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In the supply chain domain, ITLS leads research in three interlinked areas: (1) sustainable and resilient supply chain design and planning, addressing network configuration, environmental policy trade-offs, and supply chain planning under disruption; (2) behavioural operations, exploring how human judgement and organisational behaviour shape supply chain decisions; and (3) AI and technology for supply chain transformation, developing data-driven, human-centred decision-support tools and scalable optimisation methods.

These three areas are interconnected and collectively define a holistic view of modern supply chain management. For example, sustainability and resilience require technological enablers; AI adoption must account for human behaviour and bias; and behavioural insights improve how decision-makers respond to sustainability and disruption challenges. The overarching goal of our research is to create human-centred, data-driven, and future-ready supply chains that are capable of achieving both operational excellence and societal value. To achieve this, our research combines quantitative modelling, behavioural experiments, field studies, and industry collaborations to deliver research that improves efficiency and environmental performance, enhances resilience, and supports better managerial decisions.

1. Sustainable and Resilient Supply Chain Design and Planning

Our research in this area develops models and practical methods for designing supply chains and logistics networks that balance economic targets with environmental performance and resilience goals. Projects typically address strategic and tactical problems including network configuration, inventory and sourcing policy under uncertainty, sustainability trade-offs, sustainability reporting, closed-loop and circular supply chains, and the design of systems robust to major disruptions.

Typical methods and tools

• Multi-objective optimisation and robust/stochastic programming to trade off cost, emissions and resilience.
• Scenario analysis and sensitivity testing to evaluate policy instruments (carbon tax, emissions trading) and disruption scenarios.
• Network design models for competitive and closed-loop systems.
• Case studies and simulation to evaluate practical implementation in sourcing strategies, retail operations, and last-mile logistics.

Representative projects and recent outputs

• Containerised parcel delivery modelling and performance evaluation (Eskandarzadeh & Fahimnia, 2024).
• Green supply chain network design under stochastic demand and carbon price (Rezaee et al., 2017).
• Trade-off models for green supply chain planning and optimisation (Fahimnia, Sarkis & Eshragh, 2015).
• Robust network design under disruption risk (Jabbarzade, Fahimnia & Sabouhi, 2018; Zokaee et al., 2017).
• Conceptual frameworks for greening logistics and integrating sustainability-resilience objectives (Fahimnia, Sarkis & Talluri, 2019; Pournader et al., 2022; Davarzani et al., 2016; Fahimnia & Jabbarzadeh, 2016).

2. Behavioural Operations and Supply Chains — the Human Factor

This stream of our research examines how human judgement, cognitive biases, and organisational behaviour shape supply chain decisions and outcomes. Rather than treating decisions and outcomes as purely technical, we investigate how mental anchors, heuristics, intuition affect ordering, inventory, pricing and coordination decisions. A class of research in this domain focus on designing interventions and decision-support methods that lead to better real-world outcomes.

Typical methods and tools

• Behavioural experiments (lab and field) to reveal systematic deviations from normative models, and understand the mechanism s beyond anchors and biases.
• Survey and interview-based studies to capture managerial attitudes, trade-offs and adoption barriers.
• Integration of behavioural findings into operational models and policy recommendations.

Representative projects and recent outputs

• Anchoring effects in judgemental demand forecasting (Fahimnia et al., 2023; Fahimnia, et al., 2025).
• Behavioural experiments on inventory and ordering decision-making under uncertainty (Perera, Fahimnia & Travis, 2020; Perera & Fahimnia, 2024).
• Human judgement in supply chain forecasting: conceptual and empirical studies (Fahimnia, Sanders & Siemsen, 2020; Perera et al., 2019).
• Conceptual frameworks on behavioural operations in sustainable supply chain management (Fahimnia, et al., 2019; Pournader et al., 2022).

3. AI and Technology Applications for Supply Chain Transformation

Our research in this area focuses on how digital technologies, AI and data governance reshape supply chain decision-making and operational performance. Research spans generative and predictive AI, digital twins, data quality and standard operating procedures, algorithmic optimisation for planning and responsiveness, and methodological advances that combine optimisation with scalable computational methods for complex networks.

Typical methods and tools

• Machine learning and generative AI for forecasting, anomaly detection and scenario generation.
• Heuristic and metaheuristic algorithms for supply chain design and planning under uncertainty.
• Empirical field studies on data practices and standard operating procedures to improve data quality and downstream analytics.
• Hybrid methods that embed behavioural insights into AI-driven decision-support systems.

Representative projects and recent outputs

• Generative AI opportunities and resilience research agenda (Boone et al., 2025).
• SOP adherence and data quality studies in postal services and last-mile delivery (Eskandarzadeh, et al., 2023).
• Surveys and frameworks on AI applications in supply chain management (Pournader et al., 2022).
• Applied optimisation and AI-driven models for multi-objective network design (Fahimnia et al., 2020; Fahimnia et al., 2018; Reisi, Gabriel & Fahimnia, 2019).

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References

1. Sustainable and Resilient Supply Chain Design and Planning

• Eskandarzadeh, S, & Fahimnia, B (2024). Containerised Parcel Delivery: Modelling and Performance Evaluation. Transportation Research (Part E), 186, 103519.
• Pournader, M, Sauer, P, & Fahimnia, B, & Seuring, S (2022), ‘Behavioural studies in sustainable supply chain management’, International Journal of Production Economics, 243, 108344.
• Fahimnia, B, Sarkis, J, & Talluri, S (2019), ‘Design and Management of Sustainable and Resilient Supply Chains’, IEEE Transactions on Engineering Management, 66(1), 2-7.
• Rezaee, A, Dehghanian, F, Fahimnia, B, & Beamon, B (2017), ‘Green supply chain network design with stochastic demand and carbon price’, Annals of Operations Research, 250(2), 463-485.
• Zokaee, S, Jabbarzadeh, A, Fahimnia, B, & Sadjadi, SJ (2017), ‘Robust supply chain network design: An optimization model with real world application’, Annals of Operations Research, 257(1-2), 15-44.
• Fahimnia, B, Sarkis, J, & Srinivas, T (2016), ‘Design and Management of Sustainable and Resilient Supply Chains: Editorial Note’, IEEE Transactions on Engineering Management, 63(3), 7514350.
• Jabbarzadeh, A, Fahimnia, B, Sheu, JB, & Moghadam, H (2016), ‘Designing a Supply Chain Resilient to Major Disruptions and Supply/Demand Interruptions’, Transportation Research: Part B, 94, 121-149.
• Fahimnia, B, & Jabbarzadeh, A (2016), ‘Marrying supply chain sustainability and resilience: A match made in heaven?’, Transportation Research: Part E, 91, 306-324.
• Davarzani, H, Fahimnia, B, Bell, M, & Sarkis, J (2016), ‘Greening port and maritime logistics: A review and network analysis’, Transportation Research: Part D, 48, 473-487.
• Fahimnia, B, Tang, C, Davarzani H, & Sarkis, J (2015), ‘Quantitative models for managing supply chain risks: A review’, European Journal of Operational Research, 247(1), 1-15.
• Zakeri, A, Dehghanian, F, Fahimnia, B, & Sarkis, J (2015), ‘Carbon pricing versus emissions trading: A supply chain planning perspective’, International Journal of Production Economics, 164, 197-205.
• Fahimnia, B, Sarkis, J, & Eshragh, A (2015), ‘A tradeoff model for green supply chain planning: A leanness-versus-greenness analysis’, OMEGA: An International Journal of Management Science, 54, 173-190.
• Fahimnia, B, Sarkis, J, & Davarzani, H (2015), ‘Green supply chain management’, International Journal of Production Economics, 162, 101-114.
• Rezapour, S, Farahani, RZ, Fahimnia, B, Govindan, K, & Mansouri, Y (2015), ‘Competitive closed-loop supply chain network design with price-dependent demands’, Journal of Cleaner Production, 93, 251-272.
• Fahimnia, B, Sarkis, J, Choudhary, A, & Eshragh, A (2015), ‘Tactical supply chain planning under a carbon tax policy scheme: A case study’, International Journal of Production Economics, 164, 206-215.
• Jabbarzadeh, A, Fahimnia, B, & Rastegar, S (2019) ‘Green and Resilient Design of Electricity Supply Chain Networks: A Multi-objective Robust Optimization Approach’, IEEE Transactions on Engineering Management, 66(1), 52-72.
• Fahimnia, B, Jabbarzadeh, A, & Sarkis, J (2018), ‘Greening versus resilience: A supply chain design perspective’, Transportation Research (Part E), 119, 129-148.
• Jabbarzade, A, Fahimnia, B, & Sabouhi, F (2018), ‘Resilient and sustainable supply chain design: Sustainability analysis under disruption risks’, International Journal of Production Research, 56(17), 5945-5968.

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2. Behavioural Operations and Supply Chains — the Human Factor

• Fahimnia, B, Arvan, M, Tan, T, & Siemsen, E (2023) ‘A Hidden Anchor: The Influence of Service Levels on Demand Forecasts’, Journal of Operations Management, 69(5), 856-871.
• Fahimnia, B, Collins, A, & Moritz, B. (2025). Managing paradoxical trade-offs: Sustainability and diversification strategies of supply managers. Transportation Research (Part E), 203, 104279.
• Fahimnia, B, Tan, T, & Tahirov, N (2025) ‘Service-level anchoring in demand forecasting: The moderating impact of retail promotions and product perishability’, International Journal of Forecasting, 41 (2), 554-570.
• Perera, N, & Fahimnia, B (2024). ‘Multi-period ordering decisions in the presence of retail promotions’. European Journal of Operational Research, 319(3), 763-776.
• Fahimnia, B, Sanders, N, & Siemsen, E (2020), ‘Human Judgment in Supply Chain Forecasting’, OMEGA: An International Journal of Management Science, 94, 102-104.
• Perera, N, Fahimnia, B, & Travis, T (2020), ‘Behavioral experiments on inventory and ordering decisions: A systematic review’, International Journal of Operations & Production Management, 40(7/8), 997-1039.
• Fahimnia, B, Siemsen, E, Bendoly, E, & Pournader M (2019), ‘Behavioral Operations and Supply Chain Management’, Decision Sciences, 50(6), 1127-1183.
• Perera, N, Hurley, J, Fahimnia, B, & Reisi, M (2019), ‘Human Factor in Supply Chain Forecasting: A Systematic Review’, European Journal of Operational Research, 274(2), 574-600.
• Arvan, M, Fahimnia, B, Reisi, M, & Siemsen, E (2019), ‘Integrating Human Judgement into Quantitative Forecasting Methods: A Review’, OMEGA: An International Journal of Management Science, 86 (2), 237-252.

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3. AI and Technology Applications for Supply Chain Transformation

• Boone, T, Fahimnia, B, Ganeshan, R, Herold, D, & Sanders, N. (2025). Generative AI: Opportunities, challenges, and research directions for supply chain resilience. Transportation Research (Part E), 199, 104135.
• Eskandarzadeh, S, Fahimnia, B, & Hoberg, K (2023) ‘Adherence to Standard Operating Procedures for Improving Data Quality: An Empirical Analysis in the Postal Service Industry’, Transportation Research (Part E), 176, 103178.
• Pournader, M, Ghaderi, H, Hassanzadegan, A, & Fahimnia, B (2022), ‘Artificial intelligence applications in supply chain management’, International Journal of Production Economics, 241, 108250.
• Esmaeilikia, M, Fahimnia, B, Sarkis, J, Govindan, K, Kumar, A, & Mo, J (2016), ‘A tactical supply chain planning model with multiple flexibility options: an empirical evaluation’, Annals of Operations Research, 244(2), 429-4454.
• Fahimnia, B, Sabouhi, F, Jabalameli, MS, &Jabbarzadeh, A. (2020), ‘A multi-cut L-shaped method for resilient and responsive supply chain network design’, International Journal of Production Research, 58(24), 7353-7381.
• Reisi, M, Gabriel, S, & Fahimnia, B (2019), ‘Supply chain competition on shelf space and pricing for soft drinks: A bilevel optimization approach’, International Journal of Production Economics, 211, 237-250.
• Fahimnia, B, Davarzani, H, & Eshragh, A (2018), ‘Planning of complex supply chains: A Performance comparison of three meta-heuristic algorithms’, Computers and Operations Research, 89, 241-252.

The synergies created by combining city logistics with urban public transport, referred to as co-modality, are potentially enormous. As planning, tracking and managing passengers and freight becomes more sophisticated and transparent, the opportunities to leverage environmental and efficiency benefits from a co-modal approach are increasingly apparent. The diversion of some freight to public transport can reduce the number of trucks and vans in circulation, cutting both congestion and emissions. Led by Professor Michael Bell and partnering with Transport for NSW and the iMOVE CRC, our project, Investigating the Feasibility of Adopting Co-

Modality, aims to investigate the potential for (1) moving freight within urban areas using latent capacity in public transport without disrupting passenger services, and (2) using public transport stations to manage freight activities.

Webinar

Facilitated by Professor Michael Bell and featuring Michael Stokoe, Associate Director, Freight and Servicing at Transport for NSW and Assistant Professor Ron van Duin from TU Delft in The Netherlands. Download the webinar Q&A (pdf, 200KB).

We have partnered with the Centre of Excellence in Bus Rapid Transit (BRT) Development with the goal of developing a new framework for planning, design, financing, implementation and operation of BRT in different urban areas, giving clear guidelines to decision makers on when and how BRT projects can effectively enhance mobility and meet accessibility needs.

Implemented in Santiago, Chile, BRT is financed by the Volvo Research and Educational Foundations working as a consortium of five institutions:

• Pontificia Universidad Católica de Chile
• Massachusetts Institute of Technology
• University of Pretoria
• The University of Sydney
• World Resources Institute Ross Center for Sustainable Cities

We have partnered with Travel Choice Simulation Laboratory () to improve the capabilities of transport planning techniques by developing new methods to improve the realism of regional congestion modelling, and the mathematical representation of traveller decision-making, thereby permitting an improved long-term transport plan.

A state-of-the-art planning and evaluation capability, encompassing demand forecasts, cost-benefit analysis and economic impact to assess the merits of major infrastructure such as roads, airports, public transport (heavy and light rail, and bus and ferry systems), as well as precinct investments such as new housing and industry and business stock.

Further information

• MetroScan-TI project (download pdf, 714KB)
• Related MetroScan publications (download pdf, 124KB)

Improving practical behavioural models to predict responses to transport policies in order to assist in better decision-making; merging methods from stated choice surveys, experimental economics, and naturalistic driving simulators in order to better investigate travel choice behaviour in realistic environments.

Our work in the active travel and micromobility space focuses on understanding and promoting travel behaviours that involve physical activity, such as walking and cycling. Our research explores how to encourage these behaviors through various strategies, including infrastructure improvements, policy changes, and understanding the factors that influence people's choices about how they travel.

Projects

Developing a pilot methodology for collecting activity data on road use by cyclists, pedestrians and personal mobility devices

Working with iMOVE CRC and the Department of Infrastructure, Transport, Regional Development, Communications and the Arts, this project designs, tests, and validates a pilot methodology for harvesting activity data of active road users – pedestrians, cyclists, and other personal mobility devices. It draws on experience in processing and analysing massive geo-spatial temporal data generated by users of smartphone devices. .

NSW E-Scooter Shared Scheme trial evaluation (Transport for NSW)

ITLS have developed a comprehensive Evaluation Plan for the shared e-scooter trial program focussed on a representative set of three councils, achieving a mix of metropolitan and regional contexts, different use cases, different e-scooter providers, available infrastructure and availability of public transport services. Read the final report.

Healthy, Equitable and Sustainable Urban Mobility (ARC Linkage with Transport for NSW)

This project explores the role that interventions to promote sustainable travel choices can play in promoting a shift towards transport practices that are, city-wide, more sustainable, and healthier. .