Saddle point approximation approaches for two-stage robust optimization problems

Ning Zhang () and Chang Fang ()
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Ning Zhang: Dongguan University of Technology
Chang Fang: Anhui Normal University

Journal of Global Optimization, 2020, vol. 78, issue 4, No 1, 670 pages

Abstract: Abstract This paper aims to present improvable and computable approximation approaches for solving the two-stage robust optimization problem, which arises from various applications such as optimal energy management and production planning. Based on sampling finite number scenarios of uncertainty, we can obtain a lower bound approximation and show that the corresponding solution is at least $${\varepsilon }$$ ε -level feasible. Moreover, piecewise linear decision rules (PLDRs) are also introduced to improve the upper bound that obtained by the widely-used linear decision rule. Furthermore, we show that both the lower bound and upper bound approximation problems can be reformulated into solvable saddle point problems and consequently be solved by the mirror descent method.

Keywords: Two-stage robust optimization; Randomized approach; Piecewise linear decision rule; Saddle point problem; Mirror descent algorithm (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10898-019-00836-4

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