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Sustainable Supply Chain Optimization and Risk Management

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: 19 October 2024 | Viewed by 1411

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Special Issue Editors

Prof. Dr. Tadeusz Sawik

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Guest Editor

1. Department of Engineering, Reykjavik University, IS-101 Reykjavik, Iceland
2. Department of Operations Research, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: logistics and supply chain optimization; supply chain risk management; homeland and cyber security; planning and scheduling; mixed integer programming; stochastic and combinatorial optimization

Prof. Dr. Bartosz Sawik

E-Mail Website
Guest Editor

Department of Business Informatics and Engineering Management, AGH University of Krakow, Krakow 30-059, Poland; Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona 31006, Spain; Haas School of Business, University of California at Berkeley, Berkeley, CA 94720, USA.
Interests: operations engineering; multi-criteria optimization; decision sciences; green vehicle routing problems; portfolio optimization; computer science; conditional value-at-risk; logistics; supply chain; cybersecurity
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Special Issue Information

Dear Colleagues，

The purpose of sustainable supply chain management is the coordination of material, information, and capital flows across the supply chain to simultaneously meet various social, economic, and environmental, often conflicting, objectives. The complexity of the resulting multi-objective optimization of supply chain operations is evident even in a deterministic environment, let alone under uncertainty and risks. The literature on sustainable supply chains is abundant, but it is mostly devoted to qualitative approaches. The lack of computationally efficient optimization approaches and over reliance on heuristics may often lead to myopic decision making. The unknown best solutions can hardly be found and then implemented in practice. In contrast to commonly reported research, the focus of this SI is on quantitative optimization models that are capable of supporting decision making in multi-tier sustainable supply chains under risks of various types and origins. Both natural disasters, such as earthquakes, hurricanes, and floods, and man-made disruptions caused by geopolitical, economic, financial, and pandemic crises will be considered. Moreover, the application of new digital technologies such as digital twins of supply chains, blockchain, AI, and data analytics can be highlighted. The new technologies enable real-time decision making and improve risk management and the performance of sustainable supply chains.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Sustainable supply portfolio.
Supply chain resilience and viability.
The viability kernels for supply chains under disruption risks.
Integration of proactive and reactive decision making in supply chain risk management.
Risk-neutral vs risk-averse optimization of sustainable supply chains.
Multi-objective optimization of multi-tier supply chains.
Mitigation of the impact of supply chain disruption risks and the ripple effect.
Mitigation of the impact of supply chain cyber risks.
Sustainable supply chain management with blockchain technology.
Sustainable supply chain in circular economy.

References

D.Ivanov, A. Dolgui, J.V. Blackhurst and T.-M. Choi (2023): Toward supply chain viability theory: from lessons learned through COVID-19 pandemic to viable ecosystems, International Journal of Production Research, 61(8), 2402-2415. https://doi.org/10.1080/00207543.2023.2177049.

D.Ivanov (2023): Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability. International Journal of Production Economies 263, 198938. https://doi.org/10.1016/j.ijpe.2023.108938.

G.Karacaoglu and J.B. Krawczyk (2021): Public policy, systemic resilience and viability theory. Metroeconomica. 2021;00:1– 23. https://doi.org/10.1111/meca.12349.

G. Mogale, N. Cheikhrouhou and M.K. Tiwari (2020): Modelling of sustainable food grain supply chain distribution system: a bi-objective Approach. International Journal of Production Research, 58(18): 5521-5544. https://doi.org/10.1080/00207543.2019.1669840.

Sawik (2023): Reshore or not Reshore - A Stochastic Programming Approach to Supply Chain Optimization. Omega 118, 102863. https://doi.org/10.1016/j.omega.2023.102863.

Sawik (2023): A stochastic optimization approach to maintain supply chain viability under the ripple effect. International Journal of Production Research, 61(8), 2452-2469. https://doi.org/10.1080/00207543.2023.2172964.

Sawik (2022): Stochastic Optimization of Supply Chain Resilience under Ripple Effect: A COVID-19 Pandemic Related Study. Omega, 109C, 102596. https://doi.org/10.1016/j.omega.2022.102596.

Sawik and B. Sawik (2022): A rough cut cybersecurity investment using portfolio of security controls with maximum cybersecurity value. International Journal of Production Research, 60(21), 6556–6572. https://doi.org/10.1080/00207543.2021.1994166.

Sawik (2022): A linear model for optimal cybersecurity investment in Industry 4.0 supply chains. International Journal of Production Research, 60(4), 1368–1385. https://doi.org/10.1080/00207543.2020.1856442.

Sawik (2022): Balancing cybersecurity in a supply chain under direct and indirect cyber risks. International Journal of Production Research, 60(2), 766-782. https://doi.org/10.1080/00207543.2021.1914356.

Sawik (2021): On the risk-averse selection of resilient multi-tier supply portfolio. Omega, 101, 102267. https://doi.org/10.1016/j.omega.2020.102267.

L.Schilling and S.Seuring (2023): Linking the digital and sustainable transformation with supply chain practices. International Journal of Production Research, https://doi.org/10.1080/00207543.2023.2173502.

Seuring (2013): A Review of Modeling Approaches for Sustainable Supply Chain Management. Decision Support Systems 54 (4): 1513–1520. https://doi.org/10.1016/j.dss.2012.05.053.

We look forward to receiving your contributions.

Prof. Dr. Tadeusz Sawik
Prof. Dr. Bartosz Sawik
Guest Editors

Manuscript Submission Information

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Keywords

supply chain optimization
supply chain risk management
supply chain resilience and viability
sustainable supply portfolio
blockchain technology
AI and data analytics

Published Papers (1 paper)

Research

18 pages, 4718 KiB

Open AccessArticle

Ensemble Deep Learning for Automated Damage Detection of Trailers at Intermodal Terminals

by Pavel Cimili, Jana Voegl, Patrick Hirsch and Manfred Gronalt

Sustainability 2024, 16(3), 1218; https://doi.org/10.3390/su16031218 - 31 Jan 2024

Viewed by 944

Abstract

Efficient damage detection of trailers is essential for improving processes at inland intermodal terminals. This paper presents an automated damage detection (ADD) algorithm for trailers utilizing ensemble learning based on YOLOv8 and RetinaNet networks. The algorithm achieves 88.33% accuracy and an 81.08% F1-score on the real-life trailer damage dataset by leveraging the strengths of each object detection model. YOLOv8 is trained explicitly for detecting belt damage, while RetinaNet handles detecting other damage types and is used for cropping trailers from images. These one-stage detectors outperformed the two-stage Faster R-CNN in all tested tasks within this research. Furthermore, the algorithm incorporates slice-aided hyper inference, which significantly contributes to the efficient processing of high-resolution trailer images. Integrating the proposed ADD solution into terminal operating systems allows a substantial workload reduction at the ingate of intermodal terminals and supports, therefore, more sustainable transportation solutions. Full article

(This article belongs to the Special Issue Sustainable Supply Chain Optimization and Risk Management)

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