Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Production control and stock rationing for a make-to-stock system with parallel production channels

Abstract: This article considers the problem of production control and stock rationing in a make-to-stock production system with lost sales, multiple servers in parallel production channels, and several customer classes. It is assumed that independent stationary Poisson demand streams and exponential service times are in operation. At decision epochs, the control specifies whether or not to increase the number of active servers in conjunction with the stock allocation decision. Previously placed production orders cannot be cancelled. The system is modeled as an M/M/s make-to-stock queue, and properties of the optimal cost function and of the optimal production and rationing policies are characterized. It is shown that the optimal production policy is a state-dependent base-stock policy, and the optimal rationing policy is of threshold type. Furthermore, it is shown that the rationing levels are non-increasing in the number of active channels. It is also shown that the optimal ordering policy transforms into a bang-...

An integrated approach to machine selection and operation allocation problem

Abstract: Machine selection and operation allocation is a multi-criteria decision-making problem which involves the consideration of both qualitative and quantitative factors. Thus, a hybrid model integrating the knowledge-based expert system and the genetic algorithm may be effectively applied to the decision problem. This paper proposes a two-step approach where suitable machines for every operation in a work center is selected and optimized as a whole to obtain the optimum machine park. The first step of the model determines the suitability of each machine type for every operation using the knowledge-based expert system. The second stage searches through the solution space to find the optimal machine park with the use of a genetic algorithm. A real-life case study at an outdoor advertisement manufacturing company demonstrates the applicability of the model.

Analysis of the learning curve for robotic hysterectomy for benign gynaecological disease

Abstract: Background The objective was to evaluate the learning curve for performing a robotic hysterectomy to treat benign gynaecological disease. Methods Thirty-six patients underwent robotic hysterectomy for benign indications. A systematic chart review of consecutive cases was conducted. The collected data included age, BMI, operating time, set-up time, docking time, uterine weight, blood loss, intraoperative complications, postoperative complications, conversions to laparotomy and length of hospital stay. Results The mean operating, set-up and docking times were 169 ± 54.5, 52.9 ± 12.4 and 7.8 ± 7.6 min, respectively. The learning curve analysis revealed a decrease in both docking and operating times, with both curves plateauing after case 9. Conclusions The learning curve analysis revealed a decrease in docking time and operating time after case 9, suggesting that there might be a fast, learning curve for experienced laparoscopic surgeons to master robotic hysterectomy, and that the docking process does not have a significant negative influence on the overall operating time. Copyright © 2013 John Wiley & Sons, Ltd.