Introduction of a new process with learning about installation and operation

IF 2 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Optimal Control Applications & Methods Pub Date : 2007-10-29 DOI:10.1002/OCA.4660090405
J. Fuller
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Abstract

A model is formulated and solved to investigate the optimal introduction of a new production process when the unit cost of the old process increases with cumulative output and the new process is subject to two learning curves. Capital costs of the new process come down with cumulative experience of installation of new capacity, and operating costs are reduced as operating experience accumulates. Demand is assumed to be constant. Because of an upper limit on the rate of expansion of capacity, conversion to the new process takes place gradually over an interval. It is shown that an equivalent, constant capital cost of the new process may be calculated before solving the model; the same may be done for the operating cost in some cases. An example with oil from the Canadian tar sands as the new process is solved, and extensions to the model are discussed.
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介绍新工艺,学习安装和操作
建立并求解了当旧工艺的单位成本随累积产量的增加而增加,且新工艺服从两条学习曲线时引入新工艺的最优模型。新工艺的资金成本随着新产能安装经验的积累而降低,运营成本随着运营经验的积累而降低。假设需求是恒定的。由于产能扩张速度有上限,向新工艺的转换是在一段时间内逐步进行的。结果表明,在求解模型之前,可以计算出新工艺的等价的、恒定的资本成本;在某些情况下,运营成本也可以采用同样的方法。以加拿大油砂为例进行了求解,并对该模型进行了扩展。
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来源期刊
Optimal Control Applications & Methods
Optimal Control Applications & Methods 工程技术-应用数学
CiteScore
3.90
自引率
11.10%
发文量
108
审稿时长
3 months
期刊介绍: Optimal Control Applications & Methods provides a forum for papers on the full range of optimal and optimization based control theory and related control design methods. The aim is to encourage new developments in control theory and design methodologies that will lead to real advances in control applications. Papers are also encouraged on the development, comparison and testing of computational algorithms for solving optimal control and optimization problems. The scope also includes papers on optimal estimation and filtering methods which have control related applications. Finally, it will provide a focus for interesting optimal control design studies and report real applications experience covering problems in implementation and robustness.
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