Robust simulation optimization using φ-divergence

Variable fidelity robust optimization of pulsed laser orbital debris removal under epistemic uncertainty

A robust optimum solution is defined as one that is optimum and maintains its feasibility regardless of the values taken by uncertain parameters within a bounded interval. Solving for a robust optimum solution, however, can be computationally costly. To reduce the computational cost, a new robust optimization method, surrogate feasibility testing–cutting robust optimization (SFTC-RO), is proposed that solves two optimization subproblems. In the first subproblem, a scenario robust optimization problem is solved using a local optimization technique. In the second subproblem, a feasibility check and constraint cut of the feasible domain are performed via operations on a surrogate model of the constraints. The two subproblems are iteratively solved until convergence at a robust optimum solution. Results from numerical and engineering examples show that on average the proposed method can obtain robust optimal solutions at a lower computational cost and with better scalability than an existing method from the literature.

This paper presents a new approach aims to solve robust multidisciplinary design optimization MDO problem called Improved Multi-objective Robust Collaborative Optimization . This method combines the Multi-objective Robust Collaborative Optimization method, the Worst Possible Point constraint cuts and the Genetic algorithm NSGA-II type as an optimizer to solve the robust optimization problem of complex structure named Y-stiffened panel under interval uncertainty . The proposed approach hierarchically decomposes the optimization problem into a structure level considered as an upper level in the Y-stiffened panel and a second level considered as a lower level of the studied panel. A robust multi-objective optimization problem intended to optimize the eigenfrequency, the global mass and the displacement at a fixed point of the Y-stiffened panel at the first level and each structure’s robust optimization problem allows optimizing its eigenfrequency and mass limited by their local constraint functions at the second one. Tor demonstrate our method, an engineering example of Y-stiffened panel is treated. A good performance of proposed method is proved by a comparison between obtained results and Non-Distributed Multi-objective Robust Optimization .

Chapter 4 - Data-driven robust stochastic optimization for power systems operations

In order to improve the aerodynamic stability of a two-dimensional airfoil with deformable trailing edge during the change of external conditions,a robust optimization method considering the uncertainty was proposed. Based on class-shape function transformation( CST) method,a parametric model was established to represent the geometry of a two-dimensional airfoil with a deformable trailing edge. The differences between robust optimization method and deterministic optimization method were discussed. Taking into account the uncertain airfoil geometry and inlet Mach number,the robust optimization method was applied to maximize the mean value of the lift-to-drag ratio and minimize its standard deviation. The actuation power requirement of the optimized airfoil with a deformable trailing edge was calculated. The results show that the robust optimization method can help to improve the aerodynamic performance of morphing airfoil and reduce the sensitivity of this performance to inlet Mach number simultaneously and to reduce the actuation power requirement of the robust optimal airfoil.

Robust optimization techniques attempt to find a solution that is both optimum and relatively insensitive to input uncertainty. In general, these techniques are computationally more expensive than their deterministic counterparts. In this article two new robust optimization methods are presented. The first method is called gradient-assisted robust optimization (GARO). In GARO, a robust optimization problem is first converted to a deterministic one by using a gradient-based approximation technique. After solving this deterministic problem, the solution robustness and the accuracy of the approximation are checked. If the accuracy meets a threshold, a robust optimum solution is found; otherwise, the approximation is adaptively modified until the threshold is met and a solution, if it exists, is obtained. The second method is a faster version of GARO called quasi-concave gradient-assisted robust optimization (QC-GARO). QC-GARO is for problems with quasi-concave objective and constraint functions. The difference between GARO and QC-GARO is in the way that they check the approximation accuracy. Both GARO and QC-GARO methods are applied to a set of six engineering design test problems and the results are compared with a few related previous methods. It was found that, compared to the methods considered, GARO could solve all test problems but with a higher computational effort compared to QC-GARO. However, QC-GARO was computationally much faster when it was able to solve the problems.

This thesis explores possibilities and limitations of extending classical combinatorial optimization problems for network flows and network design. We propose new mathematical models for logistics networks that feature commodities with multidimensional properties, e.g. their mass and volume, to capture consolidation effects of commodities with complementing properties. We provide new theoretical insights and solution methods with immediate practical impact that we test on real-world instances from the automotive, chemical, and retail industry. The first model is for tactical transportation planning with temporal consolidation effects. We propose various heuristics and prove for our instances, that most of our solutions are within a single-digit percentage of the optimum. We also study problem variants where commodities are routed unsplittably and give hardness results for various special cases and a dynamic program that finds optimal forest solutions, which overestimate real costs. The second model is for strategic route planning under uncertainty. We provide for a robust optimization method that anticipates fluctuations of demands by minimizing worst-case costs over a restricted scenario set. We show that the adversary problem is NP-hard. To still find solutions with very good worst-case cost, we derive a carefully relaxed and simplified MILP, which solves well for large instances. It can be extended to include hub decisions leading to a robust M-median hub location problem. We find a price of robustness for our instances that is moderate for scenarios using average demand values as lower bounds. Trend based scenarios show a considerable tradeoff between historical average costs and worst case costs. Another robustness concept are incremental hub chains that provide solutions for every number of hubs to operate, such that they are robust under changes of this number. A comparison of incremental solutions with M-median solutions obtained with an LP-based search suggests that a price of being incremental is low for our instances. Finally, we investigate the problem of scheduling the maintenance of edges in a network. We focus on maintaining connectivity between two nodes over time. We show that the problem can be solved in polynomial time in arbitrary networks if preemption is allowed. If preemption is restricted to integral time points, the problem is NP-hard and for the non-preemptive case, we show strong non-approximability results.

We consider a general worst-case robust convex optimization problem, with arbitrary dependence on the uncertain parameters, which are assumed to lie in some given set of possible values. We describe a general method for solving such a problem, which alternates between optimization and worst-case analysis. With exact worst-case analysis, the method is shown to converge to a robust optimal point. With approximate worst-case analysis, which is the best we can do in many practical cases, the method seems to work very well in practice, subject to the errors in our worst-case analysis. We give variations on the basic method that can give enhanced convergence, reduce data storage, or improve other algorithm properties. Numerical simulations suggest that the method finds a quite robust solution within a few tens of steps; using warm-start techniques in the optimization steps reduces the overall effort to a modest multiple of solving a nominal problem, ignoring the parameter variation. The method is illustrated with several application examples.

Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty

This paper proposes a data-driven method based on distributionally robust optimization to determine the maximum penetration level of distributed generation (DG) for active distribution networks. In our method, the uncertain DG outputs and load demands are formulated as stochastic variables following some ambiguous distributions. In addition to the given expectations and variances, the polyhedral uncertainty intervals are employed for the construction of the probability distribution set to restrict possible distributions. Then, we decide the optimal sizes and locations of DG to maximize the total DG hosting capacity under the worst-case probability distributions among this set. Since more information is utilized, our proposed model is expected to be less conservative than the robust optimization method and the traditional distributionally robust method. Using the CVaR (Conditional Value at Risk) reformulation technique and strong duality, we transform the proposed model into an equivalent bilinear matrix inequality problem, and a sequential convex optimization algorithm is applied for solution. Our model guarantees that the probability of security constraints being violated will not exceed a given risk threshold. Besides, the predefined risk level can be tuned to control the conservativeness of our model in a physically meaningful way. The effectiveness and robustness of this proposed method are demonstrated numerically on the two modified IEEE test systems.

Two-stage robust optimization for space heating loads of buildings in integrated community energy systems

In this paper, we design a Branch and Bound algorithm based on interval arithmetic to address nonconvex robust optimization problems. This algorithm provides the exact global solution of such difficult problems arising in many real life applications. A code was developed in MatLab and was used to solve some robust nonconvex problems with few variables. This first numerical study shows the interest of this approach providing the global solution of such difficult robust nonconvex optimization problems.

In this article, a new robust optimization method is proposed to simultaneously optimize the expectation and variability of system performance with parametric uncertainties and fuzzy variables. The expectation-entropy model is presented to characterize the fuzzy robust optimization problem as an equivalent biobjective optimization problem. An approximate mapping method is developed to calculate the response of fuzzy variables, which improves the computational efficiency of objective functions. Then, according to the decision makers’ preference for objectives, the optimization framework based on Stackelberg game is established. A leader–follower state transition algorithm is designed to search for the equilibrium solutions. Two practical case studies are provided to show the effectiveness of the new optimization approach in both subjective judgment and objective assessment.

This paper is concerned with the problem of robust optimization method for coupling parametrization design of space observation system. The objective is to develop an robust optimization framework to design space observation system. An improved robust collaborative optimization (IRCO) method for complex system design using the hybrid optimization algorithm is developed, which associates genetic algorithm (GA) with tabu search (TS). The proposed method can avoid unsolvability or converging difficulty and handle uncertainty within and across disciplines more efficiently and reasonably. The results demonstrate the effectiveness and applicability of the proposed method.

The uncertainty of renewable energy restricts their penetration in distributed network. Demand response (DR) provides a new scheme to improve the utilization rate of renewable energy. However, traditional stochastic optimization and robust optimization methods have some limitations dealing with uncertainty of renewable generation and demand response. This paper proposes a distributionally robust optimization model for distributed network energy management to improve the utilization rate of renewable energy. Firstly, a large number of historical data of user load and renewable generation are analyzed by K-means clustering method to obtain typical scenarios and their corresponding probability distribution. Then, the confidence set of probability distribution constrained by 1-norm and $\infty$ -norm is established to construct the distributionally robust optimization model for distributed network energy management. This model considers uncertain renewable energy and user load under the worst probability distribution, and dispatches user load and distributed generation in the condition of meeting security constraints and user power consumption constraints to search the optimal solution. Finally, columns and constraints generation (CCG) algorithm is proposed to solve the model, and the effectiveness of proposed model is verified based on simulation.