the paper discusses the location-allocation model for logistic networks and distribution centers through considering uncertain parameters. in real-world cases, demands and transshipment costs change over the period of the time. this may lead to large cost deviation in total cost. scenario based robust optimization approaches are proposed where occurrence probability of each scenario is not know...

optimization of maintenance resources to maximize the system availability is a major concern in different manufacturing systems. therefore, a lot of effort is put to construct optimization models to reach the maximum availability level and to reduce the costs of lack of availability. however, despite these efforts, data uncertainty in the real world problems was neglected in proposed models whi...

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 ...

 Ambiguity in the inputs of the models is typical especially in portfolio selection problem where the true distribution of random variables is usually unknown. Here we use robust optimization approach to address the ambiguity in conditional-value-at-risk minimization model. We obtain explicit models of the robust conditional-value-at-risk minimization for polyhedral and correlated polyhedral am...

There are many real problems in which multiple responses should be optimized simultaneously by setting of process variables. One of the common approaches for optimization of multi-response problems is desirability function. In most real cases, there is a correlation structure between responses so ignoring the correlation may lead to mistake results. Hence, in this paper a robust approach based ...

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 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...

optimization models have been used to support decision making in production planning for a long time. however, several of those models are deterministic and do not address the variability that is present in some of the data. robust optimization is a methodology which can deal with the uncertainty or variability in optimization problems by computing a solution which is feasible for all possible ...