In this paper, we address the problem of allocating and scheduling employees for work shifts in the pharmacy sector of a private hospital. To tackle this issue, we introduce the pharmacy staff scheduling problem (PSSP) in the literature. To solve the problem, we propose a mixed-integer programming formulation that considers various aspects, such as the … Read more
Many optimization problems are made more challenging due to multiple, conflicting criteria. The subjective nature of balancing these criteria motivates techniques for inverse optimization. This study establishes foundations for an exact representation of the inverse feasible region of a multiobjective integer program. We provide the first insights into its exact structure, as well as two … Read more
Sparse Principal Component Analysis (SPCA) is a fundamental technique for dimensionality reduction, and is NP-hard. In this paper, we introduce a randomized approximation algorithm for SPCA, which is based on the basic SDP relaxation. Our algorithm has an approximation ratio of at most the sparsity constant with high probability, if called enough times. Under a … Read more
Implied-integer detection is a well-known presolving technique that is used by many Mixed-Integer Linear Programming solvers. Informally, a variable is said to be implied integer if its integrality is enforced implicitly by integrality of other variables and the constraints of a problem. In this work we formalize the definition of implied integrality by taking a … Read more
We focus on multi-agent, multi-objective problems, particularly on those where the objectives admit a potential structure. We show that the solution to the potential multi-objective problem is always a noncooperative optimum for the multi-agent setting. Furthermore, we identify a class of problems for which every noncooperative solution can be computed via the potential problem. We … Read more
The Universal Classification (UC) problem seeks an optimal classifier from a universal policy space to minimize the expected 0-1 loss, also known as the misclassification risk. However, the conventional empirical risk minimization often leads to overfitting and poor out-of-sample performance. To address this limitation, we introduce the Distributionally Robust Universal Classification (DRUC) formulation, which incorporates … Read more
In this paper, we study a joint problem in which the Independent System Operator (ISO) intends to minimize the joint cost of operation and investment in a network structure. The problem is formulated through operational and investment control variables; we discuss the hierarchy between them and use the so-called Day Ahead Problem to find an … Read more
There is a growing need to expand and strengthen the industrial engineering/operations research workforce. Undergraduate research experiences are an effective way to build in-demand skills and to attract people to science, technology, engineering, and mathematics fields, such as industrial engineering/operations research. However, the traditional apprenticeship model of an undergraduate research experience limits the number of … Read more
We revisit the classical Lucky Ticket (LT) enumeration problem, in which an even-digit number is called lucky if the sum of the digits of its first half equals to that of its second half. We introduce two new subclasses — SuperLucky Tickets (SLTs), where all digits are distinct, and LuckyPrime Tickets (LPTs), where all digits … Read more
The prediction-correction framework developed in [B. He, Splitting Contraction Algorithm for Convex Optimization, Science Press, 2025] is a simple yet powerful tool for analyzing the convergence of diverse first-order optimization methods, including the Augmented Lagrangian Method (ALM) and the Alternating Direction Method of Multipliers (ADMM). In this paper, we propose a generalized prediction-correction framework featuring … Read more