Vehicle Routing and Scheduling Problems in Supply Chain Management using Ant Colony Algorithm

Abstract

Supply chain management plays a crucial role in ensuring smooth business operations and meeting customer demands effectively. Failure to optimize delivery scheduling and route selection can lead to inefficiencies, such as increased travel distances and operational challenges. This study addresses the Vehicle Routing and Scheduling Problem (VRSP) within the context of supply chain management, focusing on minimizing total travel distance. The research employs the Ant Colony algorithm, a powerful method for solving complex and nondeterministic polynomial-time (NP-hard) problems, implemented using Python software. Key factors considered include vehicle capacity, customer demand, and delivery time constraints, ensuring efficient route planning and delivery schedules. The study involves deliveries from a warehouse in Guar Chempedak, Kedah, to 19 customer locations across Kedah, such as Kulim, Kodiang, Jitra, Kampung Bukit Selambau, Pokok Sena, Sik, Amanjaya Sungai Petani, and others. Results reveal optimized travel distances for five delivery trips: 215.25 km for Trip 1, 167.01 km for Trip 2, 115.45 km for Trip 3, 113.04 km for Trip 4, and 181.56 km for Trip 5. These findings demonstrate the effectiveness of using the Ant Colony algorithm in selecting optimal delivery routes and achieving significant reductions in transportation distance. In conclusion, the study highlights the importance of optimizing route planning and scheduling in supply chain management to reduce travel distances. Although potential limitations, such as dynamic traffic conditions or unexpected delays, may influence real-world implementation, the outcomes provide valuable insights for enhancing delivery efficiency and improving overall supply chain performance.