Assembly Line Balancing Research Articles

Assembly line balancing and activity scheduling for customised products manufacturing

Nowadays, end customers require personalized products to match their specific needs. Thus, production systems must be extremely flexible. Companies typically exploit assembly lines to manufacture produces in great volumes. The development of assembly lines distinguished by mixed or multi models increases their flexibility concerning the number of product variants able to be manufactured. However, few scientific contributions deal with customizable products, i.e., produces which can be designed and ordered requiring or not a large set of available accessories.This manuscript proposes an original two-step procedure to deal with the multi-manned assembly lines for customized product manufacturing. The first step of the procedure groups the accessories together in clusters according to a specific similarity index. The accessories belonging to a cluster are typically requested together by customers and necessitate a significant mounting time. Thus, this procedure aims to split accessories belonging to the same cluster to different assembly operators avoiding their overloads.The second procedure step consists of an innovative optimization model which defines tasks and accessory assignment to operators. Furthermore, the developed model defines the activity time schedule in compliance with the task precedencies maximizing the operator workload balance. An industrial case study is adopted to test and validate the proposed procedure. The obtained results suggest superior balancing of such assembly lines, with an average worker utilization rate greater than 90%. Furthermore, in the worst case scenario in terms of customer accessories requirement, just 4 line operators out of 16 are distinguished by a maximum workload greater than the cycle time.

Possibilities to Increase Assembly Line Productivity Using Different Management Approaches

A number of scientific methods are used to support productivity growth in companies, aimed at reducing waste, balancing assembly lines and supporting the introduction of automation into assembly processes. Their use in industrial practice is widespread, especially in large and medium-sized enterprises, and small businesses that use scientific methods to a limited extent. The aim of the research is to show the assembly process of throttle valves implemented in a small company, the effects of balancing the current assembly line and the effects of proposals of variant solutions on the automated assembly line and on the amount of hourly assembly production and costs per product. Within costs, two cost types are monitored: namely the hourly labor costs, and hourly machine costs. The sum of the hourly assembly line costs is determined by their sum. In the results section, the main sources of waste in the assembly process are identified, and eight proposals leading to the elimination of waste are processed. In the discussion, the individual proposals are evaluated in terms of the cost of one product, the hourly production of assembly and the tact times of assembly lines. The proposal that is evaluated as the best process is compared with the current state. The purpose of this article is to point out the advantages of the implementation of scientific methods in industrial practice, the achieved savings in the solved problem, and thus, to support those scientific methods in the management of assembly processes which are beginning to be used in greater extents by small businesses.

Modelling and Optimization of Energy Efficient Assembly Line Balancing Using Modified Moth Flame Optimizer

Energy utilization is a global issue due to the reduction of fossil resources and also negative environmental effect. The assembly process in the manufacturing sector needs to move to a new dimension by taking into account energy utilization when designing the assembly line. Recently, researchers studied assembly line balancing (ALB) by considering energy utilization. However, the current works were limited to robotic assembly line problem. This work has proposed a model of energy efficient ALB (EE-ALB) and optimize the problem using a new modified moth flame optimizer (MMFO). The MMFO introduces the best flame concept to guide the global search direction. The proposed MMFO is tested by using 34 cases from benchmark problems. The numerical experiment results showed that the proposed MMFO, in general, is able to optimize the EE-ALB problem better compared to five comparison algorithms within reasonable computational time. Statistical test indicated that the MMFO has a significant performance in 75% of the cases. The proposed model can be a guideline for manufacturer to set up a green assembly line in future.

An improved multi-objective multifactorial evolutionary algorithm for assembly line balancing problem considering regular production and preventive maintenance scenarios

• ALBP considering PM scenarios promotes the productivity and smoothness. • Multi-task optimization benefits the simultaneous optimization of all scenarios. • The matrix reduction method is proposed to reduce the decoding time. • The neighborhood search operator is designed to expedite the convergence speed. • Statistical results indicate IMO-MFEA with two improvements outperforms others. Assembly line balancing considering regular production and preventive maintenance scenarios (ALBP_RP/PM) is a new and effective way to simultaneously handle the assembly production and equipment maintenance. It generates multiple allocation plans in advance. If a workstation is to be maintained, the corresponding allocation plan is applied to bypass this workstation and enable production continuity within the assembly line. However, the ALBP_RP/PM was generally solved as a single-task optimization problem, implying the coupling relationship of allocation plans under different scenarios was essentially ignored. For this reason, it is hereafter treated as a multi-task optimization problem under the premise that the assembly line balancing under each scenario is an independent task. On this basis, an improved multi-objective multifactorial evolutionary algorithm with two extra improvements is proposed to minimize cycle times and operation alterations. Specifically, the native evolutionary operators are reformulated to encourage the inter-scenario sharing of effective knowledge fragments among allocation plans to accelerate the simultaneous optimization of all the tasks; a matrix reduction method is proposed to reduce the decoding time in generating operation sequence; a neighborhood search operator is designed to strengthen the convergence performance of the algorithm. Comprehensive computational results demonstrate that the inter-scenario sharing mechanism in the multi-task optimization expedites the convergence speed and improves the operation allocation plans effectively. Besides, the proposed algorithm clearly outperforms the other five single-task optimization algorithms and four multi-task ones under the same computational limits.

Stochastic assembly line balancing: General bounds and reliability-based branch-and-bound algorithm

We analyze the assembly line balancing problem with stochastic task times. Tasks have to be assigned to a minimum number of stations with a constraint on the line reliability, which is the probability of finishing a work piece completely. A sampling approach is developed that ensures the line reliability. We prove that any lower bound on the number of stations for the related deterministic problem can be transformed into a lower bound for this sampling formulation. This general transformation can be applied to any bound that has already been developed or to any potential new bound. Those bounds can be applied to any MIP model, optimization algorithm or heuristic procedure based on a sampling formulation. We exemplify the usefulness of these bounds in a reliability-based branch-and-bound (RB&B) algorithm that explicitly considers the dependence among all stations due to the constrained line reliability. A partial assignment of tasks to stations has to consider already constructed stations and potential further assignments to other stations. Hence, a feasible assignment of tasks to this station may allow for exceeding the cycle time with a certain probability but has to consider the overall line reliability with respect to the remaining stations. Effective fathoming strategies based on the new transformed lower bounds or based on a direct consideration of the line reliability are proposed. A numerical study shows that the transformed lower bounds are tight and that they substantially reduce the required computation times of the RB&B algorithm and of the solver CPLEX.

Harmonizing ergonomics and economics of assembly lines using collaborative robots and exoskeletons

In the design phase of assembly lines, the harmonization of the frequently conflicting ergonomic and economic objectives is particularly important to enable both humane and efficient assembly. Recent technological developments such as collaborative robots and exoskeletons can support human workers in their assembly tasks and have the potential to enable both ergonomic enhancements and cost reductions. However, ergonomic factors in assembly line balancing are still underrepresented in literature. Exoskeletons have not yet been considered in assembly line balancing. We develop a mixed-integer programming model for balancing assembly lines under consideration of ergonomic and economic objectives and the availability of novel technologies. We validate our model using test instances from literature and find collaborative robots and exoskeletons to be ergonomically and economically viable options for partial automation of manual assembly. We present Pareto-optimal frontiers for the visual inspection of the optimization results and we quantify the potential reductions in costs and biomechanical load to support decision-makers in evaluating the technologies based on their preferences. Our approach is capable to identify nonsupported nondominated solutions, which allows for a stepwise reduction of biomechanical load with only a minor increase in costs. In this article, for the first time, the trade-offs between ergonomic and economic objectives in assembly line balancing are discussed and the potential to harmonize the conflicting objectives using collaborative robots and exoskeletons is evaluated.

A Demand-Driven Model for Reallocating Workers in Assembly Lines

This paper introduces the <i>demand-driven assembly line rebalancing problem</i> (DDALRP) and proposes a non-linear, multi-objective, combinatorial optimization model to solve it. A DDALRP arises whenever the production output of the assembly line (AL) must be continuously readjusted along a planning horizon in order to satisfy as much as possible a given demand forecast; thus, dealing not with a one-time rebalance, but with a multi-period rebalance, fact that exponentially increases the complexity and combinatorial nature of the problem. Adapting or regulating the production output of the AL to some demand forecast or production plan is a relatively new idea in the assembly line balancing (ALB) / rebalancing (ALR) literature; and we address this problem by reallocating workers to stations, taking into consideration their learning and forgetting (L&#x0026;F) curves. Our proposed model was solved by implementing a genetic algorithm (GA) in 162 cases (three problem instances under 54 scenarios each), obtaining useful insights about the dynamic of worker reallocation under different scenarios: optimistic, most-likely, pessimistic L&#x0026;F coefficients; experienced and inexperienced workers; and different demand scenarios.

Mixed-Flow Assembly Line Balancing with Uncertain Assembly times in Remanufacturing

Remanufacturing has in recent decades become an alternative way for sustainable development to cope with the increasingly resource crisis and environmental pollution problems. The line balancing of remanufacturing is more complex than that of traditional manufacturing due to the uncertainty in the remanufacturing process, especially as the assembly time is uncertain. As a result, the line balancing of the remanufacturing assembly has become a critical issue. This paper evaluates the uncertain assembly time in each station based on an approach of Fuzzy Graphical Evaluation and Review Technology (FGERT) network. A remanufacturing multi-objective mixed-flow assembly model is established by optimizing the cycle time, assembly line smoothing coefficient and balance time loss rate. Then an adaptive bilayer genetic algorithm is proposed to solve the optimization problem. Finally, an example was given to demonstrate the effectiveness of the proposed method. Results show that the production rhythm balance of remanufacturing assembly is significantly improved, and the idle time in each assembly station has been reduced as well. The cycle time of the remanufacturing has reduced from 344s to 336s, with the smoothing coefficient and balance time loss rate of being 8.73 and 1.69% compared to that without assembly line balancing. The proposed method provides a useful tool for improving mixed-flow remanufacturing assembly lines in an electromechanics industry.

Car sequencing problem with cross-ratio constraints: A multi-start parallel local search

Car sequencing is a popular approach to implicitly improve the short-term balancing of a mixed-model assembly line in real-world applications. In this paper, based on a real-world case from a truck final assembly line, we study this problem considering cross-ratio constraints which concern the limitations of some dependent features of products. A mathematical programming model is developed to minimize the total weighted penalty of disrespecting the independent and dependent limitations. Since the problem is NP-hard, a multi-start parallel local search is developed to efficiently solve the problem. A comparison of the results illustrates the effectiveness of using the proposed algorithm.

A digital-twin based worker's work allocation framework for a collaborative assembly system

Assigning the right task to the right worker remains a challenge to an assembly line manager. This work is proposed to use digital-twin technologies for Assembly line worker assignment and balancing problems (ALWABP) for a collaborative assembly system. Applications of the Internet of Things, Data analytics and Learning algorithms are proposed to estimate the worker's work skill and task performance. Assembly line Digital Twin and collaborative robot (CoBot) work simulation tools are proposed to estimate the CoBot's assembly task performing ability and execution time. ALWABP framework is proposed to optimize the work allocation between the worker and the CoBot. Assembly line digital twin is furthermore proposed to simulate the ALWABP output during the decision-making process of the assembly line manager by considering the Human-in-the-loop decision-making system.

Assessment and Enhancementofthe Manufacturing Productivity through Discrete Event Simulation

A balanced and resource-efficient assembly line plays a pivotal role in deciding the profit or loss of an industry. In automotive industries, the productivity of the specific materials is highly dependent on several explanatory variables and the processing operations. Moreover, in this 21st century, simulation is considered a powerful and cost-efficient tool for the designing and analysis of manufacturing operations. Therefore, in this research work, a case-study-based assessment of the assembly line in an automotive component vendor company has been proposed through the study of discrete events. Different processing operations are analyzed in terms of the productivity and effective utilization of available resources. The proposed model is set up by taking actual data using time study techniques from each workstation and process along the production line. All the processes, starting from the arrival of raw materials, forging, to the packing of final products are studied and their data is used to set up a model in software. A detailed statistical distribution of the data urged us to simulate the working area in the virtual environment under the different scenarios to define the bottlenecks. The simulation results have elaborated the realistic sense and meaningful contribution towards the performance enhancement of all the processes involved in the production line. Interestingly, Discrete event simulation has helped us to increase productivity as well as reduce the cost by eradicating the less used resources of the assembly line. Based on the results obtained, it is highly recommended to utilize the proposed methodology in the production system for acquiring better-quality and cost-effective products.

Considering physical workload and workforce diversity in a Collaborative Assembly Line Balancing (C-ALB) optimization model.

In comparison to the traditional usage of robots, Cobotization (Human-Robot collaboration) can be considered as an effective way to increase the productivity of assembly lines while ensuring job security and flexibility. However, successful implementation of human-robot collaboration scenarios requires adapted decision support tools. Workforce diversity can be mentioned as one of the factors that should be included to study its impact on both the performance of the production system and on ergonomics. Accordingly, in this research, a new bi-objective optimization model for the collaborative assembly line with Cobots is proposed to simultaneously minimize the cycle time and the physical workload of human operators. The workforce diversity of human operators is modeled through experience level and physical ability. To analyze the benefits of the developed model, a comparison between the different solutions from the Pareto front is conducted. The results show that the utilization of Cobots can reduce both cycle time and physical workload in the assembly line.

Multi-manned assembly line balancing using exact solution approach - a case study of a tricycle assembly line

Multi-manned assembly line balancing using exact solution approach - a case study of a tricycle assembly line

Mixed-integer programming for robotic assembly line balancing considering cross-station task and carbon footprint

Robotic assembly lines are widely applied to process workflow, increase capacity, and easily produce a wide range of products because of their flexibility and multifunctionality. Robotic assembly line balancing problem (RALBP) refers to allocating the tasks and robots to workstations in order to maximize the line efficiency, which has become a popular research direction recently. In this paper, we propose a mixed-integer programming model to optimize the carbon footprint of the RALBP. Minimizing carbon footprint can reduce global greenhouse effect and atmospheric dust pollution, which is a very significant environmental topic in the world. We also investigate a realistic production process design in our model—cross-station task. In this design, a task can be operated by the station to which it is assigned as well as the adjacent stations. The model is solved by a commercial solver (Gurobi) and some results are presented.

A Review on Collaborative Robot Assembly Line Balancing Problems

Assembly line balancing problems have been the subject of numerous studies for decades. The recent advantages of technologies in the Industry 4.0 era and integrating collaborative robots into the assembly systems have created task allocation, workload balance, and scheduling challenges. This paper provides a brief literature review on the existing collaborative assembly line balancing problems comparing their main characteristics and suggests some research directions for future studies.

A case study on the integration of assembly line balancing and feeding decisions

Mass-customization of products imposes new challenges on the planning of assembly lines due to an increased number of product models and a large variety of parts. Traditionally, both academics and practitioners approached the planning of an assembly line by balancing the assembly line, i.e., assigning tasks to stations, and decided on line feeding policies based on this outcome. However, this approach neglects the reciprocal effects between the two decision-making outcomes, i.e., the impact of line feeding decisions on task times and the impact of balancing decisions on feeding requirements and constraints. This interaction is of peculiar interest for assembly lines in a mass-customization environment due to the large number of assembly tasks and parts. Therefore, this study describes a framework that simultaneously optimizes both problems and minimizes overall costs. We show preliminary results using real-world data from a large automotive company.

A genetic algorithm for heterogenous human-robot collaboration assembly line balancing problems

Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations.

A queuing network and Markov chain approach for balancing assembly line: A case study

A queuing network and Markov chain approach for balancing assembly line: A case study

A queuing network and Markov chain approach for balancing assembly line: a case study

A queuing network and Markov chain approach for balancing assembly line: a case study

An improved preference-based variable neighborhood search algorithm with ar-dominance for assembly line balancing considering preventive maintenance scenarios

For assembly line balancing problem considering preventive maintenance scenarios (ALBP-PM), the production managers’ preferences should be concerned and hence the derived Pareto-optimal solution set (POS) should be advanced towards the corresponding region-of-interest. Otherwise, production managers might select a final solution far from their interests, and then obviously reducing the quality and efficiency of decision-making. Thus, a preference-based multi-objective optimization problem is formulated to simultaneously minimize the cycle times under different scenarios and total task adjustments among scenarios. An improved variable neighborhood search algorithm (IVNS) with novel ar-dominance and three modifications is proposed to obtain a preferred POS. Specifically, ar-dominance is integrated into it to guide the advancement direction of the preferred POS. Enhanced neighborhood structures based on critical stations are proposed to generate better-quality neighbor solutions whose cycle times are mathematically proven smaller. On this basis, a local search strategy is designed to randomly select a neighbor solution to guarantee quality and decrease the computational cost. A problem-specific adaptive restart mechanism is developed to escape from the local optimum. Computational results suggest that the IVNS obtains a better preferred POS than other eight state-of-the-art algorithms in terms of convergence, distribution and closeness to the preferences, and furthermore, incorporating preferences into the ALBP-PM helps production managers make final decisions in a more precise and faster way.