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On the number of customers served in the M/G/1 retrial queue: first moments and maximum entropy approach
Affiliation:1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China;2. City College, Kunming University of Science and Technology, Kunming 650051, China;1. Department of Medical Oncology, National Cancer Centre Singapore, Singapore;2. Department of Haematology–Oncology, National University Health System, Singapore;3. Divisions of Clinical Trials and Epidemiological Sciences;4. Oncologic Imaging, National Cancer Centre Singapore, Singapore;5. Cancer Science Institute of Singapore, National University of Singapore, Singapore;6. SingHealth Duke-NUS Radiological Sciences Academic Clinical Program, Singapore;7. Laboratory of Molecular Endocrinology, Division of Molecular and Cellular Research, National Cancer Centre Singapore, Singapore;8. Department of Pathology, Singapore General Hospital, Singapore, Singapore
Abstract:In this paper we present general results on the number of customers, I, served during the busy period in an M/G/1 retrial system. Its analysis in terms of Laplace transforms has been previously discussed in the literature. However, this solution presents important limitations in practice; in particular, the moments of I cannot be obtained by direct differentiation. We propose a direct method of computation for the second moment of I and also for the probability of k,k⩽4, customers being served in a busy period. Then, the maximum entropy principle approach is used to estimate the true distribution of I according to the available information.Scope and purposeWe consider an M/G/1 queue with retrials. Retrial queueing systems are characterized by the fact that, an arriving customer who finds the server busy is obliged to leave the service area and return later to repeat his request after some random time. We deal with I, the number of customers served during the busy period of a retrial queue, and obtain closed expressions for its main characteristics, which will be employed in order to estimate the true distribution of this random variable.
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