Abstract
Rapid urbanization has resulted in traffic congestion leading to air and noise pollution. An easy and effective solution to this problem is carpooling. The objectives of the carpooling system are generally conflicting in nature and hence obtaining an optimal route falls under the domain of multi-objective optimization. Most of the literature, available in this domain, treats these objectives at a horizontal level. This work proposes a hierarchical approach of classifying objectives; first at the micro level to choose a particular passenger based on passenger location characteristics and then optimizing at a macro level to obtain the most profitable route. The most optimum routes are generated which maximizes the profit for the service provider by minimizing the travel cost and the passenger pickup-drop cost and maximizing the capacity utilization of the car. The proposed algorithm generates Pareto optimal solutions.
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Beed, R.S., Sarkar, S., Roy, A., Bhattacharya, D. (2020). Hierarchical Multi-objective Route Optimization for Solving Carpooling Problem. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_30
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DOI: https://doi.org/10.1007/978-981-15-2188-1_30
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