applying robust optimization to solve product mix problem in automotive industries in this article, applying robust optimization in product mix problem in automotive industries is presented. the problem includes determining both the quantity and the identification of each product to produce to maximize profit. at first, the deterministic model will be presented. then, robust model of product mi...

This paper proposes a novel optimization approach for multi-scenario multi-objective robust decision making, as well an alternative way scenario discovery and identifying vulnerable scenarios even before any solution generation. To demonstrate test the approach, we use classic shallow lake problem. We compare results obtained with to those previously used approaches. show that guarantees feasib...

Determination of optimum location for drilling a new well not only requires engineering judgments but also consumes excessive computational time. Additionally, availability of many physical constraints such as the well length, trajectory, and completion type and the numerous affecting parameters including, well type, well numbers, well-control variables prompt that the optimization approaches b...

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 ...

This paper introduces a method for solving multi-objective optimization problems in uncertain environment. When the uncertainty factors of the optimization problem can be included into the mathematical model, through bounded intervals, [I]RMOA (Interval Robust Multi-objective Algorithm) can find an enclosure of the robust Pareto frontier. In this approach, the robust Pareto solutions are the on...

There are several different methods to treat uncertainty in literacy. First, sensitivity analysis which is a post-optimization method analyzing stability of generated solutions. Second, stochastic programming is a modeling approach, and this method is limited to that the uncertainty is stochastic in nature. Third, “Robust Mathematical Programming” is that a candidate solutions allowing to viola...

As most robust combinatorial min-max and min-max regret problems with discrete uncertainty sets are NP-hard, research in approximation algorithm and approximability bounds has been a fruitful area of recent work. A simple and well-known approximation algorithm is the midpoint method, where one takes the average over all scenarios, and solves a problem of nominal type. Despite its simplicity, th...

As most robust combinatorial min-max and min-max regret problems with discrete uncertainty sets are NP-hard, research into approximation algorithm and approximability bounds has been a fruitful area of recent work. A simple and wellknown approximation algorithm is the midpoint method, where one takes the average over all scenarios, and solves a problem of nominal type. Despite its simplicity, t...