Locating Self-Collection Points for Last-Mile Logistics Using Public Transport Data
Delivery failure and re-scheduling cause the delay of services and increase the operation costs for logistics companies. Setting up self-collection points is an effective solution that is attracting attentions from many companies. One challenge for this model is how to choose the locations for self-collection points. In this work, we design a methodology for locating self-collection points. We consider both the distribution of a company’s potential customers and the people’s gathering pattern in the city. We leverage on citizens’ public transport riding records to simulate how the crowds emerge for particular hours. We reasonably assume that a place near to a people crowd is more convenient for customers than a place far away for self parcel collection. Based on this, we propose a kernel transformation method to re-evaluate the pairwise positions of customers, and then do a clustering.
KeywordsKernel Function Public Transport Gaussian Mixture Model Gaussian Component Facility Location Problem
Unable to display preview. Download preview PDF.
- 2.Biernacki, C., Celeux, G., Govaert, G., Langrognet, F., Noulin, G., Vernaz, Y.: Mixmod-statistical documentation (2008)Google Scholar
- 4.Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1–38 (1977)Google Scholar
- 7.Goodman, R.W.: Whatever you calll it, just don’t think of last-mile logistics, last. Glabal Logistics & Supply Chain Strategies (2005)Google Scholar
- 8.Hwang, C., Masud, A.: Multiple objective decision making, methods and applications: a state-of-the-art survey. Lecture notes in economics and mathematical systems. Springer-Verlag (1979)Google Scholar
- 9.MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, California, USA, vol. 1, pp. 281–297 (1967)Google Scholar
- 10.McLachlan, G., Peel, D.: Finite mixture models. John Wiley & Sons (2004)Google Scholar
- 12.Morganti, E., Dablanc, L., Fortin, F.: Final deliveries for online shopping: The deployment of pickup point networks in urban and suburban areas. Research in Transportation Business & Management (2014)Google Scholar
- 15.Wolfe, J.H.: Normix: Computational methods for estimating the parameters of multivariate normal mixtures of distributions. Technical report, DTIC Document (1967)Google Scholar