Model Masalah Lokasi Fasilitas Dinamis di Wilayah Pasca Bencana dalam Ketidakpastian Menggunakan Pendekatan Deep Learning

Abstract

Disaster logistics management is vital in planning and organizing humanitarian assistance distribution. The planning problem is facing challenges, such as coordinating the allocation and distribution of essential resources while considering the severity of the disaster, population density, and accessibility This study proposes an optimized disaster relief management model, including distribution center placement, demand point prediction, prohibited route mapping, and efficient relief goods distribution. A dynamic model predicts the location of distribution centers post-disaster using the K-Means method based on impacted demand points’ positions. Artificial Neural Networks (ANN) aid in predicting assistance requests around formed distribution centers. The Forbidden Route model maps permitted and prohibited routes, considering constraints to enhance relief supply distribution efficacy. The objective function aims to minimize both cost and time in post-disaster aid distribution. The Model Deep Location Routing Problem (DLRP) effectively handles mixed nonlinear multi-objective programming, choosing the best forbidden routes. The combination of these models provides a comprehensive framework for optimizing disaster relief management, resulting in more effective and responsive disaster handling. Numerical examples show the model’s effectiveness in solving complex humanitarian logistics problems with lower computation time, crucial for quick decision-making during disasters.