Paper Title
Advanced Multi-Objective Truck Dispatch Optimization Using Fractional Calculus and Tensor Algebra: A Surface Mining Application

Abstract
Efficient truck dispatching is a critical factor in surface mining operations, directly affecting productivity, cost, and equipment utilization. Traditional dispatch systems rely on static rules or operator experience, often leading to suboptimal truck–shovel matching, increased queuing time, and uneven equipment utilization. This paper proposes an intelligent multi-objective optimization model for truck dispatching in surface mines using a hybrid approach combining fractional calculus-based dynamic adjustment and tensor-based system representation. The model simultaneously minimizes truck waiting time, shovel idle time, and fuel consumption while maximizing fleet utilization and production rate. Real operational mine data is structured into a tensor framework representing time, location, equipment state, and haul cycle variability. A dynamic optimization algorithm is then applied to continuously update dispatch decisions in real time. Simulation results demonstrate that the proposed model improves truck utilization by 12–18%, reduces queue time by 20–28%, and increases overall system productivity by 10–15% compared to conventional dispatch rules. The proposed system provides a scalable and adaptive solution for modern surface mining operations. Keywords - Truck Dispatching, Surface Mining, Optimization, Fractional Calculus, Tensor Model, Fleet Management