Coordinated Truck Loading and Routing Problem: A Forestry Logistics Case Study

Cristian Oliva, Manuel Cepeda and Sebastián Muñoz-Herrera ()
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Cristian Oliva: Departamento de Ingeniería Industrial, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile
Manuel Cepeda: Facultad de Arquitectura, Arte y Diseño, Universidad San Sebastián, Concepción 4081339, Chile
Sebastián Muñoz-Herrera: Facultad de Ingeniería, Universidad del Desarrollo, Concepción 4040418, Chile

Mathematics, 2025, vol. 13, issue 15, 1-18

Abstract: This study addresses a real-world logistics problem in forestry operations: the distribution of plants from cultivation centers to planting sites under strict delivery time windows and limited depot resources. We introduce the Coordinated Truck Loading and Routing Problem (CTLRP), an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) that integrates routing decisions with truck loading schedules at a single depot with constrained capacity. To solve this NP-hard problem, we develop a metaheuristic algorithm based on Ant Colony Optimization (ACO), enhanced with a global memory system and a novel stochastic return rule that allows trucks to return to the depot when additional deliveries are suboptimal. Parameter calibration experiments are conducted to determine optimal values for the return probability and ant population size. The algorithm is tested on a real forestry dispatch scenario over six working days. The results show that an Ant Colony System (ACS–CTLRP) algorithm reduces total distance traveled by 23%, travel time by 22%, and the number of trucks used by 13 units, while increasing fleet utilization from 54% to 83%. These findings demonstrate that the proposed method significantly outperforms current company planning and offers a transferable framework for depot-constrained routing problems in time-sensitive distribution environments.

Keywords: Vehicle Routing Problem; Time Windows; Ant Colony Optimization; metaheuristic; scheduling; forestry; coordinated loading (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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