Alireza Goli is currently an assistant professor in the Department of Industrial Engineering and Future Studies at the University of Isfahan, Iran. He received his Bachelor’s and Master’s Degrees in Industrial Engineering from Golpayegan University of Technology (Iran, 2013) and Isfahan University of Technology (Iran, 2015) respectively, followed by a Ph.D. in Industrial Engineering from Yazd University (Iran, 2019).
He has published extensively in highly regarded peer-reviewed journals, international conferences, and book chapters. Dr. Goli serves on editorial boards and actively reviews for various reputable journals. His research interests include supply chain management, metaheuristic algorithms, artificial intelligence, and uncertainty. He focuses on how novel intelligent methods can support more sustainable, greener, and circular supply chains.
Waste to fuel supply chains
Data-driven optimization
Machine learning
Novel meta-heuristic algorithms
supply chain management
metaheuristic algorithms
artificial intelligence
uncertainty
Research Output
Articles
Publication Date: 2025
Communications in Computer and Information Science (18650937)2652pp. 105-115
Blockchain technology, although still considered a relatively new innovation, has rapidly emerged as a transformative and fast-growing concept in supply chain management (SCM). In parallel, social concerns, such as labor rights, ethical sourcing, and community impact, have recently gained significant attention among researchers and scholars, much like environmental concerns did previously alongside economic performance objectives. These three dimensions (economic, environmental, and social) collectively constitute the comprehensive concept of sustainability within a supply chain (SC) system. With the growing global emphasis on sustainable development, effectively integrating these three pillars into SC strategies has become essential for long-term success and resilience. This paper proposes a detailed and structured framework for implementing blockchain technology within sustainable supply chains (SCs). The framework is further enhanced by integrating mathematical modeling techniques and game theory approaches to facilitate strategic adoption. Finally, the study examines practical benefits, potential limitations, and key managerial implications through an in-depth SWOT analysis, providing actionable insights for policymakers and SC practitioners aiming to balance innovation with sustainability goals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Publication Date: 2025
Journal Of Engineering Research (23071877)
Addressing the escalating complexity and demands for resilience, sustainability, and real-time visibility in modern supply chains, this study develops and validates a novel hybrid engineering framework. This framework synergistically integrates machine learning and multi-objective optimization to design and manage smart supply chains leveraging Internet of Things (IoT) and blockchain technologies. A bi-objective mathematical model is formulated to concurrently minimize total operational costs and delivery times, while inherently supporting enhanced traceability, transparency, and environmental performance. To overcome the computational challenges posed by large-scale problem instances, a Multi-Objective Gray Wolf Optimizer (MOGWO) is engineered and applied. The framework further incorporates machine learning for precise demand forecasting and operational parameter estimation, utilizing real-time RFID and sensor data to significantly improve decision accuracy. Comprehensive computational experiments demonstrate that the proposed model achieves substantial performance improvements, reducing total costs by up to 18.7 % and delivery times by 22.4 % compared to traditional supply chain models without IoT and blockchain integration. Furthermore, blockchain integration demonstrably enhances data transparency and trust across the supply network. The results underscore the framework's efficacy in generating diverse Pareto-optimal solutions and highlight the critical engineering value of digital transformation in fostering intelligent, resilient, and sustainable supply chain systems. © 2025 The Authors.
Goli, A.,
Babaee tirkolaee, E.,
Golmohammadi, A.,
Atan, Z.,
Weber, G.,
Ali, S.S. Publication Date: 2025
Central European Journal of Operations Research (16139178)33(3)pp. 1025-1046
Supply chain network design is one of the most important issues in today’s competitive environment. Moreover, the ratio of transportation costs to the income of manufacturing companies has increased significantly. In this regard, strategic decisions, as well as tactical decisions making, are of concern for supply chain network design. In this research, a flexible, sustainable, multi-product, multi-period, and Internet-of-Things (IoT)-based supply chain network with an integrated forward/reverse logistics system is configured where the actors are suppliers, producers, distribution centers, first- and second-stage customers, repair/disassembly centers, recycling centers, and disposal centers. In order to create flexibility in this supply chain, it is possible to dispatch directly to customers from distribution centers or manufacturing plants. For direct shipping, the application IoT system is taken into account in the transportation system to make them able to manage direct and indirect delivery at the same time. The options and considerations are then incorporated into a Multi-Objective Mixed-Integer Linear Programming model to formulate the problem which is then converted into a single-objective model using Goal Programming (GP) method. Moreover, in order to deal with uncertainty in the demand parameter, robust optimization approach is applied. The obtained results from a numerical example reveal that the proposed model is able to optimally design the supply chain network whose robustness is highly dependent on the budgets of uncertainty whereas up to 213.528% increase in the GP objective function is observed. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
