Treffer: Developing a green multi-modal dry port-seaport logistics network enhanced by the internet of things and machine learning.

• Investigating the viable DPS network considering IoT by reducing the medicines expiring. • The application of ARIMA and STL model to forecast import demand (cancer medicines) in DPS network. • The research problem is investigated under uncertainty (Fuzzy Logic). Dry Port-Seaport (DPS) facilities play a crucial role in driving international trade and streamlining logistics networks. These facilities efficiently handle substantial quantities of inbound and outbound goods, catering to the ever-increasing global demand for a wide range of products. DPS facilities enhance if international trade and economic development by ensuring the smooth movement of goods through efficient logistical infrastructure. This study is among the first studies that suggest a fuzzy Bi-Objective Mixed-Integer Programming (BOMIP) model to set up a data-driven DPS network while considering resilience, sustainability, and agility. The problem under consideration encompasses several real-world factors, including Multi-Modal Transportation (MMT), Cap-and-Trade (CAPT) policy, demand forecasting using Machine Learning (ML), and the implementation of an Internet of Things (IoT)-based system for waste management. To mitigate the risk of uncertainty, a fuzzy optimization method is used. Among the different Time-Series (TSs) forecasting models analyzed, the SeasonalTrend decomposition procedure based on LOESS (Locally Estimated Scatterplot Smoothing) (STL) model is ultimately selected to forecast import demand. The study culminates in a series of sensitivity analyses performed on the integrated locationallocation-distribution model to offer valuable managerial insights. The numerical findings underscore the negative impact of heightened demand on both resilience and sustainability metrics. In response to this challenge, the study proposes recommendations for the MMT and CAPT policy interventions. Additionally, the exploration of IoT deployment for efficiency enhancement indicates the potential to reduce waste by up to 25%, albeit with associated setup expenses. Ultimately, while initial costs may be incurred, the adoption of enhanced agility strategies is shown to yield considerable long-term advantages, including increased trade volumes and profitability. [ABSTRACT FROM AUTHOR]

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