mathematical programming model

In this research, spare parts logistics in the aviation industry has been investigated. Since each aircraft consists of several components, the availability, ordering, and delivery of them are important during aircraft repair and maintenance operations and delays in supplying component spare parts will have a direct impact on the delivery of aircraft. This paper aims to optimize a supply of component spare parts using a programming model considering minimize the holding and purchasing costs. Moreover, two critical constraints including the purchase budget and repair capacity which are rarely mentioned in the previous papers have been noted in this research. FARSCO aviation maintenance & overhaul center as the biggest dedicated MRO center in the Middle East has been considered in this paper as the case study. Based on its capabilities, FARSCO provides services to all airlines and 90% of domestic airlines send their aircraft to this center for performing heavy checks and maintenance. The proposed model was verified and solved by using CPLEX solver and is constructed for the case by considering the numerical data. The results are obtained in two average demand and pessimistic modes (worst case scenario). Finally, a sensitivity analysis of the model is carried out to investigate the applicability of the problem. Results demonstrate the optimal number of maintenance jobs that can be completed to deliver at each period, as well as the order quantity of spare parts and the shortages of spare parts which are important for managers to deliver aircraft to the customers on time.

In this study, a mathematical programming model is developed for a single round-robin tournament problem to provide a schedule for the preliminary round of the Volleyball Nations League. In this setting, the aim is to assign the teams to the pools at each week as well as to specify the host teams of the pools. This schedule is obtained by minimizing the sum of the differences between the total distance traveled by every team and the average of the total distances traveled by all teams. Then, to evaluate the performance of the developed model, it is applied to obtain the optimal schedule for the preliminary round of the Volleyball Men’s Nations League in year 2018. The results indicate that the sum of the travel distance deviations from the average of the total travel distances of all teams obtained from the schedule provided by the mathematical model is significantly lower than that calculated from the schedule presented by the International Volleyball Federation. Moreover, the schedule presented by the International Volleyball Federation leads to a percentage gap of 449.92% in comparison with the optimal schedule provided by the developed model.

In healthcare, the Patient Appointment Scheduling (PAS) problem is one of the critical issues in Outpatient Chemotherapy Clinics (OCC). In the wake of this, this paper proposes a novel bi-objective mathematical programming model for solving the PAS problem in OCC. The developed mathematical model is inspired by cellular manufacturing. The first objective function minimizes the completion time of all treatments, and the second objective function maximizes the use of nurses' skills while considering clustered patients about their characteristics. To solve the bi-objective mathematical model, for the first time a hybrid approach based on Torabi-Hassini (TH) and Lagrange method is utilized. The results indicate that an increase in the number of nurses will enhance the treatment completion speed and allocation of nurses’ work skill. On the other hand, an increase in the number of chairs in clinics will decrease the assignments of nurses’ skills priority. The study supports decision makers in considering nurses' skills for the PAS problem. The results also denote the desirability of the proposed model. To validate the proposed model, OCC in Tehran is considered as a case study. Computational results reveal that considering nurses' skills in OCC and using the fuzzy clustering model, as a new method in patient clustering, lead to achieving a desirable and more realistic outcome.