A Stochastic Approach for Product Costing in Manufacturing Processes

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.

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.

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.

Automated sheet metal handling in the automotive industry using robot manipulators is a standard in modern production. However, the desire of automotive companies to speed up the production process on the assembly line and at the same time to reduce expensive hardware components poses new challenges for robotics. Excessively rapid movement of flexible parts or sheet metal can either lead to its plastic deformation or increase the decay time of the vibrations to such an extent that it is necessary to wait before the part can be further processed, for example by welding. The traditional approach to solving these problems is to add fixing points for the part and/or to allow for waiting times at the end of the robot movement to ensure that the sheet metal vibration subsides. This means that more effort than necessary has to be put into the hardware setup or a poor cycle time has to be accepted. In our work, we propose to improve virtual commissioning systems by conducting a deeper analysis of the dynamics of sheet metal parts during its movement by the robot. To achieve this, a three-step optimization strategy is proposed. The first step is a structural transient analysis of the thin metal part under disturbances that arise during its movements. The second step is the creation of a substitute model, which is trained on the basis of the data obtained in the first phase and considerably reduces the computing time required compared to the time needed for a finite element method (FEM) simulation. Subsequently, this is used to optimize robotic handling efficiency by optimizing the robot trajectory. By addressing the challenges posed by sheet metal dynamics, enhanced process control, reduced cycle times, and ultimately, improved manufacturing outcomes are anticipated.

An assembly line balancing method based on genetic algorithm (GA) and visual simulation is proposed for the assembly line dynamic balancing in order to take into account more factors in addition to the implicit factors. Firstly, the assembly line virtual digital model is created based on systematic layout planning (SLP). Secondly, the GA for solving assembly line dynamic balancing is presented on the conditions that minimize the number of stations and the tasks assigned to a set of workstations with the same cycle time. In the view of assembly line visual modelling and simulation, the visual simulation based on eM-plant is adopted. This system framework and simulation process is discussed. The assembly line balancing system is developed based on the object-oriented language VC++6.0 with the access database. The gear-box assembly line case is studied by using this system. The results showed the method is valid and effective. It can be used to provide the technology support for improving the production efficiency and redesigning the assembly line for the manufacturing plant. (4 pages)

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 for a real-world test case. Automotive assembly lines are highly complex, and multiple factors have to be considered while balancing the lines. All factors relevant in 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.

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

The liquid composite molding family (LCM) includes several processes like RTM (“Resin transfer molding”) and VARI (“Vacuum assisted resin infusion”), to satisfy the requirements of each industry. The objectives of recent years in the automotive and aerospace industries tend towards better control of production costs by using of new materials, shorter manufacturing cycles, a higher level of performance and safety and better environmental respect. In the automotive sector, a short cycle time and a lower cost equipment are the most criteria to determine a suitable process, while the quality of the part is the primary parameter for aeronautical process selection. The main objective of this paper is to propose and discuss a new design of LCM mold, allowing at the same time to facilitate the manufacturing process, in particular to reduce the cycle time and to respect the material’s health by obtaining a part with minimum defects. This innovation is achieved by using the TRIZ theory (“Theory of inventive problem solving”), in order to eliminate the contradictions that exist between the requirements of the two sectors.

This paper deals with aggregate planning of Reconfigurable Assembly Lines (RAL). The assembly line considered in this paper consists of hexagonal cells. These have multiple slots where processing modules can be inserted to perform certain operations. In addition, each cell has a single central slot where a central module can be inserted for inter-cellular and intra-cellular transportation of parts. Multiple products with different assembly sequences must be handled over multiple planning periods. An Integer Quadratic Programming (IQP) model is proposed to solve the following problems simultaneously: (i) assigning processing modules and a central module to the cells; (ii) installation of the cells and conveyors between the cells; and (iii) routing products, ensuring that availability of the resources is not exceeded. The assembly line should be reconfigured over time to adapt to possible product functionality and demand changes at minimum reconfiguration, operational and material handling costs while ensuring the demand is met within each period. The IQP model is implemented and solved for an illustrative problem and its extensions using Gurobi.

As automotive assembly systems become more complex every year, use of computer aided technology in planning stage is common. Every change in assembly system or assembly line must be validated by software engineering department so the transition in changes are smooth and without unexpected problems. Assembly systems especially in automotive industry must be easy to adjust for new production. Companies in automotive industry, especially manufacturers of final cars have production lines for several versions of one car or even for different car models. This means that production systems are complex and must be balanced for work with different set ups. Time and work flow are essential things in production lines in automotive industry. That is the reason for software solutions like Tecnomatix. Implementation of new assembly parameters are calculated by software. Looking for bottlenecks or errors in assembly processes is easier in well-made virtual models of production lines and it also faster solution. In this article will be mentioned software solutions for this kind of processes. Basic work of software will be demonstrated in example made in Tecnomatix Plant Simulate and Tecnomatix Process Simulate from company SIEMENS. Examples made in these software packages will be aimed on automotive assembly respectively production lines. Also, simulation of production robotic workplaces will be mentioned in example with use of Tecnomatix Process Simulate. As welding robotic stations in automotive production systems are essentials of whole production. Robotic stations are high investment workplaces, so it is only understanding that simulation of such robotic workplaces is needed. Use of discrete simulation software for production simulation purposes is bigger every year and it will continue in future as well. Future of assembly planning will be discussed towards the end of article.

The paper describes difficulties in estimation of the quality of assembly line balancing problem (ALBP). The mentioned problem is NP-hard and even optimal solutions can contain idle times. The objective of optimization ALBP is to minimize the number of workstations on an assembly line for a given cycle time (TYPE 1) or to minimize the value of cycle time for a given number of workstations (TYPE 2). Since more than 50 years many exact and heuristic methods were introduced to solve the problem. As a result we get sequence of assigned tasks to the workstations of the assembly line. The additional problem is to choose appropriate solutions because of existing idle times. Some quality performances help to estimate the final results. In this paper efficiency line, smoothness index and time of the line are discussed. Author underlines the problem of differences between cycle time and maximum station load. Numerical examples are presented and conclusion are given.

The total cost of assembly is a critical factor in robotic assembly line balancing, as it encompasses all the costs associated with the assembly line, including initial costs, setup, maintenance, and energy cost. This study introduces a different approach to the robotic assembly line balancing problem, with a dual focus on minimizing both cycle time and overall assembly costs. The effectiveness of the proposed approach is validated through three case study problems taken from the literature and results are compared to traditional assembly allocation methods. For case study 1, 89.4% (42 out of 47) of the solutions achieved a lower total cost, and 34% (16 out of 47) of the solutions utilized fewer workstations; and for case study 2, 96.4% (108 out of 112) of the solutions achieved a lower total cost, and 58.9% (66 out of 112) of the solutions utilized fewer workstations for the same cycle time. These results demonstrate a significant savings in cost and a notable improvement in workstation efficiency for a substantial portion of the solutions. This comprehensive approach allows an effective resource allocation, reduces inefficiencies, and enhances the overall cost-effectiveness and performance of the robotic assembly line. It also supports decision-makers in selecting more sustainable and economically viable assembly line solutions that optimize both productivity and energy efficiency.

In real-world assembly lines, that the size of the product is large (e.g., automotive industry), usually there are multi-manned workstations where a group of workers simultaneously perform different operations on the same individual product. This paper presents a mixed integer programming model to solve the balancing problem of the multi-manned assembly lines optimally. This model minimizes the total number of workers on the line as the first objective and the number of opened multi-manned workstations as the second one. Since this problem is well known as NP (nondeterministic polynomial-time)-hard, a heuristic approach based on the ant colony optimization approach is developed to solve the medium- and large-size scales of this problem. In the proposed algorithm, each ant tries to allocate given tasks to multi-manned workstations in order to build a balancing solution for the assembly line balancing problems by considering the precedence relations, multi-manned assembly line configuration, task times, and cycle time constraints. Through computational experiments, the performance of the proposed ACO is compared with some existing heuristic on various problem instances. The experimental results validate the effectiveness and efficiency of the proposed algorithm.

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.

Towards Operator Empowerment in Assembly Lines with Human-Centered Design: A Concept with Application in the Automotive Industry