Applications – OR and Management Sciences – Page 3

Nested Benders Decomposition for Large-Scale Multi-Follower Bilevel Optimization

Published: 2026/06/01

Bilevel Optimization, Parallel Algorithms, Transportation benders decomposition, bilevel optimization, mixed-integer programming, parallel computing, Public transportation

We propose a scalable nested Benders decomposition (BD) framework for single-leader, multi-follower bilevel optimization problems. The proposed framework is applicable to bilevel optimization problems in which each follower solves a linear program and is particularly well suited for instances involving a large number of followers. By identifying the upper-level decisions as complicating variables, the method … Read more

Objective Domain Reduction for Enhancing Solver Performance on Challenging Integer Programs

Published: 2026/05/29, Updated: 2026/07/01

Combinatorial Optimization, Integer Programming, Transportation Binary Search, Branch-and-Bound, Domain-Reduced Reformulation, integer program, Objective domain reduction

In this study, we explore how the domain of objective function values for challenging integer programs can be reduced and whether such a reduction can improve the solution process. Our work is motivated by binary search, a technique that efficiently narrows a search space by repeatedly halving it through feasibility checks. Building on this idea, … Read more

Optimality Gap of Tailored Base-Surge Policies Decays Exponentially in Regular-Source Lead Times for Dual-Sourcing Models

Published: 2026/05/17, Updated: 2026/05/22

Dynamic Programming, Stochastic Programming, Supply Chain Management asymptotic optimality, dual-sourcing, exponential decay, inventory, lead time, tailored base-surge policy (tbs)

This paper resolves an open problem posed in the literature by proving that, in dual-sourcing inventory systems, the optimality gap of tailored base-surge (TBS) policies decays exponentially with the regular source lead time, with the express-source lead time fixed. In contrast to the existing approach, which relies on conditional Jensen inequalities and a vanishing-discount argument … Read more

Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach

Published: 2026/05/13, Updated: 2026/06/16

Applications - OR and Management Sciences, Applications - Science and Engineering, Robust Optimization betweenness, Data-Driven Robust Optimization, group betweenness centrality, markov chain, markov chain approximation, markov chain monte carlo, markov chain sample average approximation, stochastic network design

We propose a betweenness centrality measure and algorithms for stochastic networks, where edges can fail and weights vary across realizations, making the most central node random. Our approach models the sequence of reported central nodes as an absorbing Markov chain and measures node importance by the share of pre-absorption time spent at each node. This … Read more

Supervised feature selection via multiobjective programming and its application in the medical field

Published: 2026/05/06

Pham Thi Hoai

Applications - OR and Management Sciences, Constrained Nonlinear Optimization, Convex and Nonsmooth Optimization, Convex Optimization, Other Topics constrained multiobjective optimization problems, convex multiobjective programming, multiobjective programming, objective normalization, Supervised feature selection

In this study, we model the supervised feature selection problem using a novel approach: convex bi-objective optimization. Traditional methods have addressed this problem by maximizing relevance to class labels and minimizing redundancy among features. Recently, Wang et al. [30] formulated this problem as a single-objective convex optimization, yielding only a unique solution. Unlike that, we … Read more

The Distributionally Robust Cyclic Inventory Routing Problem

Published: 2026/05/05, Updated: 2026/05/06

Production and Logistics, Supply Chain Management, Transportation branch-and-price, chance constraints, cyclic inventory routing, distributionally robust optimization

We study the cyclic inventory routing problem that involves joint decisions on vehicle routing and inventory replenishment on an infinite, cyclic horizon. It considers a single warehouse and a set of geographically dispersed retailers. We model retailer demand as random variables with uncertain distributions belonging to a moment-based ambiguity set. We develop a distributionally robust … Read more

Finite-Sample Optimality and Constraint Satisfaction: Learning-Based Optimal Control in Dynamic Dispatch Networks

Published: 2026/04/30

Moustafa Rashidy

Data Science Theory, Production and Logistics, Transportation constrained optimization, nonlinear optimization, stochastic programming

Dynamic dispatch networks in logistics and transportation require real-time, constraint-aware decision-making under stochastic demand. This paper bridges mathematical optimization, optimal control theory, and reinforcement learning by establishing non-asymptotic theoretical guarantees for learning-based optimal control in constrained stochastic dispatch systems. We formulate the problem as a constrained Markov decision process, enforce feasibility via a projection-based policy … Read more

Rethinking Last-Mile Routing at Scale: Near-Linear Planning on Commodity Hardware

Published: 2026/04/28, Updated: 2026/06/03

Martin Vizzolini

Optimization Software and Modeling Systems, Production and Logistics, Supply Chain Management Amazon Last Mile Routing Challenge, capacitated vehicle routing, challenge, delivery, fleet optimization, fleet planning, global optimization, large-scale optimization, last-mile delivery, metaheuristics, optimization, parallel algorithms, vehicle routing problem, vrp, vrp with pickups and deliveries

This paper presents a practical architecture for last-mile delivery routing at scales reaching one million stops under realistic operational conditions, including vehicle capacity, package volume, route stop limits, and time windows. Unlike conventional systems that require pre-partitioning or large-scale infrastructure, the proposed system addresses the full fleet planning problem through a single coherent planning pipeline … Read more

Reverse stress testing for supply chains

Published: 2026/04/21, Updated: 2026/04/30

Applications - OR and Management Sciences, Integer Programming, Supply Chain Management optimization, risk identification, stress testing, supply chain resilience

This study introduces reverse stress testing for supply chains, designed to identify the minimal deviations from normal operations that would drive supply chains to a predefined performance failure. First, we present a framework for reverse stress testing with the purpose of assessing supply chain vulnerabilities. The framework involves six steps: selecting risk variables, defining baselines, … Read more

Pricing Discrete and Nonlinear Markets With Semidefinite Relaxations

Published: 2026/04/18

(Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Semi-definite Programming electricity market, pricing, semidefinite programming, strong duality, unit commitment

Nonconvexities in markets with discrete decisions and nonlinear constraints make efficient pricing challenging, often necessitating subsidies. A prime example is the unit commitment (UC) problem in electricity markets, where costly subsidies are commonly required. We propose a new pricing scheme for nonconvex markets with both discreteness and nonlinearity, by convexifying nonconvex structures through a semidefinite … Read more