Inverse Optimization for Routing Problems

We propose a method for learning decision-makers’ behavior in routing problems using Inverse Optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context … Read more

Robust Workforce Management with Crowdsourced Delivery

We investigate how crowdsourced delivery platforms with both contracted and ad-hoc couriers can effectively manage their workforce to meet delivery demands amidst uncertainties. Our objective is to minimize the hiring costs of contracted couriers and the crowdsourcing costs of ad-hoc couriers while considering the uncertain availability and behavior of the latter. Due to the complication … Read more

Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks

CitationOjha, R., Chen, W., Zhang, H., Khir, R., Erera, A. & Van Hentenryck, P. (2023). Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks.ArticleDownload View PDF

Playing Stackelberg security games in perfect formulations

Protecting critical infrastructure from intentional damage requires foreseeing the strategies of possible attackers. The problem faced by the defender of such infrastructure can be formulated as a Stackelberg security game. A defender must decide what specific targets to protect with limited resources, maximizing their expected utility (e.g., minimizing damage value) and considering that a second … Read more

Integrating Order-to-Delivery Time Sensitivity in E-Commerce Middle-Mile Consolidation Network Design

This paper proposes an approach that leverages data on customer purchasing sensitivity to quoted order-to-delivery times (ODTs) when designing middle-mile consolidation networks to maximize the profit of e-commerce retailers. Our approach integrates quoted ODT-dependent sales volume predictions into a new mixed-integer program (MIP) that simultaneously determines ODT quotes and a consolidation plan, characterized by the … Read more

The Vehicle Routing Problem with Access Restrictions

To mitigate the negative effect of freight vehicles on urban areas, many cities have implemented road accessibility restrictions, including limited traffic zones, which restrict access to specific areas during certain times of the day. Implementing these zones creates a trade-off between the delivery cost and time, even under the assumption of equal traversal time and … Read more

A Tailored Derivative Instrument to Mitigate the Price-and-Quantity Risk faced by Wind Power Companies

The intermittent nature of wind generation combined with the well-known volatility of electricity spot prices expose Wind Power Companies (WPCs) committed to long-term forward contracts to the so-called price-and-quantity risk. Several instruments were designed in the past years to mitigate this risk exposure. However, most of them were mainly constructed to cope with only one … Read more

Sensitivity-based decision support for critical measures using the example of COVID-19 dynamics

We parametrize public policies in the context of the COVID-19 pandemic to evaluate the effectiveness of policies through sensitivity-based methods in order to offer insights into understanding the contributions to critical measures in retrospective. The study utilizes a group-specific SEIR model with a tracing and isolation strategy and vaccination programs. Public policies are applied to … Read more

A new upper bound of the Euclidean TSP constant

Let X1, X2, . . . , Xn be n independent and uniformly distributed random points in a compact region R ⊂ R2 of area 1. Let TSP(X1, . . . , Xn) denote the length of the optimal Euclidean traveling salesman tour that traverses all these points. The classical Beardwood-Halton-Hammersley theorem proves the existence … Read more