this paper discusses the portfolio selection based on robust optimization. since the parameters values of the portfolio optimization problem such as price of the stock, dividends, returns, etc. of per share are unknown, variable and their distributions are uncertain because of the market and price volatility, therefore, there is a need for the development and application of methodologies for de...

Perishability of blood products as well as uncertainty in demand amounts complicate the management of blood supply for blood centers. This paper addresses a mixed-integer linear programming model for blood platelets production planning while integrating the processes of blood collection as well as production/testing, inventory control and distribution. Whole blood-derived production methods for...

in this paper, integrated direct and reverse logistics considering production, distribution, customer, devastation, retrieval centers under uncertainty are developed. in this model, cost parameters are not certain, thus the scenario-based robust optimization method is applied. the aim of this model is to minimize the total cost and obtain a robust solution. finally, a practical case study is pr...

Optimization of noisy non-linear problems plays a key role in engineering and design problems. These optimization problems can't be solved effectively by using conventional optimization methods. However, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) seem very efficient to approach in these problems and became very popular. The efficiency of these ...

In the context of public transportation system, improving the service quality and robustness through minimizing the average passengers waiting time is a real challenge. This study provides robust stochastic programming models for train timetabling problem in urban rail transit systems. The objective is minimization of the weighted summation of the expected cost of passenger waiting time, its va...