Research Project: A New Perspective on City Logistics: Concepts, Theory, and Models for Designing and Managing Logistics as a Service
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Contributors
Funders
ID
EC.00179
Authors
Yıldız, Barış
Faculty Member
Publications
Express shipments with autonomous robots and public transportation
(Elsevier, 2024-12-01) Yıldız, Barış; Salman, Fatma Sibel; Ermağan, Umut; Department of Industrial Engineering; Yes; College of Engineering
Growing urbanization, exploding e-commerce, heightened customer expectations, and the need to reduce the environmental impact of transportation ask for innovative last-mile delivery solutions. This paper explores a new express shipment model that combines public transportation with Autonomous Robots (ARs) and studies its real-time management. Under dynamic demand arrivals with short delivery time promises, we propose a rolling horizon framework and devise a machine learning-enhanced Column Generation (CG) methodology to solve the real-time AR dispatching problem. The results of our numerical experiments with real-world delivery demand data show the significant potential of the proposed system to reduce travel time, vehicle traffic, emissions, and noise. Our results also reveal the efficacy of the learning-based CG methodology, which provides almost the same quality solutions as the classical CG approach with much less computational effort.
A machine learning approach for marginal fulfillment cost estimation in last mile delivery
(Elsevier, 2025) Nalbant, Ali; Yıldız, Barış; Graduate School of Sciences and Engineering; Department of Industrial Engineering; Yes; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; College of Engineering
Determining marginal fulfillment costs (MFC) is crucial for effective decision-making in online grocery retail, a sector struggling with small profit margins and arduous service requirements of attended home deliveries. Paramount to improving operational efficiency, e-grocers need accurate real-time MFC estimations to optimize their service offers and prices for online customers. Traditional methods for estimating MFC are either too slow for online decision-making or inaccurate. This paper introduces a novel machine learning (ML) approach that provides fast and accurate MFC estimations with the help of carefully engineered features (predictors) that can capture complex routing dynamics. Experiments with real-world data demonstrate the superiority of the proposed approach over state-of-the-art MFC estimation methods. Our analysis of more than 2000 potential predictors, from which 20 are curated for practical applicability, reveals critical insights into the use of network-level, neighborhood-based, and node-level features in capturing complex VRP dynamics to develop ML-based approaches to address problems that arise in different transportation applications.
