ITSC 2025 Paper Abstract

Paper FR-EA-T39.6

Castañeda-Rodríguez, Ignacio (University of Technology Sydney), Mihaita, Adriana-Simona (University of Technology in Sydney), MARCHE, Brunelle (Université de Lorraine), Dávila-Gálvez, Sebastián (Universidad de Santiago de Chile), Camargo, Mauricio (Université de Lorraine, ERPI)

A Novel Mixed-Integer Linear Programming Model for the Capacitated Arc Routing Problem Adapted to Roadside Management Operations

Scheduled for presentation during the Regular Session "S39b-Data-Driven Optimization in Intelligent Transportation Systems" (FR-EA-T39), Friday, November 21, 2025, 14:50−15:30, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Autonomous Freight Transport Systems and Fleet Management Solutions, Dynamic Scheduling and Routing for Freight Transport in Urban Environments, Real-time Cargo Tracking and Intelligent Supply Chain Management

Abstract

Roadside management is an issue in many territories worldwide. Decision-makers face multiple challenges in finding the right combination in scheduling roadside maintenance activities while meeting several objectives, such as minimising travelled distance and time while collecting the mowed biomass for future valorisation. This work addresses the problem of planning optimal configurations for Roadside Management Operations (RMO) under tactical and operational decisions. At the tactical decision level, the allocation of resources must be carried out for each technical centre operating within the territory. At an operational level, the routing of maintenance vehicles must be scheduled. For this purpose, a Mixed-Integer Linear Programming (MILP) model is proposed to formulate a new Capacitated Arc Routing Problem (CARP) adapted to RMO (which we denote CARP-RMO). We further evaluate our proposed model with benchmark instances and well-known literature heuristics and show that our proposed optimisation approach is better performing, especially when scaling up to larger areas and multiple constraints. A case study is also presented for the area of Neufchateau, France, based on real data collected from the local operation technical centres, for which we showcase that our optimisation method can achieve good operational performance for both an optimal travel time and efficient biomass collection.