Multi-Objective Probabilistically Constrained Programming with Variable Risk: New Models and Applications

We consider a class of multi-objective probabilistically constrained problems MOPCP with a joint chance constraint, a multi-row random technology matrix, and a risk parameter (i.e., the reliability level) defined as a decision variable. We propose a Boolean modeling framework and derive a series of new equivalent mixed-integer programming formulations. We demonstrate the computational efficiency of the formulations that contain a small number of binary variables. We provide modeling insights pertaining to the most suitable reformulation, to the trade-off between the conflicting cost/revenue and reliability objectives, and to the scalarization parameter determining the relative importance of the objectives. Finally, we propose several MOPCP variants of multi-portfolio financial optimization models that implement a downside risk measure and can be used in a centralized or decentralized investment context. We study the impact of the model parameters on the portfolios, show, via a cross-validation study, the robustness of the proposed models, and perform a comparative analysis of the optimal investment decisions

Integer Programming Approaches for Distributionally Robust Chance Constraints with Adjustable Risks

We study distributionally robust chance constrained programs (DRCCPs) with individual chance constraints and random right-hand sides. The DRCCPs treat the risk tolerances associated with the distributionally robust chance constraints (DRCCs) as decision variables to trade off between the system cost and risk of violations by penalizing the risk tolerances in the objective function. We consider two types of Wasserstein ambiguity sets: one with finite support and one with a continuum of realizations. By exploring the hidden discrete structures, we develop mixed integer programming reformulations under the two types of ambiguity sets to determine the optimal risk tolerance for the chance constraint. Valid inequalities are derived to strengthen the formulations. We test instances with transportation problems of diverse sizes and a demand response management problem

An augmented lagrangian decomposition method for chance-constrained optimization problems

Joint chance-constrained optimization problems under discrete distributions arise frequently in financial management and business operations. These problems can be reformulated as mixed-integer programs. The size of reformulated integer programs is usually very large even though the original problem is of medium size. This paper studies an augmented Lagrangian decomposition method for finding high-quality feasible solutions of complex optimization problems, including nonconvex chance-constrained problems. Different from the current augmented Lagrangian approaches, the proposed method allows randomness to appear in both the left-hand-side matrix and the right-hand-side vector of the chance constraint. In addition, the proposed method only requires solving a convex subproblem and a 0-1 knapsack subproblem at each iteration. Based on the special structure of the chance constraint, the 0-1 knapsack problem can be computed in quasi-linear time, which keeps the computation for discrete optimization subproblems at a relatively low level. The convergence of the method to a first-order stationary point is established under certain mild conditions. Numerical results are presented in comparison with a set of existing methods in the literature for various real-world models. It is observed that the proposed method compares favorably in terms of the quality of the best feasible solution obtained within a certain time for large-size problems, particularly when the objective function of the problem is nonconvex or the left-hand-side matrix of the constraints is random

Safe Zero-Shot Model-Based Learning and Control: A Wasserstein Distributionally Robust Approach

This paper explores distributionally robust zero-shot model-based learning and control using Wasserstein ambiguity sets. Conventional model-based reinforcement learning algorithms struggle to guarantee feasibility throughout the online learning process. We address this open challenge with the following approach. Using a stochastic model-predictive control (MPC) strategy, we augment safety constraints with affine random variables corresponding to the instantaneous empirical distributions of modeling error. We obtain these distributions by evaluating model residuals in real time throughout the online learning process. By optimizing over the worst case modeling error distribution defined within a Wasserstein ambiguity set centered about our empirical distributions, we can approach the nominal constraint boundary in a provably safe way. We validate the performance of our approach using a case study of lithium-ion battery fast charging, a relevant and safety-critical energy systems control application. Our results demonstrate marked improvements in safety compared to a basic learning model-predictive controller, with constraints satisfied at every instance during online learning and control.Comment: In review for CDC2

Threshold boolean form for joint probabilistic constraints with random technology matrix.

We develop a new modeling and exact solution method for stochastic programming problems that include a joint probabilistic constraint in which the multirow random technology matrix is discretely distributed. We binarize the probability distribution of the random variables in such a way that we can extract a threshold partially defined Boolean function (pdBf) representing the probabilistic constraint. We then construct a tight threshold Boolean minorant for the pdBf. Any separating structure of the tight threshold Boolean minorant defines sufficient conditions for the satisfaction of the probabilistic constraint and takes the form of a system of linear constraints. We use the separating structure to derive three new deterministic formulations equivalent to the studied stochastic problem. We derive a set of strengthening valid inequalities for the reformulated problems. A crucial feature of the new integer formulations is that the number of integer variables does not depend on the number of scenarios used to represent uncertainty. The computational study, based on instances of the stochastic capital rationing problem, shows that the MIP reformulations are orders of magnitude faster to solve than the MINLP formulation. The method integrating the derived valid inequalities in a branch-andbound algorithm has the best performance