A stochastic optimization framework for planning of waste collection and value recovery operations in smart and sustainable cities. (August 2018)

• Record Type: Journal Article
• Authors: Jatinkumar Shah, Parth; Anagnostopoulos, Theodoros; Zaslavsky, Arkady; Behdad, Sara
• Is Part Of: Waste management. Volume 78(2018)
• Page Start: 104
• Page End: 114
• Publication Date: 2018-08
• Subjects: IoT-enabled waste collection and recovery -- Smart cities -- End-of-life recovery -- Chance-constrained programming; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2018.05.019
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: The concept of IoT-enabled waste management in smart cities is discussed. An optimization model with two Sub-models is developed. Sub-model 1 determines the quantity of waste that is transported from bins to depots. Sub-model 2 determines the quantity that is transported from depots to a recovery site. The potential value that can be recovered from smart bins is modeled as an uncertain parameter. Abstract: The concept of City 2.0 or smart city is offering new opportunities for handling waste management practices. The existing studies have started addressing waste management problems in smart cities mainly by focusing on the design of new sensor-based Internet of Things (IoT) technologies, and optimizing the routes for waste collection trucks with the aim of minimizing operational costs, energy consumption and transportation pollution emissions. In this study, the importance of value recovery from trash bins is highlighted. A stochastic optimization model based on chance-constrained programming is developed to optimize the planning of waste collection operations. The objective of the proposed optimization model is to minimize the total transportation cost while maximizing the recovery of value still embedded in waste bins. The value of collected waste is modeled as an uncertain parameter to reflect the uncertain value that can be recovered from each trash bin due to the uncertain condition and quality of waste. The application of the proposed model is shown by using a numerical example. The study opens new venues for incorporating the value recovery aspect into waste collection planning and development of new data acquisition technologies that enable municipalities to monitor the mix of recyclables embedded in individual trash bins.