A multi-station unreliable machine model with working vacation policy and customers' impatience. Issue 6 (2nd November 2022)

• Record Type: Journal Article
• Authors: Bouchentouf, Amina Angelika; Boualem, Mohamed; Yahiaoui, Lahcene; Ahmad, Hijaz
• Is Part Of: Quality technology & quantitative management. Volume 19:Issue 6(2022)
• Page Start: 766
• Page End: 796
• Publication Date: 2022-11-02
• Subjects: System reliability -- customers' impatience -- working vacation -- Q-matrix method -- optimization; 60K25 -- 68M20 -- 90B22; Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2022.2054088
• Languages: English
• ISSNs: 1684-3703

Abstract

ABSTRACT: Working vacation queues with breakdowns and customers impatience have many applications in different real-life situations. The development of these models to determine their performance is extremely important. In this paper, we deal with a finite population Markovian multi-server machine system with breakdowns, repairs, Bernoulli feedback, balking, reneging, and retention of reneged customers, under multiple synchronous working vacations. The investigated model has a potential application in the real-world machine systems, such as the automated manufacturing systems with limited space. For the analysis purpose, we employ Q-matrix method in order to obtain the steady-state probabilities as well as closed-form expressions for several system characteristics. Particular cases of the current study are presented. We construct the expected cost function and develop an optimization problem to determine the optimum cost. The direct search method and the Quasi–Newton method are applied to find the optimum system capacity, the minimum number of servers, and the optimum service rates during both working vacation and regular busy periods at minimum cost. Lastly, a sensitivity analysis for the numerical simulation is carried out.