Improved Algorithms for Machine Allocation in Manufacturing Systems
نویسندگان
چکیده
منابع مشابه
Improved Algorithms for Machine Allocation in Manufacturing Systems
In this paper we present two algorithms for a machine allocation problem occurring in manufacturing systems. For the two algorithms presented we prove worst-case performance ratios of 2 and 32 respectively. The machine allocation problem considered is a general convex resource allocation problem, which makes the algorithms applicable to a large variety of resource allocation problems. Numerical...
متن کاملImproved Approximation Algorithms for Resource Allocation
We study the problem of finding a most profitable subset of n given tasks, each with a given start and finish time as well as profit and resource requirement, that at no time exceeds the quantity B of available resource. We show that this NP-hard Resource Allocation problem can be (1/2−ε)-approximated in polynomial time, which improves upon earlier approximation results for this problem, the be...
متن کاملMachine Learning Algorithms in Heavy Process Manufacturing
In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economic...
متن کاملMachine learning algorithms for time series in financial markets
This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...
متن کاملMachine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Operations Research
سال: 1994
ISSN: 0030-364X,1526-5463
DOI: 10.1287/opre.42.3.523