Adaptive large neighbourhood search for the multi-depot arc routing problem with flexible assignment of end depot and different arc types

Ameni Kraiem, Jean-François Audy and Amina Lamghari

Journal of the Operational Research Society, 2025, vol. 76, issue 7, 1319-1337

Abstract: This article introduces an advanced solution to optimize street sweeping operations by extending a multi-depot arc routing problem. The key enhancement involves flexible end depot assignments, where vehicles start and conclude shifts at designated depots. A notable constraint requires subsequent shifts to begin from the destination depot of the preceding shift. The problem involves servicing highway exclusively during night shifts, while other arc types can be addressed during both day and night. The objective is to identify optimal shifts meeting practical criteria while adhering to constraints like maximum shift duration. To address this, a mixed-integer linear programming (MILP) model is presented. It aims to minimize the number of shifts and total travel time. Given the computational complexity of large instances, an adaptive large neighbourhood search (ALNS) metaheuristic was developed. This approach incorporates specialized operators that address unique attributes such as arc type and depot assignments, ensuring arcs are repositioned based on their type and proximity to depots. This tailored approach provides a distinct advantage over classical ALNS operators, as numerical tests indicate that the specialized operators are more efficient in comparison. The approach is evaluated on larger and a real-world instances, demonstrating notable performance in solution quality and computational efficiency.

Date: 2025
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DOI: 10.1080/01605682.2024.2432605

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