Genetic algorithm and decision support for assembly line balancing in the automotive industry

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

Enhance of OEE by hybrid analysis at the automotive semi-automatic assembly lines

This paper addresses a genetic algorithm approach for solving a single machine scheduling problem considering the learning effect on sequence-dependent setup times. The objective of the proposed model is to minimize the makespan. Scheduling problems are referred to as the NP-hard. Since genetic algorithm parameters influence the result of the solution, the selection of convenient design value for reaching global optimum includes a notable impact on the performance of the genetic algorithm. Therefore, a full experimental design is conducted to get the optimal parameter combination design in the genetic algorithm. After determining the suitable parameter combinations, a decision support system is created to achieve optimal or near-optimal job-sequence within the automotive industry.

Artificial chromosomes with genetic algorithm 2 (ACGA2) for single machine scheduling problems with sequence-dependent setup times

This study suggests a mathematical modelling approach to sub-assembly detection and assembly line balancing problems. The sub-assembly detection problem involves two issues as follows: 1 determine which components of products are assembled in sub-assembly area; 2 determine which components of the products are assembled directly in the line. In assembly line balancing problem tasks are assigned to the workstations. The main motivation of this study is to determine the best compromised assembly line structure by solving the sub-assembly detection and the line balancing problems jointly. A multi-objective mathematical model is developed to formulate the above mentioned problems in which two conflicting costs of sub-assembly generation and assembly line labour cost are involved. Since the applications in real life are imprecise, fuzzy goal programming is adapted to the developed model to determining the vague goal. Then the model is implemented in a real life automotive manufacturers assembly line balancing.

Machine vision systems are widely used in assembly lines for providing sensing abilities to robots to allow them to handle dynamic environments. This paper presents a comparison of 3D sensors for evaluating which one is best suited for usage in a machine vision system for robotic fastening operations within an automotive assembly line. The perception system is necessary for taking into account the position uncertainty that arises from the vehicles being transported in an aerial conveyor. Three sensors with different working principles were compared, namely laser triangulation (SICK TriSpector1030), structured light with sequential stripe patterns (Photoneo PhoXi S) and structured light with infrared speckle pattern (Asus Xtion Pro Live). The accuracy of the sensors was measured by computing the root mean square error (RMSE) of the point cloud registrations between their scans and two types of reference point clouds, namely, CAD files and 3D sensor scans. Overall, the RMSE was lower when using sensor scans, with the SICK TriSpector1030 achieving the best results (0.25 mm ± 0.03 mm), the Photoneo PhoXi S having the intermediate performance (0.49 mm ± 0.14 mm) and the Asus Xtion Pro Live obtaining the higher RMSE (1.01 mm ± 0.11 mm). Considering the use case requirements, the final machine vision system relied on the SICK TriSpector1030 sensor and was integrated with a collaborative robot, which was successfully deployed in an vehicle assembly line, achieving 94% success in 53,400 screwing operations.

The production efficiency is among the most important for the success of the companies. The more efficient production lines are in the companies, the more products with higher quantity and quality would come out of them. When Lean approach is applied even during the design and development phase of the assembly line, this will lead to less need for subsequent optimizations of this line when it is in serial production. And in this way time and money will be saved, i.e. cost will be minimized. The combination of Lean practices and Industry 4.0 technologies could be called Lean 4.0. The productivity would be even higher when using Lean 4.0. The main goal of this paper is to analyze how semi-automated automotive industry assembly line to be designed implementing Lean 4.0 approach. In order to achieve it, Lean analysis was performed for the elements of the most important KPI of the assembly lines – the overall equipment effectiveness. Based on it, Lean 4.0 technologies which would eliminate the negative impact over each element were selected. In the conclusion, few assembly line design recommendations for the automotive industry were given, such as incorporating smart sensors, adjustable actuators, internet connection, Internet of Things and Artificial Intelligence.

Dynamic takt time decisions for paced assembly lines balancing and sequencing considering highly mixed-model production: An improved artificial bee colony optimization approach

The pursuit of assembly line balance aims to enhance efficiency by optimizing the ratio between output and input. Achieving balance necessitates meticulous planning to ensure that machines at each workstation operate with equitable workloads. Notably, the assembly line plays a crucial role in this equilibrium. In a manufacturing company, excels in timely product delivery to customers. However, a decline in productivity is attributed to inefficient production processes. The production department operates based on takt time, aligning with customer demand requirements. Despite meeting customer demands promptly, the company usually needs to work on productivity due to fluctuating customer demands and varying process capacities. The consistent use of a capacity production leads to stable productivity and high loss of time due to efficient work hours. Driven by the background, the researcher aims to delve into workstation arrangements for efficient production processes, with the ultimate goal of minimizing wait times, analyzing work hour precision to reduce loss time, and optimizing time usage based on takt time in the production line for efficiency. The study incorporates line-balancing methods to enhance time usage effectiveness in each production line.

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

Purpose – This study seeks to present a simple assembly line design and its balance for a low‐volume manufacturing company.Design/methodology/approach – The study presents experiences with the design and implementation of a simple assembly line. The implementation concerns three aspects; design and construction of the assembly line, the assembly analysis of the product, and then balancing of the line. It also discusses construction and implementation difficulties of this tactical tool in the case company.Findings – The study presents some outcomes from the design, implementation, and balancing of an assembly line for SMEs.Research limitations/implications – The study is limited by the case company and its experience.Practical implications – This study is not pure theoretical study, it has application stages for industry, and it provides some real interface for the people from SMEs.Originality/value – The approach has an original value in respect of implementation of assembly line for a small manufacturing...

The objective of this research is to identify the benefits of ergonomic improvements in workstations and in planned parts supply in an automotive assembly line. Another aim is to verify to what extent it is possible to create competitive advantages in the manufacturing area with reduction in vehicle assembly time by using technological investments in ergonomics with benefits to the worker and to the company. The Methods Time Measurement (MTM) methodology is chosen to measure the process time differences. To ensure a reliable comparison, a company in Brazil that has two different types of assembly line installations in the same plant was observed, and both assembly lines were under the same influences in terms of human resources, wages, food, and educational level of the staff. In this article, the first assembly line is called “new” and was built 6 years ago, with high investments in ergonomic solutions, in the supply system, and in the process. The other is called “traditional” and was built 23 years ago with few investments in the area. © 2010 Wiley Periodicals, Inc.

Manual picking on assembly lines is an important part of the assembly operator's work. In the automotive industry, alternative material exposure strategies are continually evaluated in order to facilitate the assembler's work and minimise non-value-adding time, resulting in increased use of smaller bins or containers at workstations, instead of large pallets. The assembly operator's retrieval of components from bins, containers or pallets is known as manual picking. Previous work in this area has focused on picking from pallets placed on the floor; therefore, there is a need to include other forms of material exposure. The purpose of this paper is to explain what material exposure factors have an impact on manual picking times on assembly lines. A full factorial experiment, testing seven factors in 128 experiments, was conducted in an automotive assembly line setting. The factors with the greatest impact on manual picking time were packaging type; angle of exposure; height of exposure; and part size. This study contributes by addressing the importance of six factors influencing manual picking times, enhancing knowledge derived from MTM and elsewhere. The results from this study can be useful for designing manual picking operations, not just at assembly lines but also other situations where discrete manual picking occurs such as at workstations or bench assembly. Managerial contributions are evident in the examination of factors important in designing new production systems, including materials supply processes and assembly processes. A further managerial application is the use of the results in managing assembly lines.

Large scale manufacturing companies are growing along with ever-increasing demand from consumers, one of which is the heavy equipment industry. Excavators are the products of the heavy equipment division at XYZ Corp, where the Excavator assembly lines are single-model. Based on observations of existing conditions that occur at XYZ Corp, the production still cannot meet the demands of consumers. In the assembly process, the Excavator has a significant time difference from each work station, the distribution of work elements in the assembly process is uneven. In the Final assembly section (Zone C) there is a discrepancy between the takt time of Excavator products which is one reason for not achieving the demand. In resolving these problems, a line balancing process is needed, namely balancing workloads on each workstation. This study applies the Heuristic Priority Rules method as an initial solution and allocation constraint should be added as input from the Genetic Algorithms method to complete line balancing with a single-model system. Problems can be solved by increasing line efficiency from existing conditions by 60.4% to 91.74% and Smoothness index which decreases from 955.54 to 158.3977. The proposed result of balancing the final assembly of the excavator results in a better balance of assembly lines.

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