Following the well-studied two-stage optimization framework for stochastic optimization [14, 17], we study approximation algorithms for robust two-stage optimization problems with exponential number of scenarios. Prior to this work, Dhamdhere et al. [7] introduced approximation algorithms for two-stage robust optimization problems with polynomial number of scenarios. To model exponential number...

The process of marketing campaign optimization takes as input a set of offers, a set of customer segments and a set of communication channels, and determines the most profitable combinations by which offers should go to segments over channels, taking into account a set of constraints for the campaign. In this paper, we argue that the combination of data mining techniques with optimization model...

This report proposes a new two-stage optimization method for robust Model Predictive Control (RMPC) with Gaussian disturbance and state estimation error. Since the disturbance is unbounded, it is impossible to achieve zero probability of violating constraints. Our goal is to optimize the expected value of a objective function while limiting the probability of violating any constraints over the ...

Evaluation of a product or process involves simultaneous study of several product quality characteristics or responses. The ultimate goal of manufacturing process optimization is to determine the settings of the process variables such that the best combination of the responses is obtained. In case of multiple processing stages, determining optimal process condition is a critical and difficult t...

In this paper, a discrete particle swarm optimization method is proposed to solve the multi-objective task assignment problem in distributed environment. The objectives of optimization include the makespan for task execution and the budget caused by resource occupation. A two-stage approach is designed as follows. In the first stage, several artificial particles are added into the initialized s...

This paper extends the traditional Markowitz’s mean-variance optimization to a two-stage dynamic multi-period portfolio optimization. The underlying assets time series data are supposed to follow a discrete time triangular cointegrated vector model, and in addition random quadratic transaction costs are taken into consideration. A twostage dynamic multi-period approach is proposed, and the opti...