A Spatio-Temporal Decision Support Framework for Large Scale Logistics Distribution in the Metropolitan Area
Rapid growing urbanization and explosive e-business expect effective logistics distribution service in the metropolitan area. Because of traffic control, commuting peak and unpredictable traffic accidents, traffic states in the metropolitan area fluctuate sharply, leading to the unacceptable logistics service delay in our daily life. To overcome this problem, a spatio-temporal decision support (STDS) framework is developed to facilitate large scale logistics distribution in the metropolitan area. It consists of a traffic information database, a spatio-temporal heuristic algorithm module, many intelligent mobile apps and a cloud geographical information science (GIS) based logistics server. The spatio-temporal heuristics algorithm is to optimize logistics vehicle routing with the historical traffic information. The mobile apps guide the deliverymen in the real-time logistics. The cloud GIS based logistics server integrates traffic information, client demands, vehicle information, the optimization of vehicle routing and the monitoring of logistics processes. The STDS framework has been implemented in a GIS environment. Its performance is evaluated with large scale logistics cases in Guangzhou, China. Results demonstrates the effectiveness and the efficiency of the developed STDS framework. The STDS framework could be widely used in the logistics distribution in metropolitan area, such as the express delivery, e-business, and so on.
KeywordsSpatio decision support Heuristic algorithm Vehicle routing Traffic information
This research was jointly supported by the National Science Foundation of China. (No. 41401444, 41371377), the Shenzhen Scientific Research and Development Funding Program (No. ZDSY20121019111146499, No. JSGG20121026111056204, No. JCYJ20120817163755063), the Shenzhen Dedicated Funding of Strategic Emerging Industry Development Program (No. JCYJ20121019111128765), China Postdoctoral Science Foundation funded project (2014M560671) and the open research fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. 13S02). The authors also would like to thank the reviewers for their valuable comments and suggestions.
- Densham PJ (1991) Spatial decision support systems. Geogr Inf Syst Principles Appl 1:403–412Google Scholar
- Hu ZH, Sheng ZH (2014) A decision support system for public logistics information service management and optimization. Decision support systems. http://dx.doi.org/10.1016/j.dss.2013.12.001
- Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 1944. IEEE Press, New York, pp 1942–1948Google Scholar
- Miller H, Shaw SL (2001) Geographic information systems for transportation: principles and applications. Oxford University Press, USAGoogle Scholar
- Tu W, Fang Z, Li Q (2010) Exploring time varying shortest path of urban OD pairs based on floating car data. In: IEEE GRSS the 18th international conference on geoinformatics, geoinformatics 2010. The Geographical Society of China, BeijingGoogle Scholar
- Tu W, Fang Z, Li Q (2012) A fast algorithm for large scale vehicle routing routing optimization based on Voronoi diagram. J GeoInf Sci 14(6):781–787Google Scholar
- Tu W, Fang Z, Li Q (2013) An empirical analysis of Voronoi neighborhood characteristics of heuristic solutions for capitated vehicle routing problems. Paper presented at the international symposium on recent advances in transport modeling, OPTIMUM2013. Golden coast, AustraliaGoogle Scholar
- Tu W, Li Q, Fanng Z (2014b) Large scale multi-depot logistics routing optimization based on network Voronoi diagram. Acta Geodaet Cartogr Sin 43(10):1075–1082Google Scholar
- Weigel D, Cao B (1999) Applying GIS and OR techniques to solve Sears technician-dispatching and home-delivery problems. Interfaces 29:112–130Google Scholar
- Zeimpekis V, Minis I, Mamassis K, Giaglis GM (2007a) Dynamic management of a delayed delivery vehicle in a city logistics environment. In: Zeimpekis V, Tarantilis C, Giaglis G, Minis I (eds) Dynamic fleet management. Operations research/computer science interfaces series, vol 38. Springer, US, pp 197–217Google Scholar
- Zeimpekis V, Tarantilis CD, Giaglis GM. Minis I (2007b) Dynamic fleet management: concepts, systems, algorithms & case studies. Springer, New YorkGoogle Scholar