A Finitely Convergent Cutting Plane, and a Bender’s Decomposition Algorithm for Mixed-Integer Convex and Two-Stage Convex Programs using Cutting Planes

We consider a general mixed-integer convex program. We first develop an algorithm for solving this problem, and show its nite convergence. We then develop a finitely convergent decomposition algorithm that separates binary variables from integer and continuous variables. The integer and continuous variables are treated as second stage variables. An oracle for generating a parametric … Read more

Polyhedral Analysis of a Polytope from a Service Center Location Problem with a Special Decision-Dependent Customer Demand

This paper establishes and analyzes a service center location model with a simple but novel decision-dependent demand induced from a maximum attraction principle. The model formulations are investigated in the distributionally-robust optimization framework for the capacitated and uncapacitated cases. A statistical model that is based on the maximum attraction principle for estimating customer demand and … Read more

A Distributionally-Robust Service Center Location Problem with Decision Dependent Demand Induced from a Maximum Attraction Principle

We establish and analyze a service center location model with a simple but novel decision-dependent demand induced from a maximum attrac- tion principle. The model formulations are investigated in the distributionally- robust optimization framework. A statistical model that is based on the max- imum attraction principle for estimating customer demand and utility gain from service … Read more

A Model of Supply-Chain Decisions for Resource Sharing with an Application to Ventilator Allocation to Combat COVID-19

We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency is to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse … Read more

Decomposition Algorithms for Distributionally Robust Optimization using Wasserstein Metric

We study distributionally robust optimization (DRO) problems where the ambiguity set is de ned using the Wasserstein metric. We show that this class of DRO problems can be reformulated as semi-in nite programs. We give an exchange method to solve the reformulated problem for the general nonlinear model, and a central cutting-surface method for the convex case, … Read more