Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery

Abstract

This study explored the optimization of groupage intercity delivery using a two-stage genetic algorithm (GA) framework, developed with the BaumEvA Python library. The primary objective was to minimize the transportation costs by strategically positioning regional branch warehouses within a logistics network. In the first stage, the GA selected optimal branch warehouse locations from a set of candidate cities. The second stage addressed the vehicle routing problem (VRP) by employing a combinatorial GA to optimize the delivery routes. The GA framework was designed to minimize the total costs associated with intercity and last-mile deliveries, factoring in warehouse locations, truck routes, and vehicle types for last-mile fulfillment while ensuring capacity constraints are adhered to. By solving both line haul and last-mile delivery subproblems, this solution adjusted variables related to warehouse placement, cargo volumes, truck routing, and vehicle selection. The integration of such optimization techniques into the logistics workflow allowed for streamlined operations and reduced costs.

Keywords

• groupage delivery optimization
• genetic algorithm
• logistics network optimization
• vehicle routing problem (VRP)
• location routing problem (LRP)
• BaumEvA