Operationalizing Experimental Design: Data Collection for Remote Ocean Monitoring

Problem definition: To collect data on ocean plastic pollution and build more accurate predictive models, we need to manually take high-resolution pictures of the sea surface via floating or flying drones. Operating these vehicles, like many data collection problems in agriculture or environmental science, challenges the traditional optimal experimental design (OED) formulation from statistics by … Read more

A Two-stage Stochastic Programming Approach for CRNA Scheduling with Handovers

We present a two-stage stochastic integer program for assigning Certified Registered Nurse Anesthetists (CRNAs) to Operating Rooms (ORs) under surgery duration uncertainty. The proposed model captures the trade-offs between CRNA staffing levels, CRNA handovers and under-staffing in the ORs. Since the stochastic program includes binary variables in both stages, we present valid inequalities to tighten … Read more

Integrated Schedule Planning for Regional Airlines Using Column Generation

Problem definition: More than one-third of US domestic flights are operated by regional airlines. This paper focuses on optimizing medium-term schedule planning decisions for a network of regional airlines through the joint optimization of frequency planning, timetable development, fleet assignment, and some limited aspects of route planning, while capturing passengers’ travel decisions through a general … Read more

A Sound Local Regret Methodology for Online Nonconvex Composite Optimization

Online nonconvex optimization addresses dynamic and complex decision-making problems arising in real-world decision-making tasks where the optimizer’s objective evolves with the intricate and changing nature of the underlying system. This paper studies an online nonconvex composite optimization model with limited first-order access, encompassing a wide range of practical scenarios. We define local regret using a … Read more

An Oracle-based Approach for Price-setting Problems in Logistics

We study a bilevel hub location problem where on the upper level, a shipment service provider –the leader–builds a transportation network and sets the prices of shipments on each possible transportation relation. Here, the leader has to take into account the customers’ reaction — the follower — who will only purchase transport services depending on … Read more

Strengthening Dual Bounds for Multicommodity Capacitated Network Design with Unsplittable Flow Constraints

Multicommodity capacitated network design (MCND) models can be used to optimize the consolidation of shipments within e-commerce fulfillment networks. In practice, fulfillment networks require that shipments with the same origin and destination follow the same transfer path. This unsplittable flow requirement complicates the MCND problem, requiring integer programming (IP) formulations with binary variables replacing continuous … Read more

New Dynamic Discretization Discovery Strategies for Continuous-Time Service Network Design

Service Network Design Problems (SNDPs) are prevalent in the freight industry. While the classic SNDP is defined on a discretized planning horizon with integral time units, the Continuous-Time SNDP (CTSNDP) uses a continuous-time horizon to avoid discretization errors. Existing CTSNDP algorithms primarily rely on the Dynamic Discretization Discovery (DDD) framework, which iteratively refines discretization and … Read more

The robust pickup and delivery problem with time windows

This study addresses the robust pickup and delivery problem with time windows (RPDPTW), in which uncertainty in demands and travel times is modelled using robust optimisation. The RPDPTW involves determining the least-cost routes to serve transportation requests from origins to destinations, while respecting vehicle capacity and time window constraints under all anticipated realisations of uncertain … Read more

Mixed Integer Linear Programming Formulations for Robust Surgery Scheduling

We introduce Mixed Integer Linear Programming (MILP) formulations for the two-stage robust surgery scheduling problem (2SRSSP). We derive these formulations by modeling the second-stage problem as a longest path problem on a layered acyclic graph and subsequently converting it into a linear program. This linear program is then dualized and integrated with the first-stage, resulting … Read more

Multiple Kernel Learning-Aided Column-and-Constraint Generation Method

Two-stage robust optimization (two-stage RO), due to its ability to balance robustness and flexibility, has been widely used in various fields for decision-making under uncertainty. This paper proposes a multiple kernel learning (MKL)-aided column-and-constraint generation (CCG) method to address this issue in the context of data-driven decision optimization, and releases a corresponding registered Julia package, … Read more