The clique problem with multiple-choice constraints (CPMC) represents a very common substructure in many real-world applications, for example scheduling problems with precedence constraints. It consists in finding a clique in a graph whose nodes are partitioned into subsets, such that exactly one node from each subset is chosen. Even though we can show that (CPMC) … Read more
We study network models where flows cannot be split or merged when passing through certain nodes, i.e., for such nodes, each incoming arc flow must be matched to an outgoing arc flow of identical value. This requirement, which we call “no-split no-merge” (NSNM), appears in railroad applications where train compositions can only be modified at … Read more
In recent years, there has been a growing interest in mathematical mod- els leading to the minimization, in a symmetric matrix space, of a Bregman di- vergence coupled with a regularization term. We address problems of this type within a general framework where the regularization term is split in two parts, one being a spectral … Read more
Extreme events such as disasters cause partial or total disruption of basic services such as water, energy, communication and transportation. In particular, roads can be damaged or blocked by debris, thereby obstructing access to certain affected areas. Thus, restoration of the damaged roads is necessary to evacuate victims and distribute emergency commodities to relief centers … Read more
Wasserstein distributionally robust optimization (DRO) has recently achieved empirical success for various applications in operations research and machine learning, owing partly to its regularization effect. Although the connection between Wasserstein DRO and regularization has been established in several settings, existing results often require restrictive assumptions, such as smoothness or convexity, that are not satisfied by … Read more
Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is to predict, then optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predictions will be used in a downstream optimization … Read more
To explore one impact of the information available by adding sensors in a classical production planning setting, we consider a continuous time infinite horizon lot-sizing model where a single product is manufactured on a single machine. Each time manufacturing restarts, a random production rate is realized, and production continues at this rate until the machine … Read more
We consider a continuous time Principal-Agent model on a finite time horizon, where we look for the existence of an optimal contract both parties agreed on. Contrary to the main stream, where the principal is modelled as risk-neutral, we assume that both the principal and the agent have exponential utility, and are risk averse with … Read more
The Traveling Salesman Problem with Pickup and Delivery (TSPPD) describes the problem of finding a minimum cost path in which pickups precede their associated deliveries. The TSPPD is particularly important in the growing field of Dynamic Pickup and Delivery Problems (DPDP). These include the many-to-many Dial-A-Ride Problems (DARP) of companies such as Uber and Lyft, … Read more
The Vehicle Routing Problem with Probabilistic Customers (VRP-PC) is a fundamental building block within the broad family of stochastic routing models, and has two decision stages. In the first stage, a dispatcher determines a set of vehicle routes serving all potential customer locations, before actual requests for service realize. In the second stage, vehicles are … Read more