Publication

@article{nokey,

title = {Leveraging Causal Machine Learning for Sustainable Automotive Industry: Analyzing Factors Influencing CO2 Emissions},

author = {Kutut Aji Prayitno and Hendro Wicaksono},

url = {https://www.sciencedirect.com/science/article/pii/S2212827124013659},

doi = {https://doi.org/10.1016/j.procir.2024.10.208},

year  = {2024},

date = {2024-11-27},

urldate = {2024-11-27},

journal = {Procedia CIRP},

volume = {130},

pages = {1070-1076},

abstract = {Efficiently managing logistics operations is crucial in elevating sustainability and tackling the challenges urbanization brings in today’s urban environment. Collaborations among the public and private sectors in urban logistics are essential to minimize environmental impacts. This study aims to create a novel conceptual framework for collaborative logistics designed explicitly for sustainable metropolitan areas. The framework aims to enable collaborative data-driven sustainability optimization in urban logistics. It comprises ontologies to facilitate interoperability among stakeholders by providing a shared understanding of the exchanged data. The framework utilizes causal artificial intelligence to enable traceability and transparency of data-driven decisions compared to conventional machine learning working based on correlations. Furthermore, the framework also employs causal reinforcement learning that enables agents to learn what actions lead to targeted outcomes and why those actions are effective. The developed framework optimizes vehicle routes and conveyance selection while considering several operational constraints such as time windows, split-load scenarios, and commodity-specific requirements. Moreover, the system integrates the distinctive features of public transport networks. The suggested strategy minimizes fuel use and overall delivery costs, promoting a more sustainable logistics environment in metropolitan areas measured using Environmental, Social, and Governance (ESG) indicators. This study contributes to the theoretical understanding of collaborative logistics. It underscores the importance of environmental stewardship and societal well-being in logistics planning and implementation by utilizing a data-driven approach.

},

keywords = {causal AI, logistics, ontologies, reinforcement learning, sustainability},

pubstate = {published},

tppubtype = {article}

}

@article{lobo2022implementation,

title = {Implementation of Blockchain Technology to Enhance Last Mile Delivery Models with Sustainability Perspectives},

author = {Carol Riona Lobo and Hendro Wicaksono and Omid Fatahi Valilai},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IFAC-PapersOnLine},

volume = {55},

number = {10},

pages = {3304–3309},

publisher = {Elsevier},

keywords = {blockchain, logistics},

pubstate = {published},

tppubtype = {article}

}