Effect of optimal routing on travel distance, travel time and fuel consumption of waste collection trucks

Abstract

Purpose The purpose of this paper was to determine the effect of route optimization on travel distance, travel time and fuel consumption of municipal solid waste (MSW) collection trucks. Design/methodology/approach The study modeled the existing waste collection routes in three local authorities in Ghana and proposed optimal routes using Esri’s ArcGIS Network Analyst Extension. The system generated optimal distances and times were validated by subjecting collection trucks to the optimal routes. Paired sample t-test was used to analyze the differences in means of the existing and optimal outcomes. The collected data were subjected to correlation and uncertainty analyses. Findings Results from the study indicated that weekly travel distance reduced by 81.27 km, translating into saving of 4.79 percent when trucks used the optimal routes. Travel time and fuel consumption reduced by 853.59 min and 145.86 L, making savings of 14.21 and 10.81 percent, respectively. Significant differences occurred between the means of the existing and optimal routes for travel time and fuel consumption, for each of the three local authorities. Research limitations/implications The study was conducted in Ghana using data collected from three local authorities. Practical implications Significant reduction of MSW collection cost can be achieved by waste management practitioners through route optimization. Originality/value The effect of route optimization on travel distance, travel time and fuel consumption has been established. Statistical analyses of the existing and optimal outcomes of the three local authorities have been provided. The findings from this study support the hypothesis that optimal routes reduce operating cost through savings in travel distance, travel time and fuel consumption.

Optimized Routing of Trucks for Institutional Solid Waste Collection in Kumasi, Ghana

“…This resulted in reduced travel times with savings recorded in all operational sites for the three collection systems (Table 3). Similar findings were reported by Sulemana et al (2019) when they studied into the effects of optimal routing on travel distance, travel time and fuel consumption of waste collection trucks in three selected local authorities in Ghana. The optimization system considered road network impedances and restrictions such as traffic light, right turn, round-about, speed rams, pedestrian crossings and one-way road to generate the optimized routes which were time dependent.…”

“…It is on this basis that travel time proved the main criterion for route optimization in this study due to the fact that travel time is directly proportional to operational time with implication on cost (Kinobe et al, 2015). This supports the claim of the possibility of increasing operational coverage of waste collection and get more work done due to optimal time usage (Sulemana et al, 2019). Findings from this study indicate that the longer a truck travels on a specific route, more distance is covered, resulting in more working time as well as fuel and maintenance cost.…”

“…These delay constraints include: speed ramps, number of turns, zebra crossings, traffic flows, etc. It is on this basis that travel distance does not prove a viable criterion for route optimization as reported by Kinobe et al (2015) and Sulemana et al (2019).…”

“…Comparison of optimized distance with existing distance showed significant differences for pegged bins (p = 0.018), small bins (p = 0.041) and all collection systems combined (p = 0.009), but not communal bins (p = 0.834) as displayed in Tables 4-7. Significant reductions in travel distance can be attribut- ed to the effective sequencing of service orders by the GIS-based route optimization system. This generated optimized routes with reduced distances as indicated by O'Connor (2013) and Sulemana et al (2019) in their studies into route optimization using GIS-based approaches. This finding is supported by a study undertaken by Chalkias and Lasaridi (2009) which applied GIS-based optimization system to reduce travel distance, saving 5.5% in the Municipality of Nikea, Athens, Greece.…”

“…This can effectively be achieved through significant savings in travel time and distance. Several studies have been conducted on the use of geographic information system (GIS) applications as a tool for municipal solid waste management (Ristic et al, 2015;Sulemana et al, 2019;Zsigraiova et al, 2013). GIS technology is a widely used decision support system which provides an advanced modelling framework for decision makers in order to analyze and simulate various spatial problems ).…”