The traditional deterministic day-ahead (DA) market clearing does not accommodate the uncertainty from variable renewable energy sources, resulting in an increasing activation of expensive reserves and curtailment events. Robust optimization (RO) has been proposed to mitigate this uncertainty. However, as RO considers worst-case scenarios, it results highly conservative solutions. This paper pr...

Linked simulation-optimization (S/O) approaches have been extensively used as tools in coastal aquifer management. However, parameter uncertainties seawater intrusion (SI) simulation models often undermine the reliability of derived solutions. In this study, a stochastic S/O framework is presented and applied to real-world case Longkou China. The three conflicting objectives maximizing total pu...

Quantitative optimization techniques have proven to be a powerful tool in tackling complicated decision-making problems in various application domains. Scheduling workforces, pricing airline tickets, managing production systems and designing engineering projects are just a small sample of the myriad industrial applications that have benefited from optimization techniques such as linear, nonline...

When modeling and solving a realistic optimization problem, we always face uncertainty presenting in the problem, e.g., unknown parameters. The uncertainty makes both the realism and tractability of optimization problems more difficult. In this section, we introduce the formulation with uncertainty and some examples stimulating from real-life decision making request to give a good description o...

Decentralized supply chain management is found to be significantly relevant in today’s competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final produc...

Most optimization problems in real life do not have accurate estimates of the problem parameters at the optimization phase. Stochastic optimization models have been studied widely in the literature to address this problem. The expected value optimization is reasonable in a repeated decision making framework. However, it does not sufficiently guard against the worst case future in more risk aver...

We consider a class of routing optimization problems under uncertainty in which all decisions are made before the uncertainty is realized. The objective is to obtain optimal routing solutions that would, as much as possible, adhere to a set of specified requirements after the uncertainty is realized. These problems include finding an optimal routing solution to meet the soft time window require...