A Simulation Study on the Layout Design of Micro Assembly Line for Lens Module

A mixed-model assembly line is a type of production line where different models of a product are assembled on. Mixed-model assembly lines can respond to unanticipated changes in product demands quickly without keeping so many inventories. Designing mixed-model assembly line involves solving the traditional problems of the assembly line design (consists of balancing problem, determining cycle time, and the number and sequence of stations) in addition of determining the sequence of products in assembly line. The main goal of this paper is presenting a method in order to determine the sequence of products in mixed-model assembly line by considering Just-in-Time systems. Moreover, supplying some required components from feeding lines is considered. A mathematical model is presented which is capable of specifying the sequence of products in the mixed-model assembly line by considering main criteria and keeping feeding lines balanced. Mathematical model can be used for solving small-size problems. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of “real world” size, the search heuristics of simulated annealing and ant colony algorithms are presented and used to find solutions for several problem sets. Experimentations show that the simulated annealing approach outperforms the ant colony approach in objective function performance.

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.

Assembly lines with fractional tasks increase the throughput with a better workload balancing among the stations. The possibility to share tasks between two consecutive stations can also support the design of assembly lines with redundancies of the tasks. The redundancy of the tasks allows reacting to short failures or variations in processing time. This paper proposes a framework to support the design and control of redundancy assembly lines. The first problem concerns a modified mixed-integer linear programming model used for the fractional allocation assembly line problem to design redundancy assembly lines. Then, this research proposes control policies to manage the allocation of the tasks shared between two consecutive stations. Finally, the simulation evaluates the performance of the proposed methods to handle short failures and uncertain processing times. The numerical results show the potential improvements of the proposed methods with a limited number of redundancies. The main improvements concern the reduction of throughput time and work in process.

This article presents a novel approach for the automated design of assembly lines that combines the assembly line balancing problem with resource selection and the positioning of the chosen resources into one single optimisation problem. Existing approaches for the automated planning of assembly plants either focus on one planning step or work through different planning steps sequentially. So far, no method exists that sufficiently takes into account the interdependency between the selection and positioning of resources. This article addresses this problem by presenting a bi-level optimisation approach for the automated design of assembly lines. A nested genetic algorithm is used to solve an assembly line balancing problem that includes the selection of production resources while simultaneously considering the layouting options for the chosen resources. Three examples for the evaluation and validation of the algorithm are presented. The presented approach is economically promising as the design of assembly lines requires a lot of expert knowledge and is still mostly done manually.

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...

Both modular product design and reconfigurable manufacturing have a great potential to enhance responsiveness to market changes and to reduce production cost. However, the two issues have thus far mostly been investigated separately, thereby causing possible mismatch between the modular product structure and the manufacturing or assembly system. Therefore, the potential benefits of product modularity may not be materialized due to such mismatch. For this reason, this paper presents a concurrent approach to the product module selection and assembly line design problems to provide a set of harmonic solutions to the two problems and hence avoid the mismatch between design and manufacturing. The integrated nature of the problem leads to several noncommensurable and often conflicting objectives. The modified Chebyshev goal programming approach is applied to solve the multi-objective problem. A genetic algorithm is further developed to provide quick and near-optimum solutions. The proposed approach and the solution procedure have been applied to an ABS motor problem. The performance of the genetic algorithm has also been examined.

In Line Balancing principles, the total workload in the assembly process is divided as evenly as possible among the workstations, without violating the sequences and relations in the assembly operations. Line balancing is important in an assembly system as it balances the line and increases the efficiency, as well as the productivity of a system. A case study was conducted in the assembly line of an electrical accessories manufacturer in Malaysia. The cycle time for each station was recorded, and the standard cycle time was estimated. The productivity, as well as the efficiency of the current assembly line, were studied. In terms of the productivity, the performance of the current systems was 500 units/worker/day, while the expected productivity was 600 units/worker/day. An assembly line setting was proposed based on the Line Balancing Method; the productivity for the proposed line increased to 671 units/worker/day, or in rough additional increase around 34%.

Multi-objective Early-stage Design of Automotive Hybrid Assembly Lines

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.

AutomationML as Seamless Data Exchange Format for Integration of Automated Planning Tools for Assembly Line Design

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 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.

Automated design of multi-station assembly lines

Design of assembly lines is a knowledge intensive process relying significantly on experience and associated with high costs, long lead times and high probability of risks and reworks. This paper presents a methodology to support the early stage design of assembly lines through Knowledge Based Engineering (KBE). In this introductory paper the authors discuss the methodology to implement the KBE approach. A set of system engineering rules is extracted from direct interviews and domain best practices and knowledge about product and process is formalized. These rules will be implemented in a user-friendly platform allowing the design of first phase line layout by taking the defined system requirements (e.g. cycle time) as input. Then, the KBE approach is extended to a specific case study taken from the powertrain sector.

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.