Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization. (March 2017)

• Record Type: Journal Article
• Authors: Akhtar, Mahmuda; Hannan, M.A.; Begum, R.A.; Basri, Hassan; Scavino, Edgar
• Is Part Of: Waste management. Volume 61(2017)
• Page Start: 117
• Page End: 128
• Publication Date: 2017-03
• Subjects: Waste collection -- Route optimization -- BSA -- CVRP model -- Threshold waste level; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2017.01.022
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: Waste collection and transportation incur the huge budget in waste management. BSA in CVRP model for waste collection and route optimization. TWL and scheduling concepts are applied in BSA based CVRP model. BSA in CVRP model improves collection efficiency, reduces costs and emissions. BSA in CVRP model is a valuable tool for waste collection route optimization. Abstract: Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.