Study of assembly line dynamic balancing based on simulation

PurposeCycle time fluctuations in assembly lines are one of the important reasons of re‐balancing. As a result of re‐balancing of assembly lines, it will be necessary to change task sequences or equipment locations. The purpose of this paper is to find the task sequence which enables assembly line balancing (ALB) with minimum number of stations (NS) for different cycle times such that tasks and equipment or fixture locations remain unchanged.Design/methodology/approachIn this paper a heuristic which consist of two stages is proposed to find a common task sequence for different cycle times in assembly lines.FindingsIt is shown that optimal NS for different cycle times can be achieved with a fixed task sequence.Research limitations/implicationsThe approach is limited to a single model case. Model variety together with cycle time variety can be investigated in further studies.Practical implicationsAssembly lines which require less time and cost for re‐balancing can be easily designed by the proposed approach.Originality/valueALB problem is handled with a new viewpoint. Also, it is observed that the proposed approach serves as a bridge between assembly line design and balancing. In this regard, it is thought to have an important place in the ALB literature.

Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.

Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.

Assembly line balancing is a problem where tasks assign to stations in compliance with precedence constraints and as far as possible to balance delays. Assembly lines which is not designed properly in an ergonomic way cause not only productivity loss but also work-related muscoloskeletal disorders on workers. To prevent such problems, it should be aimed to catch acceptable level for each workstation’s risk score and assign ergonomic risks equivalently among workers. In this study, for assembly line balancing problem, a new model approach is tackled, which constitute stations by consider ergonomic risk scores in addition to cycle time and precedence constraints. In order to compare proposed model with a classical assembly line balancing technique, a performance criterion which is combination of cycle time and ergonomic risk score were defined. The developed model was applied in a factory’s oven assembly line. In the assembly line, task times were measured, precedence diagram was constructed and ergonomic risk levels were evaulated by using REBA method. When balancing lines with using Longest Operation Times technique, performance score was %25.61 but when it comes to balance with developed model performance score was obtained %18.25. It has been detected that developed model provides 7.41% improvement in line’s total performance.

Enumerations and stability analysis of feasible and optimal line balances for simple assembly lines

The process of optimally assigning the timed tasks required to assemble a product to an ordered sequence of workstations is known as the Assembly Line Balancing (ALB) problem. Typical approaches to ALB assume a strict mathematical posture and mostly treat it as a combinatorial optimization problem with the objective of minimizing the idle time across the workstations, while satisfying precedence constraints. The actual nature of the tasks assigned is seldom taken into consideration. While this approach may yield satisfactory cycle time results on paper, it often leads to inconvenient task assignments in an actual work environment. It has been postulated in the literature that assigning groups of related tasks to the same workstation may lead to assembly lines that exhibit increased robustness in real-world situations at the expense of a slightly increased cycle time. The prototypical example of such an approach, Agrawal’s Largest Set Rule (LSR), utilizes backward work relatedness to assign a set of cohesive tasks to the proper workstation. In this paper, we enhance the performance of the original LSR algorithm through the concept of bi-directional work relatedness, where backward and forward relationships are taken into consideration for task assignments. The proposed concept leads to comparable cycle time and improved work relatedness. Applying this novel concept to a benchmark ALB problem demonstrates the feasibility and applicability of the proposed approach.

The most important factor affecting efficiency and ergonomic risk levels in an assembly line design is the problem of assigning certain tasks to certain stations, namely the assembly line balancing problem. In the literature, assembly line balancing problem has often been studied, but studies that consider ergonomic risks are deficient. Recently, it has been one of the issues that have started to attract great attention with the realization of health problems caused by assembly lines. To this end, in this study, a bi‐objective mathematical model is developed that considers balancing assembly line station time and ergonomic risk levels, simultaneously. It is aimed to minimize both station time and the total deviations of ergonomic risk scores for the stations. Weighted sum and conic scalarization methods are applied to solve the bi‐objective model. To analyze the outcomes of the developed model, an application is proposed and tested on a real industrial case, at a home appliance assembly line. The deployment of the OMAX method is a contribution to the literature since it shows an analysis tool which evaluates the results of assembly line balancing. This method evaluates the performance of the stations based on different criteria such as station time and ergonomic risk. The number of high‐risk stations is obtained as 13 in the single‐objective model aiming to minimize the station time, while it is found to be nine in the bi‐objective model solved with CSM, without an increase in the total number of stations.

SUMMARYIn this paper, an integrated mathematical model for the balancing and sequencing problems of a mixed-model assembly line (MMAL) is developed. The proposed model minimizes the total overload and idleness times. For the sake of reality, the impact of operator’s learning and fatigue issues on the optimization of the assembly line balancing and sequencing problems is considered. Furthermore, it is assumed that the Japanese mechanism is used in this assembly line to deal with the overload issue. With respect to the complexity level of the proposed model, a genetic algorithm is developed to solve the model. In order to set the parameters of the developed genetic algorithm, the well-known Taguchi method is used and the efficiency of this solution method is compared with the GAMS software using several test problems with different sizes. Finally, the sensitivity of the balancing and sequencing problems to the parameters such as station length, learning rate, and fatigue rate are analyzed and the impact of changing these parameters on the model is studied.

The planning of assembly lines has always been a complex task, as a large number of factors that influence and disrupt production must be taken into account. Uncertainties in the early planning stages necessitate adjustments to the initial assumptions for the detailed assembly line plan throughout the planning project. Notably, the experience of assembly planners enables the integration of changing requirements into the planning process. This experience-based methodology is reaching its limits due to the increasing complexity of product variants and product portfolios. Newer data-driven approaches in assembly planning enable high productivity and the targeted use of synergies in assembly lines by utilising descriptive systematics that facilitate digital processing, thereby creating the basis for utilising digital planning assistance systems. For example, assembly precedence matrices (APMs) can structure and digitally represent the dependencies between assembly operations.If product variants with partially different assembly contents—whether in terms of the quantity of assembly operations or the specific activities within assembly operations—are processed within an assembly line, the individual consideration of these variants is no longer sufficient to optimise the system. To enable holistic optimisation, the synchronisation of APMs ensures consistency across variants. This is achieved through an expansion step, which integrates all relevant assembly operations into every APM within the product portfolio, followed by a transformation through row and column rearrangements. The synchronisation thus creates a daily basis for describing the assembly precedence relationships (APRs) of different product variants within the same product family.The scientific findings gained in this paper result from combining synchronised APMs of a mixed-model assembly line (MMAL) into a joint APM. The result includes defining operators for combining unified APM grids for a given product portfolio with different cell entries, taking into account application-related aspects. By combining the APMs of the product portfolio under consideration, APRs for the assembly procedure analysis can be described holistically as part of the assembly planning process. This enables analyses and optimisations for planning, such as assembly line balancing (ALB), the design of hybrid assembly structures or the dimensioning of assembly system networks.

In response to the deficiencies of commonly used optimization methods for assembly lines, a production demand-oriented optimization method for assembly lines is proposed. Taking a certain compressor assembly line as an example, the production rhythm and the number of workstations are calculated based on production requirements and working systems. With assembly rhythm and smoothing index as optimization goals, an improved particle swarm optimization algorithm is employed for process allocation. Subsequently, Flexsim simulation is used to analyze the assembly line. The final results show that after optimization using the improved particle swarm algorithm, the assembly line balance rate increased from 71.1% to 85.9%, and the assembly line smoothing index decreased from 47.4 to 29.8, significantly enhancing assembly efficiency. This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.

PurposeMulti-manned assembly lines are designed to produce large-sized products, such as automobiles. In this paper, a multi-manned assembly line balancing problem (MALBP) is addressed in which a group of workers simultaneously performs different tasks on a workstation. The key idea in this work is to improve the workstation efficiency and worker efficiency of an automobile plant by minimizing the number of workstations, the number of workers, and the cycle time of the MALBP.Design/methodology/approachA mixed-integer programming formulation for the problem is proposed. The proposed model is solved with benchmark test problems mentioned in research papers. The automobile case study problem is solved in three steps. In the first step, the authors find the task time of all major tasks. The problem is solved in the second step with the objective of minimizing the cycle time for the sub-tasks and major tasks, respectively. In the third step, the output results obtained from the second step are used to minimize the number of workstations using Lingo 16 solver.FindingsThe experimental results of the automobile case study show that there is a large improvement in workstation efficiency and worker efficiency of the plant in terms of reduction in the number of workstations and workers; the number of workstations reduced by 24% with a cycle time of 240 s. The reduced number of workstations led to a reduction in the number of workers (32% reduction) working on that assembly line.Practical implicationsFor assembly line practitioners, the results of the study can be beneficial where the manufacturer is required to increased workstation efficiency and worker efficiency and reduce resource requirement and save space for assembling the products.Originality/valueThis paper is the first to apply a multi-manned assembly line balancing approach in real life problem by considering the case study of an automobile plant.

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

In this chapter, a system to support the design of assembly and production lines used in the make-to-order production phase is presented. The relevance of the system is supported by the fact that current market dynamics often leads to frequent modifications in the allocation of manufacturing resources, and, as a result, decisions related to manufacturing systems design that used to belong to the strategic level are now taken at the tactical level, and thus require new tools to support them.The decision support system addresses two different categories of problems: (i) assembly line balancing and (ii) production line balancing. Due to the high complexity of these problems, it uses heuristic methods based on evolutionary computation approaches to tackle them. The system aggregates several modules to address the different problems, a database to handle both input and output data and an interface that enables a user-friendly interaction with the decision maker.

Real-time administration of tool sharing and best matching to enhance assembly lines balanceability and flexibility

Manufacturing industries these days are more concerned about the energy consumption due to the increasing energy cost. It is observed that industries can achieve significant savings in the energy cost even when a small percentage of energy consumption is reduced. In a manufacturing industry, assembly lines are equipped with robots to perform the assembly tasks and there is a need for efficiently balancing the assembly line by reducing the energy consumption during the production phase. The authors could not find any research on optimizing energy consumption in U-shaped robotic assembly line systems to date. The objective of this paper is to propose an optimization models with an objective of minimizing the energy consumption in a U-shaped robotic assembly line when the number of workstations are fixed. Since the assembly line balancing problems falls under the category of NP-hard, an efficient bio inspired algorithm is implemented to solve the problem. The model proposed is very well applicable for automobile assembly line where robots are employed. The energy consumption is evaluated on the benchmark data and energy consumption obtained by the U-shaped robotic assembly line is compared with the energy consumption obtained with straight robotic assembly line. The cycle time of the layouts are also compared. The results obtained using this proposed model can be used for the decision making process in the industry. The paper also proposes an optimization model for minimizing the cycle time. The energy consumption of the assembly line when the cycle time is minimized is evaluated and presented.