The robust optimization methodology that we have introduced so far is built on a fundamental modeling approach, based set-theoretic, deterministic uncertainty models.

We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to Bayesian optimization: given an oracle that returns αapproximate solutions for distributions over objectives, we compute a distribution over solutions that is α-approximate in the worst case. We show that deran...

We study the maintenance task scheduling problem for an aircraft fleet in uncertain environment from viewpoint of robust optimization. Given a daily horizon, tasks delegated to shop should be scheduled such way that sufficient aircrafts are available on time meet demand planned missions. The either activities or unexpected repair jobs when major fault is detected during preor after-flight check...

Acceptance control charts (ACC), as an effective tool for monitoring highly capable processes, establish control limits based on specification limits when the fluctuation of the process mean is permitted or inevitable. For designing these charts by minimizing economic costs subject to statistical constraints, an economic-statistical model is developed in this paper. However, the parameters of s...

Topology optimization is a method to find the best distribution of material in a given design domain. The use of topology optimization for structural design often leads to slender structures which are sensitive to geometric imperfections such as the misplacement or misalignment of material. A robust approach to topology optimization is therefore presented which takes into account these geometri...

open-pit production scheduling (opps) problem focuses on determining a block sequencing and scheduling to maximize net present value (npv) of the venture under constraints. the scheduling model is critically sensitive to the economic value volatility of block, block weight, and operational capacity. in order to deal with the opps uncertainties, various approaches can be recommended. robust opti...

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...

In this paper, a robust optimization approach is proposed to design a dynamic cellular manufacturing system (DCMS) under uncertainty of processing time of products. In addition, a mathematical model considering cell formation, inter-cell design and production planning under a dynamic environment (i.e., product mix and demand are changed in each period) is presented. Therefore, reconfiguration b...