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Author: Yuchen Li
Publisher: Springer Nature
ISBN: 9811942153
Category : Technology & Engineering
Languages : en
Pages : 164
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Book Description
This book introduces several mathematical models in assembly line balancing based on stochastic programming and develops exact and heuristic methods to solve them. An assembly line system is a manufacturing process in which parts are added in sequence from workstation to workstation until the final assembly is produced. In an assembly line balancing problem, tasks belonging to different product models are allocated to workstations according to their processing times and precedence relationships among tasks. It incorporates two features, uncertain task times, and demand volatility, separately and simultaneously, into the conventional assembly line balancing model. A real-life case study related to the mask production during the COVID-19 pandemic is presented to illustrate the application of the proposed framework and methodology. The book is intended for graduate students who are interested in combinatorial optimizations in manufacturing with uncertain input.
Author: Yuchen Li
Publisher: Springer Nature
ISBN: 9811942153
Category : Technology & Engineering
Languages : en
Pages : 164
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Book Description
This book introduces several mathematical models in assembly line balancing based on stochastic programming and develops exact and heuristic methods to solve them. An assembly line system is a manufacturing process in which parts are added in sequence from workstation to workstation until the final assembly is produced. In an assembly line balancing problem, tasks belonging to different product models are allocated to workstations according to their processing times and precedence relationships among tasks. It incorporates two features, uncertain task times, and demand volatility, separately and simultaneously, into the conventional assembly line balancing model. A real-life case study related to the mask production during the COVID-19 pandemic is presented to illustrate the application of the proposed framework and methodology. The book is intended for graduate students who are interested in combinatorial optimizations in manufacturing with uncertain input.
Author: Celso Gustavo Stall Sikora
Publisher: Springer Nature
ISBN: 3658362820
Category : Business & Economics
Languages : en
Pages : 190
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Book Description
Assembly lines are productive systems, which are very efficient for homogeneous products. In the automotive industry, an assembly line is used in the production of several vehicle variants, including numerous configurations, options, and add-ins. As a result, assembly lines must be at the same time specialized to provide high efficiency, but also flexible to allow the mass customization of the vehicles. In this book, the planning of assembly lines for uncertain demand is tackled and optimization algorithms are offered for the balancing of such lines. Building an assembly line is a commitment of several months or even years, it is understandable that the demand will fluctuate during the lifetime of an assembly line. New products are developed, others are removed from the market, and the decision of the final customer plays a role on the immediate demand. Therefore, the variation and uncertainty of the demand must be accounted for in an assembly line. In this book, methods dealing with random demand or random production sequence are presented, so that the practitioners can plan more robust and efficient production systems.
Author: Matthias Thürer
Publisher: Springer Nature
ISBN: 3031716337
Category :
Languages : en
Pages : 488
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Book Description
Author: Yuchen Li
Publisher:
ISBN:
Category : Assembly-line methods
Languages : en
Pages : 193
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Book Description
Assembly line has been widely used in producing complex items, such as automobiles and other transportation equipment, household appliances and electronic goods. Assembly line balancing is to maximize the efficiency of the assembly line so that the optimal production rate or optimal length of the line is obtained. Since the 1950s there has been a plethora of research studies focusing on the methodologies for assembly line balancing. Methods and algorithms were developed to balance an assembly line, which is operated by human workers, in a fast and efficient fashion. However, more and more assembly lines are incorporating automation in the design of the line, and in that case the line balancing problem structure is altered. For these automated assembly lines, novel algorithms are provided in this dissertation to efficiently solve the automated line balancing problem when the assembly line includes learning automata. Recent studies show that the task time can be improved during production due to machine learning, which gives the opportunities to rebalance the assembly line as the improvements occur and are observed. The concept of assembly line rebalancing or task reassignment are crucial for the assembly which is designed for small volume production because of the demand variation and rapid innovation of new product. In this dissertation, two forms of rebalancing are provided, forward planning and real time adjustment. The first one is to develop a planning schedule before production begins given the task time improvement is deterministic. The second one is to rebalance the line after the improvements are realized given the task time improvement is random. Algorithms address one sided and two sided assembly lines are proposed. Computation experiments are performed in order to test the performance of the novel algorithms and empirically validate the merit of improvement of production statistics.
Author: Prakhash Udayakumar
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 84
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Book Description
In a single model manual assembly line, product flows through series of workstations arranged in a sequential manner. Each workstation has a finite number of tasks and each task has probabilistic processing time. Due to the probabilistic nature of task time, the task times can exceed the expected standard task time at some instance. If a series of tasks exceeds in a particular station, then there is a risk that the product may exceed the cycle time. As a result, a small variability in task time can lead to large delays in the delivery lead time of the product. Most of the line balancing approaches assume deterministic task times thereby ignoring the impact of task time variability on the system performance measures. The larger the variability of task time, the higher the risk associated with the station. In this paper, the impact of variability in task time is quantified in terms of risk. Risk is defined as potential loss caused when the product fails to complete within the specified station time. For line balancing, in addition to cycle time balancing, the risk should be balanced in order to improve the performance of the assembly line. In this research, a risk based assembly line balancing technique for highly variable task times is presented. The results from the case study show that the method increases the performance of the assembly line while balancing the risk of delays at each station.
Author: Motokazu Orihata
Publisher:
ISBN:
Category :
Languages : en
Pages : 210
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Book Description
Author: Fred N. Silverman
Publisher:
ISBN:
Category : Assembly-line methods
Languages : en
Pages : 23
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Book Description
Author: Dodla Nageswara Rao
Publisher:
ISBN:
Category : Assembly-line balancing
Languages : en
Pages : 462
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Book Description
Line balancing is concerned with the optimal assignment of work elements to individual operators in an assembly tine of a mass producing system. This paper summarizes the assembly line balancing terminology, the computational methods, and objective functions applicable to a wide variety of assembly lines. Single and mixed-model situations for both constant and variable work element times are examined. A Back Tracking Method of Assembly Line Balancing (BALB) is developed and programmed in FORTRAN IV. BALB, as a manual procedure was able to find an optimal solution to problems that other existing methods such as Helgeson and Birnie's positional weight technique, could not yield. In general, BALB was also found to be simple and more efficient than the heuristic methods by Tonge, Hoffman, Mansoor and Arcus. The computer program, *BALB, accepts data for both single and mixed-model ALB problems and considers both constant and variable work element times. This program uses production shift time as the criterion for balancing the mixed-model lines. Numerical examples are used throughout the paper to illustrate the steps of various methods.
Author: David Bennett
Publisher:
ISBN:
Category : Manufacturing processes
Languages : en
Pages : 384
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Book Description
Author:
Publisher:
ISBN:
Category : Agricultural prices
Languages : en
Pages : 622
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Book Description
