Machine Learning–Enhanced Column Generation for Large-Scale Capacity Planning Problems

Capacity Planning problems are a class of optimization problems used in diverse industries to improve resource allocation and make investment decisions. Solving real-world instances of these problems typically requires significant computational effort. To tackle this, we propose machine-learning-aided column generation methods for solving large-scale capacity planning problems. Our goal is to accelerate column generation by … Read more

New Location Science Models with Applications to UAV-Based Disaster Relief

Natural and human-made disasters can cause severe devastation and claim thousands of lives worldwide. Therefore, developing efficient methods for disaster response and management is a critical task for relief teams. One of the most essential components of effective response is the rapid collection of information about affected areas, damages, and victims. More data translates into … Read more

Solution of Stochastic Facility Location Problems with Combinatorially many Decision-Dependent Distributions

This article describes a model and an exact solution method for facility location problems with decision-dependent uncertainties. The model allows characterizing the probability distribution of the random elements as a function of the choice of open facilities. This, in turn, generates a combinatorial number of potential distributions of the random elements. Though general in the … Read more

Integrated Bus Fleet Electrification Planning Through Accelerated Logic-Based Benders Decomposition and Restriction Heuristics

To meet sustainability goals and regulatory requirements, transit agencies worldwide are planning partial and complete transitions to electric bus fleets. This paper presents the first comprehensive and computationally efficient multi-period optimization framework integrating the key planning decisions necessary to support such electrification initiatives. Our model, formulated as a two-stage integer program with integer subproblems, jointly … Read more

Collection points placement in urban delivery: A game-theoretic analysis of public and competitive strategies

Collection point networks are rapidly expanding as delivery companies and public authorities promote their implementation to consolidate deliveries and reduce urban congestion. However, rather than catering to public interest by maximizing accessibility, the placement of collection points remains primarily driven by competition among delivery companies, which seek to maximize their market share. This paper thus … Read more

Algorithmic Approaches for Identifying the Trade-off between Pessimism and Optimism in a Stochastic Fixed Charge Facility Location Problem

We introduce new algorithms to identify the trade-off (TRO) between adopting a distributional belief and hedging against ambiguity when modeling uncertainty in a capacitated fixed charge facility location problem (CFLP). We first formulate a TRO model for the CFLP (TRO-CFLP), which determines the number of facilities to open by minimizing the fixed establishment cost and … Read more

Risk-aware Logic-based Benders Decomposition for a Location-Allocation-Pricing Problem with Stochastic Price-Sensitive Demands

We consider a capacitated location-allocation-pricing problem in a single-commodity supply chain with stochastic price-sensitive demands, where the location, allocation and pricing decisions are made simultaneously. Under a general risk measure representing an arbitrary risk tolerance policy, the problem is modeled as a two-stage stochastic mixed-integer program with a translation-invariant monotone risk measure. To solve the … Read more

Neural Embedded Mixed-Integer Optimization for Location-Routing Problems

We present a novel framework that combines machine learning with mixed-integer optimization to solve the Capacitated Location-Routing Problem (CLRP). The CLRP is a classical yet NP-hard problem that integrates strategic facility location with operational vehicle routing decisions, aiming to simultaneously minimize both fixed and variable costs. The proposed method first trains a permutationally invariant neural … Read more

Resilient Relay Logistics Network Design: A k-Shortest Path Approach

Problem definition: We study the problem of designing large-scale resilient relay logistics hub networks. We propose a model of k-Shortest Path Network Design, which aims to improve a network’s efficiency and resilience through its topological configuration, by locating relay logistics hubs to connect each origin-destination pair with k paths of minimum lengths, weighted by their … Read more

Improvements for Decomposition Based Methods Utilized in the Development of Multi-Scale Energy Systems

The optimal design of large-scale energy systems can be found by posing the problem as an integrated multi-period planning and scheduling mathematical programming problem. Due to the complexity of the accompanying mathematical programming problem decomposition techniques are often required but they to are plagued with converge issues. To address these issues we have derived a … Read more