Journal of the Operations Research Society of China ›› 2023, Vol. 11 ›› Issue (3): 459-487.doi: 10.1007/s40305-021-00384-3

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A Multi-server Queue in a Multi-phase Random Environment with Waiting Servers and Customers' Impatience Under Synchronous Working Vacation Policy

Meriem Houalef1, Amina Angelika Bouchentouf2, Lahcene Yahiaoui3   

  1. 1. Mathematics Laboratory, University of Sidi Bel Abbes, Ecole Supérieure En Sciences Appliquées, Tlemcen, 13000, Algeria;
    2. Mathematics Laboratory, Djillali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes, 22000, Algeria;
    3. Laboratory of Stochastic Models, Statistic and Applications, University of Saida-Dr. Moulay Tahar, B. P. 138, En-Nasr, Saida, Algeria
  • Received:2020-07-24 Revised:2021-11-22 Online:2023-09-30 Published:2023-09-07
  • Contact: Amina Angelika Bouchentouf, Meriem Houalef, Lahcene Yahiaoui E-mail:bouchentouf_amina@yahoo.fr;houalef8o@gmail.com;lahceneya8@gmail.com

Abstract: In this paper, we develop an M/M/c queueing system in a Markovian environment with waiting servers, balking and reneging, under both synchronous single and multiple working vacation policies. When the system is in operative phase j, $ j=\overline{1,K}, $ customers are served one by one. Once the system is empty, the servers have to wait a random period of time before leaving, causing the system to move to vacation phase 0 at which new arrivals can be served at lower rate. Using the method of the probability generating functions, we establish the steady-state analysis of the system. Special cases of the queueing model are presented. Then, explicit expressions of the useful system characteristics are derived. In addition, a cost model is constructed to define the optimal values of service rates, simultaneously, to minimize the total expected cost per unit time via a quadratic fit search method. Numerical examples are provided to display the impact of different system characteristics.

Key words: Queueing models, Random environment, Working vacation policy, Impatient customers, Probability generating function, Optimization

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