Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Manufacturing and Logistics Optimization · Vehicle Routing Optimization Methods · Metaheuristic Optimization Algorithms Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
Information and Control Systems Department, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Prospekt, 660037 Krasnoyarsk, Russia
Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia
References (67)
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