Route optimization of an electric garbage truck fleet for sustainable environmental and energy management

Erdinc O., Yetilmezsoy K., Erenoğlu A. K., Erdinç O.

JOURNAL OF CLEANER PRODUCTION, vol.234, pp.1275-1286, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 234
  • Publication Date: 2019
  • Doi Number: 10.1016/j.jclepro.2019.06.295
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1275-1286
  • Keywords: Electric vehicles, Garbage trucks, Route optimization, Solid waste management, SOLID-WASTE BIN, COLLECTION, SMART, VEHICLES, FUTURE, CITIES, MODEL, GIS, TECHNOLOGIES, FRAMEWORK
  • Yıldız Technical University Affiliated: Yes


The waste collection process is an issue where numerous studies have already been conducted in the existing literature especially based on finding the optimal routes for the garbage trucks assigned in this service process. In this manner, A Mixed-Integer Linear Programming (MILP) based route optimization model has been conducted as the first attempt for waste collection-oriented electric garbage trucks routing process in this study. Even different studies exist in the literature for the route optimization of conventional fuels-based garbage trucks as mentioned above, no studies devote to considering the electric garbage trucks to the best of our knowledge. Besides, it is not easy to reach the detailed garbage collection area information in the literature. In this manner, data have been obtained by real field measurements in a region within the service area of Bakirkoy Municipality, Istanbul, Turkey. A unit energy consumption value that can be considered as a reference in the future has also been obtained using real data. Besides, real road information data have been integrated to the data used as input while assessing the optimization approach and the system analyses have been conducted in a more realistic concept. The proposed concept has led to an increased reality of nearly 38% for the analysis of the results under conditions closer to real-time, and a decrement of nearly 32% has been obtained. It is expected that this study may lead a conceptual input to an enhanced and greener waste collection process. (C) 2019 Elsevier Ltd. All rights reserved.