A Novel Multi-Objective Optimization Approach with Flexible Operation Planning Strategy for Truck Scheduling

Article A Novel Multi-Objective Optimization Approach with Flexible Operation Planning Strategy for Truck Scheduling Yiming Wang , Weibo Liu *, Chuang Wang , Futra Fadzil , Stanislao Lauria , and Xiaohui Liu Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom * Correspondence: Weibo.Liu2@brunel.ac.uk Received: 13 April 2023 Accepted: 25 April 2023 Abstract: The transportation system plays an important role in the open-pit mine. As an effective solution, smart scheduling has been widely investigated to manage transportation operations and increase transportation capabilities. Some existing truck scheduling methods tend to treat the critical parameter (i.e., the moving speed of the truck) as a constant, which is impractical in real-world industrial scenarios. In this paper, a multi-objective optimization (MOO) algorithm is proposed for truck scheduling by considering three objectives, i.e., minimizing the queuing time, maximizing the productivity, and minimizing the financial cost. Specifically, the proposed algorithm is employed to search continuously in the solution space, where the truck moving speed and truck payload are chosen as the operational variables. Moreover, a smart scheduling application integrating the proposed MOO algorithm is developed to assist the user in selecting a suitable scheduling plan. Experimental results demonstrate that our proposed MOO approach is effective in tackling the truck scheduling problem, which could satisfy a wide range of transportation conditions and provide managers with flexible scheduling options.