system. By considering assembly line balancing (ALB) and part feeding (PF) as an integrated problem and programming them simultaneously opens additional opportunities to improve the performance of the entire assembly system. However, the integrated ALB and PF problem is a non-deterministic polynomial (NP) hard problem. This implies that exact solutions cannot be obtained in a reasonable computation time and its near-optimal solutions can only be realised by meta-heuristics. In this study, we propose a novel bi-level multi-objective genetic algorithm (NBMGA) to solve the integrated ALB and PF problem. First, a bi-level mathematical model is established to simultaneously minimise the number of stations and workload smoothness of ALB in the upper level as well as the number of supermarkets of PF in the lower level. Second, the NBMGA with two modified strategies, including extending fitness evaluation and adaptive termination condition, is designed for problem solving. Finally, a series of computational experiments are conducted to demonstrate the efficacy of the proposed algorithm. The computational results indicate that the proposed algorithm outperforms the bi-level nondominated sorting genetic algorithm (NSGA) II in terms of the approximation to the true frontier without sacrificing computational efficiency. [ABSTRACT FROM AUTHOR]

Numerous research studies have focused on the Simple Assembly Line Balancing Problem (SALBP). Although many of these studies have attempted to minimise the number of workstations for a given cycle time (SALBP-1) or min-imise the cycle time for a given number of workstations (SALBP-2), few have focused on the E-type of the SALBP, which is a general NP-hard problem. Therefore, a Systematic Literature Review (SLR) in this area is extremely important. The SALB-E problem involves scheduling a given set of tasks in an orderly sequence of workstations, where task precedence relations are satisfied, and finding the optimal pair of number of workstations and cycle time that maximises line efficiency. The aim of this systematic review was to examine existing research on SALB-E issues and predict future directions for studies on this topic. A systematic methodology was used to review papers published between 1995 and 2023, selected from the Scopus, ScienceDirect, and Google Scholar data-bases. The review showed that future studies should focus on multi- and mixed-model U-shaped and two-sided lines in the SALBP-E. In addition, it may be important to evaluate multiple objective functions rather than just a single one. To solve this problem, it is preferable to develop and refine genetic algorithms and investigate new approaches within the metaheuristic framework.

Assembly line worker assignment and balancing problem (ALWABP) is an important research topic originated from sheltered work centres for disabled, in which workforce is assumed to be heterogeneous due to their disabilities. Since the employment of disabled workers may sustain higher absenteeism rates due to their health, especially under COVID-19, employing temporary workers to fill labour shortage is a crucial issue. In addition, in practice, the movement of workers between stations on assemble lines can increase the flexibility of worker assignment. In this study, we investigate a new risk-averse ALWABP with uncertain disabled worker availability, limited temporary workers and moving workers. The objective is to minimise the risk-averse weighted sum of the cycle time and the number of employed temporary workers. For the problem, a risk-averse two-stage stochastic programming model is formulated. The first stage assigns specific tasks (called fixed tasks) to stations, while the second stage assigns workers and remaining tasks (called flexible tasks) to stations. A genetic algorithm combining K-means clustering approach and variable neighbourhood search (GAKV) is designed. Experiment results show the superiority of the GAKV in terms of solution quality and computation time compared with sample average approximation (SAA). In addition, managerial insights are drawn. [ABSTRACT FROM AUTHOR]

In an assembly system, balancing the line and designing the part feeding setting play a fundamental role in guaranteeing efficiency and effectiveness. Although these two aspects are interrelated, they are usually performed subsequent to each other, with the part feeding decisions only being made once the line has already been balanced. However, the simultaneous solving of the assembly line balancing and part feeding problems can lead to interesting benefits. In this paper, an innovative approach for solving the Joint Assembly Line Balancing and Feeding Problem (JALBFP) is proposed, to achieve an integrated balancing of assembly tasks and parts picking/kitting and a fully synchronised assembly-feeding system. The aim is to minimise the operational system cost, including workforce, space and transportation costs. The Mixed Integer Linear Programming (MILP) model is applied to several instances and two case studies and compared to a hierarchical approach. The proposed model leads to a significant improvement in reducing the number of assembly stations required and the inventory level at the line, where space is notoriously scarce, also avoiding workforce oversizing in the feeding system. The strength of the model relies on its formulation and on its versatility, which makes it applicable to different assembly sectors. [ABSTRACT FROM AUTHOR]

In order to adapt to the changing and personalized market demand, the traditional single-type mass production manufacturing mode is gradually changing to multi-species small batch personalized custom production, flexible manufacturing in machinery manufacturing occupies an increasingly important position. However, complexity, uncertainty, multi-objective and multi-constraints are the problems faced by production scheduling in flexible manufacturing workshop, which restricts the intelligent transformation of enterprises. This paper takes an assembly shop as the research object, and carries out research on assembly line balancing and multi-automatic guided vehicle (AGV) scheduling problems in shop production. Firstly, for the multi-objective assembly line balancing problem with fluctuating demand, the relevant assembly line model is established by changing only the number of workstations and replanning the boundary, and an improved multi-objective whale optimization algorithm is proposed to reduce the rebalancing cost of the assembly line. Secondly, the multi-AGV dynamic scheduling problem with corresponding scheduling objective weights according to the current number of AGVs is analyzed, and a mathematical model is established to maximize the value of the objective function by considering a variety of factors under the premise of meeting the production requirements of the assembly line. Then, a scheduling rule selection method based on neural network and knowledge base is proposed to determine the optimal combination of scheduling rules for different system states. Finally, a simulation analysis of an assembly workshop example is carried out to verify the feasibility and effectiveness of the proposed method in solving the assembly line balancing problem and the multi-AGV scheduling problem, which improves the manufacturing efficiency and the resilience of the workshop. [ABSTRACT FROM AUTHOR]

Assembly lines are widely used mass production techniques applied in various industries from electronics to automotive and aerospace. A branch, bound, and remember (BBR) algorithm is presented in this research to tackle the chance-constrained stochastic assembly line balancing problem (ALBP). In this problem variation, the processing times are stochastic, while the cycle time must be respected for a given probability. The proposed BBR method stores all the searched partial solutions in memory and utilizes the cyclic best-first search strategy to quickly achieve high-quality complete solutions. Meanwhile, this study also develops several new lower bounds and dominance rules by taking the stochastic task times into account. To evaluate the performance of the developed method, a large set of 1614 instances is generated and solved. The performance of the BBR algorithm is compared with two mixed-integer programming models and twenty re-implemented heuristics and metaheuristics, including the well-known genetic algorithm, ant colony optimization algorithm and simulated annealing algorithm. The comparative study demonstrates that the mathematical models cannot achieve high-quality solutions when solving large-size instances, for which the BBR algorithm shows clear superiority over the mathematical models. The developed BBR outperforms all the compared heuristic and metaheuristic methods and is the new state-of-the-art methodology for the stochastic ALBP. [ABSTRACT FROM AUTHOR]

Smoothing the workloads among the stations of an already installed assembly line is one of the major objectives in assembly line (re-)balancing. In order to find a feasible task-station assignment that distributes the total workload as equal as possible, two exact task-oriented branch-and-bound algorithms have recently been proposed. In this paper, we systematically analyse their effectiveness in solving the workload smoothing problem on simple assembly lines. In our experiments, we also examine the performance of a state-of-the-art mathematical programming solver and a 'combined' exact branch-and-bound procedure that integrates components of the two algorithms from the literature. In terms of theory, we show the equivalence of two recently developed local lower bounding arguments and suggest a slight improvement of the bound. We also propose an enhanced feasibility test. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) has been frequently used in recent years to develop intelligent robot agents that collaborate with human workers. In human-robot collaboration (HRC), the adaptation ability of automated robots is critical for efficient collaboration. However, previous studies using RL for assembly lines with HRC did not properly consider the possibilities of dynamics changes in the environment when collaborating with other human workers. In this study, we propose a method for adaptive assembly line balancing to efficiently collaborate with diverse workers having different task proficiencies through Bayesian RL. We mathematically formulate a Hidden-parameter Markov Decision Processes (HiP-MDP) model to represent the task proficiency of unknown workers in sequential assembly tasks. The robotic arm learns generalized policy to collaborate with diverse workers through meta-learning, and in a practical implementation, uses the policy adaptively by estimating the task proficiency of workers through Bayesian inference. We demonstrate the superiority of the proposed method in both virtual and real-world environments. Numerical studies show that the proposed method can collaborate well with diverse new workers.

In recent years, the use of collaborative robots in assembly lines has promised productivity improvement. It provides more alternatives for the assembly line design, which are the alternative resources of the human, robot, or human-robot collaboration (HRC), and the alternative subsets of processes, termed alternative subgraphs, taking advantage of the variety of robotic tools or end-effectors. However, more alternatives make the assembly line balancing problem more complex. This situation is encountered frequently in modern electronics and automotive assembly lines. The contribution of this study is to provide a mathematical model and solution to the assembly line balancing problem that has both HRC and alternative subgraphs, which has not been discussed as an integrated problem in previous literature. To accomplish this optimization problem, a mixed-integer linear programming (MILP) model has been developed to assign tasks to stations and determine the type of resources required while minimizing the cycle time. Practical constraints such as the available number of robots and robotic end-effector types are also considered. Owing to the complexity of the problem, the exact method for MILP is extremely time-consuming for real-world applications. Therefore, a metaheuristic algorithm based on the ant colony optimization (ACO) approach has been developed to solve the problem more efficiently. The results show that the MILP model can obtain optimal solutions for small-sized problems, whereas the ACO algorithm has proven to be a practical solution for medium- to large-sized problems, providing good solutions within an acceptable computation time. The results also show that the presence of alternative subgraphs can give opportunities for better solutions.

Assembly and disassembly are important activities in the manufacturing/remanufacturing process. Although the line balancing problems of them have been extensively discussed in the existing literature, they are rarely integrated into one system. In this paper, a hybrid production line balancing problem is adopted while considering the similarity between the assembly and disassembly tasks. First, to better reflect the uncertainty existing in the actual production environment, a mathematical model of the multi-objective stochastic hybrid production line balancing problem is presented, in which task disassembly times are assumed to be random variables with known normal probability distributions. Then, a hybrid VNS-NSGA II algorithm combining variable neighbourhood search (VNS) and non-dominated sorting genetic algorithm II (NSGA II) is proposed to solve the problem. VNS is embedded into NSGA II as a local search to improve the quality of the solutions found by the NSGA II at each generation. Finally, the effectiveness of the proposed method is verified by a case study, and the superiority of hybrid production line is reflected by comparing the solutions of the independent production line with the hybrid production line. Computational comparisons demonstrate the potential benefits of the hybrid production line and the proposed method. [ABSTRACT FROM AUTHOR]

In this work, we consider a mixed-model two-sided assembly line balancing problem for a given cycle time (MTALBP-I), in which the primary objective is to minimise the number of mated-stations (i.e. the length of the two-sided assembly line), while the number of stations, which describes the total number of operators, is also concerned. To solve this problem, we propose a combinatorial Benders decomposition-based exact algorithm, and develop a sequence-based enumerative search method to calculate effective combinatorial Benders cuts. To evaluate the performance of our proposed solution approach, we conduct extensive computational experiments on a set of benchmark instances, and the results demonstrate its efficiency of finding exact solutions even for large-sized instances. [ABSTRACT FROM AUTHOR]

This paper proposes new algorithms for the assembly line balancing problem with hierarchical worker assignment (ALBHW). The ALBHW appears in real industrial contexts, where companies deal with a multi-skilled workforce. It considers task execution times that vary depending on the worker type to whom the task is assigned. Qualification levels among workers are ranked hierarchically, where a lower qualified worker costs less but requires larger execution times then a higher qualified one. The aim is to assign workers and tasks to the stations of an assembly line, in such a way that cycle time and precedence constraints are satisfied, and the total cost is minimised. In this paper, we first present a mathematical model and improve it with preprocessing techniques. Then, we propose a constructive heuristic and a variable neighbourhood descent that are useful to solve large instances. Extensive computational experiments on benchmark instances prove the effectiveness of the algorithms. [ABSTRACT FROM AUTHOR]

Collaborative robots (cobots) are important components of the Industry 4.0 paradigm and smart manufacturing. Cobots are known for their ability to interact with the operators in a shared workspace. Due to their spread in the last decade, cobot research proliferated. However, most individual studies focused on specific aspects of cobot deployment, and only scant attention was given to their evaluation (mostly not based on productivity criteria). Thus, better support is needed for cobot acquisition and deployment decisions. This paper answers this need by presenting a summary of the major considerations related to cobots acquisition and deployment, and providing a productivity analysis procedure that supports cobot acquisition and deployment decisions. Defining the cobots' required characteristics and capabilities, effectively narrows the possible selection of cobots. However, it does not give information as to the economic value of acquiring and deploying a specific cobot. So, in addition to cobots' characteristics and capabilities, this paper presents computational techniques to analyse and support this decision for a single workstation per se, and for a station in an assembly line. The difference between these two cases is discussed and analysed, and corresponding models are presented with computational examples. [ABSTRACT FROM AUTHOR]

Assembly line balancing usually presupposes binary task-station assignments. Some authors have previously described efficiency increases due to fractional task allocations or work-sharing. However, the internal storage requirements for such efficiency increases have not been analytically described. This paper defines the Fractional Allocation Assembly Line Balancing Problem and presents mixed-integer linear programming models to bridge that gap. The main opportunity afforded by the studied flexibility is increased throughput, which is associated to higher internal storage costs. Worst-case analyses define mathematical expressions for these costs both for paced (line length) and unpaced lines (buffers). A screening on a 1050-instance dataset is conducted. Results suggest that fractional allocations can often allow better resource utilisation with relatively low costs: the higher space requirement costs are often one-time investments, while lower cycle time represents fundamentally continuous gains. Lastly, the proposed formulation was adapted and applied to industrial data. This mixed-model assembly line case study suggests that fractional allocations can also lead to more robust balancing regarding demand uncertainty. [ABSTRACT FROM AUTHOR]

Natural disasters, pandemics, and political nationalism force companies toward more responsive, flexible, and resilient assembly systems. For manufacturers, adaptability of the assembly process and local production ensure short product lead times even during supply chain disruptions. Yet one downside of regional production is that fixed takt time assembly lines become overburdened, especially when customisation is unlimited. In this context, variable takt time groups (VTGs) are a major competitive lever. We introduce the notion of a workload equilibrium balancing overload and underutilization. This preliminary stage of the assembly line balancing and sequencing problem significantly reduces the planning effort. Moreover, we present a model for minimising (i) the number of VTGs for a given maximum operator drift per unit or (ii) the maximum operator drift per unit for a given number of VTGs. We solve these dynamic problems by developing a heuristic approach: the variable takt time groups algorithm (VTGA). In our analysis of three real-world data sets from two German manufacturers—Fendt and Rolls-Royce Power Systems—we benchmark the VTGA against existing takt times. We find that VTGs result in higher labour efficiency than a fixed takt time and that the VTGs segmentation level plays an important role in reducing operator inefficiencies. [ABSTRACT FROM AUTHOR]

Human–robot collaboration can enhance productivity of production lines and reduce human ergonomic risk. The numbers and types of robots and stations in which robots are allocated need to be determined. Operations should be scheduled carefully when a human and robot work on a part in a station to obtain a feasible operation allocation with the highest efficiency and lowest ergonomic risk. A mixed-integer linear programming model, constraint programming model, and Benders decomposition algorithm were developed to analyse advantages of collaborative robots in assembly lines. An energy expenditure method was used to evaluate ergonomic risk. By scheduling and balancing collaborative human–robot assembly lines, operational advantages and scheduling constraints from human–robot collaboration were studied when immobile and mobile robots are used. Regression lines were developed that can help managers determine how many and what types of robots are best for a line and what the impact of robot mobility on robot and line performance can be. The best configuration for equipping a line with collaborative robots is when (number of robots)/(number of stations) is near.7 and about 37% of robots are mobile. Robots can be efficiently used in lines with both a small and large number of passive resources and in simple and mixed-model lines. [ABSTRACT FROM AUTHOR]