Matheuristic for Vehicle Routing Problem with Multiple Synchronization Constraints and Variable Service Time

TL;DR

This paper introduces a matheuristic approach to a complex vehicle routing problem with multiple synchronization constraints and variable service times, motivated by real agricultural operations, demonstrating improved efficiency over traditional methods.

Contribution

It develops a novel MIP model and a three-phase matheuristic with local search for large instances, addressing real-world synchronization and variable service time challenges.

Findings

The matheuristic outperforms state-of-the-art MIP solvers in solution quality and speed.

Real-world data shows current policies overestimate costs by 15-20%.

Different decision-maker preferences significantly affect key performance metrics.

Abstract

This paper considers an extension of the vehicle routing problem with synchronization constraints and introduces the vehicle routing problem with multiple synchronization constraints and variable service time. This important problem is motivated by a real-world problem faced by one of the largest agricultural companies in the world providing precision agriculture services to their clients who are farmers and growers. The solution to this problem impacts the performance of farm spraying operations and can help design policies to improve spraying operations in large-scale farming. We propose a Mixed Integer Programming (MIP) model for this challenging problem, along with problem-specific valid inequalities. A three-phase powerful matheuristic is proposed to solve large instances enhanced with a novel local search method. We conduct extensive numerical analysis using realistic data. Results show that our matheuristic is fast and efficient in terms of solution quality and computational time compared to the state-of-the-art MIP solver. Using real-world data, we demonstrate the importance of considering an optimization approach to solve the problem, showing that the policy implemented in practice overestimates the costs by 15-20%. Finally, we compare and contrast the impact of various decision-maker preferences on several key performance metrics by comparing different mathematical models.