Search results

This paper presents an investigation on the line balancing of an automated cylinder block production transfer line in order to reduce the total cycle time and increase machine utilization in an automotive plant. Results were verified by computer simulation, which showed increased throughput and higher machine utilization as a result of line balancing.

Three main operation lines of the transfer line were identified as critical and having the highest cycle time and were chosen for optimisation study. Strategies of re‐sequencing of existing operations and tools were used to reduce the cycle time of these critical operations and to balance the line. Results of a simulation study using Simul8 software are also presented to demonstrate the increase in machine utilisation and throughput as a result of line balancing.

Owing to line balancing, the cycle time of cylinder block line was reduced from 293.9 to 200 s, an almost 32 per cent reduction. This also resulted in increased throughput and machine utilisation. Throughput was increased by 65 per cent. Machine utilization was found to increase at all stations, with the highest increase at one station was recorded from 48 to 95 per cent due to balancing.

Introduces a new application to line balancing of automotive cylinder block production line. Demonstrates that effective strategies of re‐sequencing and changing of tools can lead to more balanced production line with increased throughput and higher machining utilisation, resulting in higher productivity.

Outlines the specific characteristics of semiautomatic production lines which, in relative terms, are given less attention than other types of line. Suggests a methodology for balancing such lines, making comparison thereof with the traditional method of line balancing. Applies the method to a case which is a typical semiautomatic line.

This paper briefly reviews the assembly line balancing techniques developed over the last 30 years. It attempts to establish the direction of research, to identify unexplored areas with potential for study and recommends future courses of action.

The purpose of this paper is to find the most efficient assembly line balancing solution across many heuristic line balancing methods, in assistance with a developed computer program.

In this paper, assembly line balancing problem was analyzed using t-shirt and knitted pants data. A computer program using MATLAB software for the solution of assembly line balancing problems has been developed. In this study, following heuristic assembly line balancing methods were applied: Hoffman method; position weight method; COMSOAL method; and Kilbridge and Wester method. A MATLAB program has been developed by taking into account of theoretical solution of all these methods. Later the program is developed further by analyzing solutions made manually and is made to verify the developed program.

Pre-studies which were conducted in order to decide which programming language would be the best choice for line balancing methods’ application came out with the result that MATLAB, from between C, C++, C# and Java, would be the best software choice. The main reason for this choice is that MATLAB is a powerful matrix operation software with a powerful user interface designing tool and has the tools to make development program to be used universally in every computer.

When the researches were investigated, it is clearly seen that, this study is the first research on using computer program for solving assembly line balancing problem.

Cycle 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.

In this paper a heuristic which consist of two stages is proposed to find a common task sequence for different cycle times in assembly lines.

It is shown that optimal NS for different cycle times can be achieved with a fixed task sequence.

The approach is limited to a single model case. Model variety together with cycle time variety can be investigated in further studies.

Assembly lines which require less time and cost for re‐balancing can be easily designed by the proposed approach.

ALB 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.

Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times.

In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times.

The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost.

Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case.

As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.

Continuous process flow is a prerequisite of lean systems as it helps to reduce throughput times, improve quality, minimize operational costs, and shorten delivery times. The purpose of this paper is to empirically demonstrate the application of a methodology that combines a time‐based study, discrete‐event simulation and the trial and error method to enable a leaner process through more efficient line balancing and more effective flow for a park homes production process. This method is replicable across other contexts and industry settings.

The paper reviews the UK park homes production industry and, specifically, a major factory that builds these homes. It compares the factory method to traditional on‐site construction methods. An empirical study of production times was carried out to collect data in order to analyse the current workload distribution and the process flow performance of the park homes production process. Finally, seven discrete‐event simulation models were developed in order to test different scenarios and define the optimum line balance for every section of the production process.

By combining time study, discrete‐event simulation and trial and error methods, the workload distribution and process flow performance of the park homes production line were analysed and improved. A reduction of between 1.82 and 36.32 percent in balancing losses in some sections of the process was achieved.

This paper supports current knowledge on process flow improvement and line balancing by exploring and analysing these issues in a real‐life context. It can be used to guide production management practitioners in their selection of methods and demonstrates how they are exploited when seeking to improve process flow, efficiency and line balancing of production operations.

The study uses a real industrial application to demonstrate how the methodological combination and deployment of process flow improvement strategies, such as time study, simulation, and trial and error, can help organisations achieve process flow improvements and, as a consequence, a leaner production process.

Design, balancing, and sequencing are the key issues associated with assembly lines (ALs). The purpose of this paper is to identify AL design issues and to develop an integrated methodology for mixed‐model assembly line balancing (MMALB) and sequencing. Primarily, mixed‐model lines are utilized for high‐variety, low‐volume job shop or batch production. Variation of a generic product is important for the manufacturers as the demand is mostly customer driven in the present global market.

Different AL design norms, performance indexes, and AL workstation indexes have been identified in the initial stage of this work. As the paper progresses, it has focused towards an integrated approach for MMALB and sequencing addressed for small‐ and medium‐scale assembly plants. A small‐scale practical problem has been justified with this integrated methodology implemented by MATLAB.

ALs execution in the production floor require many important factors to be considered. Different line orientations, production approaches, line characteristics, performance and workstation indexes, problem definitions, balancing and product sequencing in accordance with the objective functions are needed to be taken into account by the line designer.

This paper has highlighted the important AL design characteristics and also provided an integrated approach for balancing mixed‐model assembly lines (MMALs) combined with sequencing heuristic. The findings of this paper can be helpful for the designers while designing an AL. The integrated approach for balancing and sequencing of MMALs can be used as a functional tool for assembly‐based contemporary industries.

Optimizing material handling within the factory is one of the key problems of modern assembly line systems. The purpose of this paper is to focus on simultaneously balancing a robotic assembly line and the scheduling of material handling required for the operation of such a system, a topic that has received limited attention in academia. Manufacturing industries focus on full autonomy because of the rapid advancements in different elements of Industry 4.0 such as the internet of things, big data and cloud computing. In smart assembly systems, this autonomy aims at the integration of automated material handling equipment such as automated guided vehicles (AGVs) to robotic assembly line systems to ensure a reliable and flexible production system.

This paper tackles the problem of designing a balanced robotic assembly line and the scheduling of AGVs to feed materials to these lines such that the cycle time and total tardiness of the assembly system are minimized. Because of the combination of two well-known complex problems such as line balancing and material handling and a heuristic- and metaheuristic-based integrated decision approach is proposed.

A detailed computational study demonstrates how an integrated decision approach can serve as an efficient managerial tool in designing/redesigning assembly line systems and support automated transportation infrastructure.

This study is beneficial for production managers in understanding the main decisional steps involved in the designing/redesigning of smart assembly systems and providing guidelines in decision-making. Moreover, this study explores the material distribution scheduling problems in assembly systems, which is not yet comprehensively explored in the literature.

The increasing energy shortage leads to worldwide attentions. This paper aims to develop a mathematical model and optimization algorithm to solve the energy-oriented U-shaped assembly line balancing problem. Different from most existing works, the energy consumption is set as a major objective.

An improved flower pollination algorithm (IFPA) is designed to solve the problem. The random key encoding mechanism is used to map the continuous algorithm into discrete problem. The pollination rules are modified to enhance the information exchange between individuals. Variable neighborhood search (VNS) is used to improve the algorithm performance.

The experimental results show that the two objectives are in conflict with each other. The proposed methodology can help manager obtain the counterbalance between them, for the larger size balancing problems, and the reduction in objectives is even more significant. Besides, the experiment results also show the high efficiency of the proposed IFPA and VNS.

The main contributions of this work are twofold. First, a mathematical model for the U-shaped assembly line balancing problem is developed and the model is dual foci including minimized SI and energy consumption. Second, an IFPA is proposed to solve the problem.