Publication Type:

Conference Paper

Source:

Machine Learning and Metaheuristics Algorithms, and Applications, Springer Singapore, Singapore (2020)

ISBN:

9789811543012

URL:

https://link.springer.com/chapter/10.1007/978-981-15-4301-2_5

Keywords:

Guided local search Iterative hill climbing, Metaheuristics, Tabu search Simulated annealing, Vehicle routing with pickup and delivery

Abstract:

In a farming community, different types of commodities may need to be transported to different destinations, like the market, storage unit or a processing unit, during the harvest season. To organize efficient transportation in such a setting, the problem is formulated as a Vehicle Routing Problem with Pickups and Deliveries, by considering a virtual field and a virtual destination for delivery of each commodity. To solve this particular problem instance, four common metaheuristics - iterative hill-climbing, guided local search, tabu search, and simulated annealing - were tried and their performances based on total tour lengths for different run times were compared. Basic implementations of these metaheuristics were done using Google OR tools. Guided local search was found to produce good solutions quicker than others. In the long run, tabu search was able to find a slightly better solution. Simulated annealing was prone to get trapped in a local optimum for hours.

Cite this Research Publication

A. Mohan, Dileep, A., Ajayan, S., Gutjahr, G., and Prof. Prema Nedungadi, “Comparison of Metaheuristics for a Vehicle Routing Problem in a Farming Community”, in Machine Learning and Metaheuristics Algorithms, and Applications, Singapore, 2020.