A Resilient Scheduling Model Using Neuro-Evolutionary Planning for Disaster-Resistant Industries

Abstract

Unprecedented natural disasters and human-made catastrophes, maintaining business continuity now heavily depends on industrial resilience. This novel scheduling approach combines adaptive planning algorithms with neuro-evolutionary techniques to automate and strengthen industrial operations enduring excessive disruptive conditions. The model utilizes predictive analytics through neural networks and applies evolutionary strategies for real-time, environmentally driven operational and maneuverable shifting dynamic systems. Simulated disaster scenarios and case studies highlighted the model's advantages over traditional scheduling methods in optimizing resource allocation, reducing downtime, and meeting set production goals. Industries seeking to strengthen their operations against future disruptions will benefit from this robust and intelligent solution called neuro-evolutionary planning framework.