K competing queues with geometric service requirements and linear costs: The μc-rule is always optimal☆
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Cited by (68)
Optimal control for parallel queues with a single batch server
2022, Operations Research LettersCitation Excerpt :For more recent advances, see Armero and Conesa [1], Chang and Takine [5], Chen et al. [7] and the reference therein. Another line of research focuses on the optimal control of multi-class queueing systems, where the classical cμ-rule is proposed as the optimal policy under a variety of input assumptions, see Baras et al. [2], Buyukkoc et al. [4] and Shanthikumar and Yao [15]. Briefly speaking, when the system consists of a single server and multiple parallel queues, in order to minimize the expected total cost, the server should serve the non-idle queue with the maximal product of the queue-dependent waiting cost coefficient c and the service rate μ.
A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data
2018, Applied Soft Computing JournalCitation Excerpt :According to the previous relationship, class of the new consumers can be determined by their similar characteristics. To achieve this goal, many statistical models and optimization techniques are taken into consideration, such as linear discriminant analysis [4], integer programming [5], logit analysis [6], probit analysis [7], classification tree [8], linear programming [9], k-nearest neighbor [10]. However, many things are different when it comes to 21st century due to the rapid development of artificial intelligence (AI).
Exploiting the characteristics of serial queues to reduce the mean and variance of flow time using combined priority rules
2018, International Journal of Production EconomicsCitation Excerpt :This deficiency can also hinder the practicality of hyper-heuristics (Li et al., 2016). The SEPT rule has been proven useful for single-server queues as applying the cμ rule (Baras et al., 1985; Buyukkoc et al., 1985) minimises the weighted mean number of customers in queue (Avrachenkov et al., 2010). This rule considers the associated waiting cost per customer and divides it by the mean service rate of each customer class.
Optimality of periodic control for fluid models of polling systems with setups
2016, IFAC-PapersOnLineA simple policy for multiple queues with size-independent service times
2013, Operations Research LettersOptimal periodical behavior of a multiclass uid ow network
2013, IFAC Proceedings Volumes (IFAC-PapersOnline)
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This work was supported in part by the Department of Energy under Grant DOE-ACOl-78ET29244, A5 and the National Science Foundation under Grant ECS-82-04451.