Integrating Drivers Differences into Green Waste Collection Vehicle Routing Problem
Date
2021-07-01
Authors
Haj Mohammad, Ahmad
Journal Title
Journal ISSN
Volume Title
Publisher
جامعة النجاح الوطنية
Abstract
Hazardous waste management is a crucial issue that needs adequate attention and planning. Many efforts were done by researchers on the Hazardous Waste Vehicle Routing Problem (HWVRP). However, a very few of these consider human aspects while solving the problem. In this research, two human related aspects were included in the developed HWVRP model, which are driver’s Green Driving Index (GDI) and driver’s awakeness levels. Therefore, three levels of drivers’ GDI were assumed based on their awareness of environmentally friendly driving behavior, as well as, three levels of alertness which reflect the degree of sleepiness (awakeness) of drivers. The effect of the aforementioned levels on different functions such as cost function is investigated to reveal the influence of driver differences on HWVRP. The proposed multi-objective model was designed to tackle real world problems by dealing with multi-vehicles, multi-waste type, multi-waste nodes (generation, treatment, recycling and disposal) along with different driver’s GDI and awakeness levels. This HWVRP model aims at: (1) minimizing total costs such as transportation, hiring, firing, training, and waste amount related costs, (2) minimizing transportation risks on the population located along the transportation route, (3) minimizing site risks on the population living around waste processing facilities, and finally (4) maximizing driver’s awakeness level. A Non-Dominated Sorting Genetic Algorithm - II (NSGA - II) was used to solve the developed model, due to its popularity in solving such problems in an effective and timely manner. Different size instances were used for the purpose of model validation; in addition, a sensitivity analysis was conducted on the effect of different parameters / variables on the model’s objective functions to ensure that the model functions as it should. Results showed a direct relationship between driver’s GDI levels and the total costs, where the increase in driver’s level yields a reduction in costs up to certain point, but costs start to increase again due to training and salaries costs for high-level drivers. Moreover, an indirect link was found between awakeness levels and transportation risk, where increasing the level of driver’s awakeness resulted in a reduction in transportation risks, although no mathematical relation is present between the two functions.