Globally today, Living in urban areas is more preferred than in living rural areas. This situation creates many problem for urban living. One of the big problem is waste management in urban areas. Optimizing waste collection has become very important phenomenon for being smart city. In this study, we aimed to optimize waste collection for reduce both cost of collection and pollution effect of environment. We designed a garbage container integrated sensors for measuring fill level of container, temperature, and ratio of carbon dioxide inside the container. We transmitted all information to our waste management software based Internet of Things (IoT) technologies. According to the ant colony algorithm, most efficient waste collection route delivered to garbage truck drivers’ cellular enabled smart tablet. We used data mining approach to forecast when garbage container can reach highest level, and the planning of garbage container placement. We applied this smart waste collection management system in a town where is in Kayseri, Turkey. In first step, we applied for 200 Items (garbage containers) in the town that has 548.028 population and urban living ratio is 100%. Before smart waste management system 200 garbage containers was collecting by garbage trucks in a static route. After we had applied smart waste management system, containers were collected by garbage truck in dynamic route. Smart waste management system significantly decreased the trucks’ oil cost, carbon emissions, traffic, truck wear, noise pollution, environmental pollution, and work hours. The system presented approximately 30% with in direct cost savings in waste collection.

[1] Bell J. and McMullen P., “Ant Colony Optimization Techniques for the Vehicle Routing Problem,” Advanced Engineering Informatics, vol. 18, no. 1, pp. 41-48, 2004.

[2] Chowdhury B. and Chowdhury M., “RFID-based Real-time Smart Waste Management System,” in Proceeding of the Australasian Telecommunication Networks and Applications Conference, Christchurch, pp. 175-180, 2007.

[3] Colorni A., Dorigo M., Maniezzo V., and Trubian M., “Ant System for Job-Shop Scheduling,” Belgian Journal of Operations Research Statistics and Computer Science, vol. 34, no. 1, pp. 39-53, 1994.

[4] Costa D. and Hertz A., “Ants can Colour Graphs,” Journal of the Operational Research Society, vol. 48, no. 3, pp. 295-305, 1997.

[5] Donati A., Montemanni R., Casagrande N., Rizzoli A., and Gambardella L., “Time Dependent Vehicle Routing Problem with a Multi Ant Colony System,” European Journal of Operational Research, vol. 185, no. 3, pp. 1174- 1191, 2008.

[6] Dorigo M., Maniezzo V., and Colorni A., “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 26, no. 1, pp. 29- 41,1996.

[7] Glouche Y. and Couderc P., “A Smart Waste Management with Self-Describing Objects,” in Proceeding of the 2nd International Conference on Smart Systems Devices and Technologies, Rome, pp. 63-70, 2013.

[8] Lawrence M. and Wood E., https://www.navigantresearch.com/research/smart -waste-collection, Last Visited 2014.

[9] Liu B., Xia Y., and Yu P., Clustering Via Decision Tree Construction, Springer, 2005.

[10] Malandraki C. and Daskin M., “Time Dependent Vehicle Routing Problems: Formulations, Properties and Heuristic Algorithms,” Jourrnal of Transportation Science, vol. 26, no. 3, pp. 185- 200, 1992.

[11] Maniezzo V., Colorni A., and Dorigo M., “The Ant System Applied to the Quadratic Assignment Problem,” Technical Report, 1994.

[12] Mazzeo S. and Loiseau I., “An Ant Colony Algorithm for the Capacitated Vehicle Routing,” Electronic Notes in Discrete Mathematics, vol. 18, no. 1, pp. 181-186, 2004.

[13] Oralhan B., Uyar K., and Oralhan Z., “Customer Satisfaction using Data Mining Approach,” International Journal of Intelligent Systems and Applications in Engineering, vol. 4, pp. 63-66, 2016.

[14] Rokach L. and Maimon O., Data Mining with Decision Trees: Theory and Applications, World Scientific Publishing Company, 2008.

[15] The Department of Economic and Social Affairs of the United Nations, World Urbanization Prospects, https://esa.un.org/unpd/wup/publications/files/w up2014-highlights.Pdf, Last Visited 2014.

[16] Yigit T. and Unsal O., “Using the Ant Colony Algorithm for Real-Time Automatic Route of School Buses,” The International Arab Journal of Information Technology, vol. 13, no. 5, pp. 559-565, 2016. Zeki Oralhan received his B.Eng., degree and Ph.D. degree in Electrical Electronics Engineering from Erciyes University, Kayseri, Turkey. He works as a lecturer and manager of an information technology company. His research interests includes: brain computer interface, artificial intelligence, data mining, signal processing, and smart technologies products. Burcu Oralhan received her B.Eng., and M.Eng. degree in Industrial Engineering from Erciyes University, Kayseri, Turkey. She received her Ph.D. degree in Business Administrative Department from Cumhuriyet University. Currently, she is an Assistant Professor Dr. in Nuh Naci Yazgan University, Kayseri, Turkey. Her research interests includes: Statistical analysis, artificial intelligence, operational research, and data mining. Yavuz Yiğit graduated from College of software programming from Ankara University, Çankırı, Turkey. He currently works as a computer engineer in Erciyes University Techno Park Area. His research interests includes: Software programming, artificial intelligence, and data mining.