We consider the problem of appointment scheduling in a robust optimization framework. The appointment scheduling problem arises in many service operations, for example health care. For each job, we are given its minimum and maximum possible execution times. The objective is to find an appointment schedule for which the cost in the worst case scenario of the realization of the processing times o...

Data Envelopment Analysis (DEA) is one of the popular and applicable techniques for assessing and ranking the stocks or other financial assets. It should be noted that in the financial markets, most of the times, the inputs and outputs of DEA models are accompanied by uncertainty. Accordingly, in this paper, a novel Robust Data Envelopment Analysis (RDEA) model, which is capable to be used in t...

a chaotic optimization algorithm (coa) based approach for the robust coordinated design of the upfc power oscillation damping controller and the conventional power system stabilizer has been investigated in this paper. chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from local optimum, is a promising tool fo...

Recently, there has been a growing interest in distributionally robust optimization (DRO) as principled approach to data-driven decision making. In this paper, we consider two-stage stochastic problem with discrete scenario support. While much research effort devoted tractable reformulations for DRO problems, especially those continuous support, few efficient numerical algorithms were developed...

In this work, we consider the problem of daily production optimization in the upstream oil and gas domain. The objective is to find the optimal decision variables that utilize the production systems efficiently and maximize the revenue. Typically, mathematical models are used to find the optimal operation in such processes. However, such prediction models are subject to uncertainty that has bee...

Multistage problems with uncertain parameters and integer decisions variables are among the most difficult applications of robust optimization (RO). The challenge in these is to find optimal here-and-now decisions, taking into account that wait-and-see have adapt revealed values parameters. An existing approach solve construct piecewise constant decision rules by adaptively partitioning uncerta...