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• This study considers a distributionally robust portfolio optimization problem with an ambiguous stochastic dominance constraint by assuming the unknown distribution of asset returns. • We propose the worst-case expected return and subject to an ambiguous second- order stochastic dominance constraint. • We use a cutting plane to solve our second-order stochastic dominance constraint portfolio optimization problem with ambiguity sets based on the Wasserstein metric. • It is also shown that the Wasserstein-moment ambiguity set-based distributionally robust portfolio optimization can be reduced to a semidefinite program and second-order conic programming. • We decompose this class of distributionally robust portfolio optimization into semi-infinite programming and apply the cutting surface method to solve it. In portfolio optimization, we may be dealing with misspecification of a known distribution, that stock returns follow it. The unknown true distribution is considered in terms of a Wasserstein-neighborhood of P to examine the tractable formulations of the portfolio selection problem. This study considers a distributionally robust portfolio optimization problem with an ambiguous stochastic dominance constraint by assuming the unknown distribution of asset returns. The objective is to maximize the worst-case expected return and subject to an ambiguous second-order stochastic dominance constraint. The expected return robustly stochastically dominates the benchmark in the second order over all possible distributions within an ambiguity set. It is also shown that the Wasserstein-moment ambiguity set-based distributionally robust portfolio optimization can be reduced to a semidefinite program and second-order conic programming. We use a cutting plane to solve our second-order stochastic dominance constraint portfolio optimization problem with ambiguity sets based on the Wasserstein metric. Then we decompose this class of distributionally robust portfolio optimization into semi-infinite programming and apply the cutting surface method to solve it. The captured optimization programs are applied to real-life data for more efficient comparison. The problems are examined in depth using the optimal solutions of the optimization programs based on the different setups. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approximate linear reformulation for distributionally robust joint chance programming with Wasserstein ambiguity sets and an efficient solution approach based on Benders decomposition. To provide a convex approximation to the distributionally robust chance constraint, we use the worst-case conditional value-at-risk constrained program. In addition, we derive an approach for distributionally robust joint chance programming with a hybrid ambiguity set that combines a Wasserstein ball with second-order moment constraints. This approach, which allows injecting domain knowledge into a Wasserstein ambiguity set and thus allows for less conservative solutions, has not been considered before. We propose two formulations of this problem, namely a semidefinite programming and a computationally favorable second-order cone programming formulation. The models and algorithms proposed in this paper are evaluated through computational experiments demonstrating their computational efficiency. In particular, the Benders decomposition algorithm is shown to be more than an order of magnitude faster than a standard solver allowing for the solution of large instances in a relatively short time. [ABSTRACT FROM AUTHOR]

With the stormy growth of marine renewable energy (MRE) and fishery, fair ocean zoning becomes crucial pillar for reconciling conservation and development. Furthermore, the ocean zoning problem with fairness is complicated by ambiguous spatial demand and suitability values (MRE, fishery, and conservation) due to the scarce data and complex environment. To effectively address this issue, we study an ocean zoning problem with fairness concerns under a public-private partnership from robust perspective, to design a spatial area-allocating scheme for the local authority and a fair spatial zone-layout strategy for sea users (MRE developer, fishery developer, and conservation manager). In the problem, a co-location strategy of MRE and fishery is established as a constraint to relieve spatial pressure and reduce development costs. We propose a robust bilevel ocean zoning model with demand ambiguity and decision-dependent value ambiguity impacted co-location decisions. Model analysis over fairness-concern degrees and a case study of the Huanghai sea justify several significant insights. First, a win-win strategy with cost advantage and a highly suitable spatial layout is obtained if the local authority realizes fairness preferences of sea users and sea users show moderate fairness concerns. Second, the robust strategy with decision-dependent value ambiguity may reduce the cost objective and increase the co-location area. Last but not least, the co-location strategy of MRE and fishery not only exerts economic advantage but also improves marine spatial utilization. • A robust bilevel ocean zoning model is the first developed for ocean zoning problem. • Fairness concern in term of spatial suitability is embedded in utility functions. • Co-location strategy of MRE and fishery is modeled from economic and spatial aspects. • Decision-dependent value ambiguities and demand ambiguity are considered. • Several significant insights into the ocean zoning in the Huanghai sea are obtained. [ABSTRACT FROM AUTHOR]