Assembly Line Rebalancing With Non Constant Task Time Attribute
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Assembly Line Balancing under Uncertain Task Time and Demand Volatility
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.
Assembly Line Rebalancing with Non-constant Task Time Attribute
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.
Assembly-Line Balancing under Demand Uncertainty
Author: Celso Gustavo Stall Sikora
language: en
Publisher: Springer Nature
Release Date: 2022-01-11
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.