Article: Elastic dynamic scheduling algorithm for instant delivery logistics vehicles using multi-objective optimisation techniques Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.52 pp.41 - 55 Abstract: This study addresses real-time vehicle management challenges in instant delivery logistics, where conventional methods face dynamic demands causing inefficiencies and higher costs. It proposes the elastic dynamic scheduling algorithm (EDSA), integrating real-time data (e.g., delivery requests, traffic) and a multi-objective genetic algorithm optimising delivery time, operational costs, and energy consumption. EDSA dynamically adjusts routes amid disruptions like congestion or new orders. Simulations compare it with PPO-DRL, HGA, and MOPSO, showing superior performance: lower average delivery time, cost, and energy use; 12.2% higher peak delivery efficiency than PPO-DRL; and 5.6% reduced CO emissions versus HGA. This research fills gaps in existing methods via real-time adaptability and multi-objective optimisation, offering a scalable, holistic solution balancing cost, time, and environmental impact, providing actionable insights for logistics firms. Inderscience Publishers - linking academia, business and industry through research

Title: Elastic dynamic scheduling algorithm for instant delivery logistics vehicles using multi-objective optimisation techniques

Authors: Zhigang Wu; Ziyi Gao; Chunhui Li; Linze Huang; Danmin Huang

Addresses: Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, 510700, Guangdong, China ' Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, 510700, Guangdong, China ' Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, 510700, Guangdong, China ' Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, 510700, Guangdong, China ' Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, 510700, Guangdong, China

Abstract: This study addresses real-time vehicle management challenges in instant delivery logistics, where conventional methods face dynamic demands causing inefficiencies and higher costs. It proposes the elastic dynamic scheduling algorithm (EDSA), integrating real-time data (e.g., delivery requests, traffic) and a multi-objective genetic algorithm optimising delivery time, operational costs, and energy consumption. EDSA dynamically adjusts routes amid disruptions like congestion or new orders. Simulations compare it with PPO-DRL, HGA, and MOPSO, showing superior performance: lower average delivery time, cost, and energy use; 12.2% higher peak delivery efficiency than PPO-DRL; and 5.6% reduced CO emissions versus HGA. This research fills gaps in existing methods via real-time adaptability and multi-objective optimisation, offering a scalable, holistic solution balancing cost, time, and environmental impact, providing actionable insights for logistics firms.

Keywords: elastic dynamic scheduling algorithm; EDSA; instant delivery logistics; multi-objective optimisation; genetic algorithm; energy efficiency; real-time.

DOI: 10.1504/IJICT.2025.151167

International Journal of Information and Communication Technology, 2025 Vol.26 No.52, pp.41 - 55

Received: 13 Aug 2025
Accepted: 13 Nov 2025
Published online: 15 Jan 2026 *

Title: Elastic dynamic scheduling algorithm for instant delivery logistics vehicles using multi-objective optimisation techniques

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