Waste Transportation Route Optimization in Malang using Network Analysis

Abstract

Malang has a projection population of 861,414 people in 2018. This big population would cause waste generation that is increasing every day. Waste generation was recorded at the amount of up to 646.07 tonnes/day just from Malang in 2018. However, waste transported to landfill was only 516.84 tonnes/day. It means that the load factor was only 84.73%. Waste transportation problems come from various factor. Malang had only 68 temporary waste storage (TPS) that were spread in the city. This number of TPS was not commensurate with waste generation that was generated every day. The limited number of trucks was another issue considering Malang only had 35 trucks capable of making three trips per day. Limited number of trucks and TPS has led to a bad situation for transporting waste. As a result, many wastes were left behind in TPS. Therefore, it is necessary to analyze the best route for the trucks sso that the waste route can be optimized using network analysis in ArcGIS. This study assessed one route of a truck which had the longest distance (TPS Tunggul Wulung) with a total of 60.2 km at a time that it reached in 113.8 minutes. The result of the network analysis method with ArcGIS showed that the total distance declined to 36.2 km. This indicates that the alternative route shown by the shortest distance method has been able to reduce the existing distance for waste transportation route by 24 km. This certainly resulted in the decline in travel time for ± 39 minutes/day. This indicates that the existing routes can be optimized by alternative route network analysis in ArcGIS.

Estimating baseline CO2 emission from waste collection and transport activity to TPST Bantargebang, Jakarta

“…The ArcGIS was used to analyze proximity in buffering, overlay, and network analysis, which served as the optimum pathfinder using the transport time and length parameters [3,4]. Detailed spatial information was needed, such as geographical data, street data, TPS coordinates, and other spatial data related to waste transport from TPS to TPST Bantargebang.…”

“…The more waste reduced at the source, the less MSW is transported to the TPST Bantargebang. As a typical big city in Indonesia, the characteristics of waste in Jakarta are dominated by households at 78.8%, while the composition of plastic, paper, and metal waste reached 17.4% [4]. The composition has huge potential to be reduced at the ITF through both organic treatment and upcycle processes.…”

“…This research complements findings on the CO2 emission calculation in TPST Bantargebang, particularly in the waste treatment process. The author noted that recent studies [1,3,4] discussed waste transportation models in other cities in Indonesia, but no studies had been conducted in Jakarta. This research urged to provide a baseline input for DKI Jakarta Governor Regulation No.…”

Collection of Waste from Passenger Ships and Its Impact on the Functioning of Tourist Port City Świnoujście

“…Properly organized waste disposal carried out in the city should take into account these resources, so as not to impede the fulfillment of other transport needs, although obviously waste transport will always be an additional burden on the city's transport system [15]. The road system of cities does not always enable the optimization of waste transport routes, thus requiring the introduction of other organizational solutions [24].…”

Prospects and challenges of ripple spreading algorithm for waste collection path optimization problem in Indonesia

“…Lastly, the city term for Bandung, Semarang, and Yogyakarta, have gained an advanced waste collection mechanism, especially in tracking and disposal centers. The cities of Semarang and Yogyakarta employed the internet of things to track daily waste generation [12] [32]. Otherwise, Bandung manages its waste collection service through disposal covering measures [23] [29].…”