Robust optimization and stochastic optimization are the two main paradigms for dealing with
the uncertainty inherent in almost all real-world optimization problems. The core principle of …

<? show [AQ ID= GQ2 POS=-24pt]?><? show [AQ ID= GQ5 POS= 12pt]?> Consider a single
server that should serve a given number of customers during a fixed period. The …

In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm
tailored for solving a certain class of non-convex distributionally robust optimisation …

Problem definition: We consider the intraday scheduling problem in a group of orthopaedic
clinics where the planner schedules appointment times, given a sequence of appointments …

We study the bundle size pricing (BSP) problem where a monopolist sells bundles of
products to customers, and the price of each bundle depends only on the size (number of …

Motivated by the worldwide Covid-19 vaccine procurement, we study an inventory problem
with an advance purchase contract which requires all ordering decisions to be committed at …

Problem definition: General upgrading is a strategy by which a firm can upgrade a customer
with any higher-end product whenever a lower-end product is out-of-stock. In this paper, we …

Recently, Bomze et al. introduced a sparse conic relaxation of the scenario problem of a two
stage stochastic version of the standard quadratic optimization problem. When compared …

Abstract The Appointment Scheduling Problem (ASP) involves scheduling a finite number of
customers with uncertain service times, served consecutively by a single server, aiming to …

Product bundling is an important selling strategy for multiproduct firms. In this thesis, we
study the bundle design and pricing problems. In particular, we focus on simple bundling …