Binary Integer Program Reformulation: A Set System Approximation Approach

This paper presents a generic reformulation framework for binary integer programs (BIPs) without imposing additional specifications for the objective function or constraints. To facilitate such generality, we introduce a set system approximation theory designed to identify the tightest inner and outer approximations for any binary solution space using special types of set systems. This development … Read more

Information Basis in Dynamic Robust Optimization

Dynamic robust optimization deals with sequential, multi-stage decisions in the face of uncertain, worst-case scenarios. To manage its complexity and the curse of dimensionality, decision rules simplify the search for an optimal policy. This paper explores a middle ground between two common decision rules: simple but imprecise constant policies, and accurate but less scalable affine … Read more

On Supervalid Inequalities for Binary Interdiction Games

Supervalid inequalities are a specific type of constraints often used within the branch-and-cut framework to strengthen the linear relaxation of mixed-integer programs. These inequalities share the particular characteristic of potentially removing feasible integer solutions as long as they are already dominated by an incumbent solution. This paper focuses on supervalid inequalities for solving binary interdiction … Read more

An Exact Approach for Solving Pickup-and-Delivery Traveling Salesman Problems with Neighborhoods

This paper studies a variant of the traveling salesman problem called the pickup-and-delivery traveling salesman problem with neighborhoods that combines traditional pickup and delivery requirements with the flexibility of visiting the customers at locations within compact neighborhoods of arbitrary shape. We derive two optimality conditions for the problem, a local condition that verifies whether a … Read more

Adjustability in Robust Linear Optimization

We investigate the concept of adjustability — the difference in objective values between two types of dynamic robust optimization formulations: one where (static) decisions are made before uncertainty realization, and one where uncertainty is resolved before (adjustable) decisions. This difference reflects the value of information and decision timing in optimization under uncertainty, and is related … Read more

Integer Programming Methods for Solving Binary Interdiction Games

This paper studies a general class of interdiction problems in which the solution space of both the leader and follower are characterized by two discrete sets denoted the leader’s strategy set and the follower’s structure set. In this setting, the interaction between any strategy-structure pair is assumed to be binary, in the sense that the … Read more

Integer Programming Formulations for Minimum Spanning Tree Interdiction

We consider a two-player interdiction problem staged over a graph where the leader’s objective is to minimize the cost of removing edges from the graph so that the follower’s objective, i.e., the weight of a minimum spanning tree in the residual graph, is increased up to a predefined level $r$. Standard approaches for graph interdiction … Read more