Who Has Access to E-Commerce and When? Time-Varying Service Regions in Same-Day Delivery

Motivated by access and equity issues in e-commerce, we study the design of same-day delivery (SDD) systems under the assumption that service regions are allowed to vary over the course of the day; equivalently, that customers in different locations may have access to SDD for different lengths of time over the service day or may … Read more

Fleet & tail assignment under uncertainty

Airlines solve many different optimization problems and combine the resulting solutions to ensure smooth, minimum-cost operations. Crucial problems are the Fleet Assignment, which assigns aircraft types to flights of a given schedule, and the Tail Assignment, which determines individual flight sequences to be performed by single aircraft. In order to find a cost-optimal solution, many … Read more

The impact of passive social media viewers in influence maximization

A frequently studied problem in the context of digital marketing for online social networks is the influence maximization problem that seeks for an initial seed set of influencers to trigger an information propagation cascade (in terms of active message forwarders) of expected maximum impact. Previously studied problems typically neglect that the probability that individuals passively … Read more

Dynamic courier capacity acquisition in rapid delivery systems: a deep Q-learning approach

With the recent boom of the gig economy, urban delivery systems have experienced substantial demand growth. In such systems, orders are delivered to customers from local distribution points respecting a delivery time promise. An important example is a restaurant meal delivery system, where delivery times are expected to be minutes after an order is placed. … Read more

Optimization-based Scenario Reduction for Data-Driven Two-stage Stochastic Optimization

We propose a novel, optimization-based method that takes into account the objective and problem structure for reducing the number of scenarios, m, needed for solving two-stage stochastic optimization problems. We develop a corresponding convex optimization-based algorithm, and show that as the number of scenarios increase, the proposed method recovers the SAA solution. We report computational … Read more

A prediction-based approach for online dynamic radiotherapy scheduling

Patient scheduling is a difficult task as it involves dealing with stochastic factors such as an unknown arrival flow of patients. Scheduling radiotherapy treatments for cancer patients faces a similar problem. Curative patients need to start their treatment within the recommended deadlines, i.e., 14 or 28 days after their admission while reserving treatment capacity for … Read more

An Efficient Tabu Search Algorithm for the Tool Indexing Problem

In this paper, we look at the tool indexing problem in which a single copy of each tool is allowed in the tool magazine. We develop problem specific methods to search the neighborhood efficiently and design a Tabu Search algorithm based on them. Computational experiments show that our algorithm is competent. CitationIndian Institute of Management … Read more

Pattern-based ILP models for the one-dimensional cutting stock problem with setup cost

The One-Dimensional Cutting Stock Problem with Setup Cost (CSP-S) is a cutting problem that seeks a cutting plan with a minimum number of objects and a minimum number of different patterns. This problem gains relevance in manufacturing settings, where time consuming operations to set up the knives of the cutting machine for the new patterns … Read more

Distributional robustness and inequity mitigation in disaster preparedness of humanitarian operations

We study a predisaster relief network design problem with uncertain demands. The aim is to determine the prepositioning and reallocation of relief supplies. Motivated by the call of the International Federation of Red Cross and Red Crescent Societies (IFRC) to leave no one behind, we consider three important practical aspects of humanitarian operations: shortages, equity, … Read more

Optimal Robust Policy for Feature-Based Newsvendor

We study policy optimization for the feature-based newsvendor, which seeks an end-to-end policy that renders an explicit mapping from features to ordering decisions. Unlike existing works that restrict the policies to some parametric class which may suffer from sub-optimality (such as affine class) or lack of interpretability (such as neural networks), we aim to optimize … Read more