The project aims to reduce the total distance travelled by the fleet of vehicles for collection of municipal solid waste by planning new collection routes using Vehicle Routing Problem Solver (part of Network Analyst extension) in ArcGIS.

1. ROUTE OPTIMIZATION FOR COLLECTION
OF MUNICIPAL SOLID WASTE
CASE EXAMPLE : KATPADI
GUIDE:- DR. AMIT MAHINDRAKAR
MATALIA YASH YOGESHBHAI 13BCL0035
HARSHIT VIKRAM SHAHI 13BCL0025
MANU SHARMA 13BCL0254

2. INTRODUCTION
• Katpadi is located in Vellore district 140 kilometres west of
Chennai in Tamil Nadu.
• The area is divided into 15 wards for ease of administration.
• The sanitation fleet consists of 9 vehicles, serving 29,280
customers six days a week.
• Studies have shown up to 75% of a sanitation department’s
budget goes directly to solid waste collection and transport. This
is clearly an area for studies to evaluate for cost saving measures.
• Hence, this project tries to reduce the distance travelled by the
fleet which would reflect as cost savings in fuel consumption.

3. OBJECTIVES
• Reduce the overall distance driven to collect and
transport municipal waste from container bins.
• Reduce the overall vehicle drive time to collect and
transport municipal waste from container bins.

4. LITERATURE REVIEW
• ArcGIS Network Analyst was employed to calculate the shortest solid waste
collection route, with the goal to reduce overall fuel costs, for City of Kragujevac,
Serbia, which has approximately 4,000 waste bins at 2,000 locations within 12 city
collection districts. There was 28.1% decrease in overall kilometres travelled[Jovicic,et
al. ,2011].
• Ghose et al. [6] who developed a GIS model for calculating optimal route in the state
of West Bengal, India, and have shown that its application would allow colossal
savings over a period of 15 years.
• Karadimas, et. Al., (2007) used ESRI’s Network Analyst to create an optimized route
to collect 15 bulk items from different locations within Attica, Greece
• 30% decrease in the number of communal bins needed within the city was reported
by Karadimas, et al. (2008) in Athens, Greece using network analyst. Reducing the
quantity from 162 to 112 waste bins produced a substantial savings in energy used
for waste collection.

5. LITERATURE REVIEW
• Routing algorithms are based on comparing different path length using
many algorithms like Annealing, Tabu Search, Genetic Algorithm, Ant
Colony Optimization, and Dijkstra’s algorithm [Karadimas, et al.,2007].
• Dijkstra’s algorithm divides the network dataset into a traversable or non-
traversable piece of the network. In addition, each network has associated
attributes which decides the travel process in a segment of road. Costs are
calculated using two different criteria. The distance is calculated based on
total edge length, and the time is evaluated using measures edge length
and time to traverse a segment [Karadimas, et al.,2008].
• The VRP solver is a new feature with very few work done with it in solid
waste management. The VRP solver is a significant element to consider
whenever developing a waste collection route optimization plan [Jovicic,et
al. ,2011].

6. METHODLOGY
• Current waste generation and collection data, details of the vehicles’ such as
fuel consumption and capacities were required for the planning of routes
which were made available from the municipal corporation.
• There are several inputs to the ArcGIS Network Analyst VRP solver to
calculate optimal routes for solid waste collection such as collection points
(bins), renewal points (depots), parking locations (start & stop points) which
were obtained by field visits and from officials of the municipal corporation.
• A network dataset of Katpadi roads was obtained and updated.
• Coordinates of all point locations (existing bins, segregation yards) were
tabulated in excel and added to ArcGIS.

7. MAJOR
COMPONENTS
OF THE
WORKFLOW

8. METHODLOGY
• Road Network was updated to add some missing segments of
roads and planarized to form a continuous network without any
connectivity issues.
• Attributes such as length & travel time of different sections of the
road network were calculated.
• Relocated bins layer was created manually on the existing map.
• Layer properties were defined according to need for executing
optimisation analysis.
• A new network dataset was created using the road network polyline
layer as the source.
• Network analyst extension was enabled and a new vehicle routing
problem was started.

9. METHODLOGY
• Location of dustbins were loaded as orders to be picked and
location of segregation site were loaded as depots in the vehicle
routing problem.
• Fleet of vehicle used by municipality were added as routes and
properties of each vehicle(route) such as capacity, service time, start
time/end time, start/end depots, curb approach etc. were defined.
• In order to satisfy capacity of routes and to optimise distance, travel
time, depots were selected as route renewal points.
• Route seed points were also added in some cases.
• Properties of all network analysis layer were defined and analysis
was done by pressing solve button.
• Output of various routes were analysed.

10. RESULTS AND
DISCUSSION
• Figure shows relocated
bins to be Serviced
• Total Capacity: 57.5 MT
• Total Waste Collected:
46 MT (80 % utilization
of bin capacity)

11. RESULTS AND DISCUSSION
Optimized Routes
Collection Route for Auto Collection Route for Dumper 1

12. RESULTS AND DISCUSSION
Driving Directions for Dumper 1

13. Analysis of optimized routes for
existing bins
EXISTING BINS EXISTING SYSTEM
VEHICLE MILEAGE
DISTANCE
(KMS)
FUEL
(L)
TIME
(MIN)
COST
(INR)
DISTANCE
(KMS)
FUEL
(L)
TIME
(MIN)
COST
(INR)
DUMPER 1 8 142.15 17.77 673.60 1,087.45 98.90 12.36 455.64 756.59
DUMPER 2 8 136.91 17.11 717.70 1,047.36 78.91 9.86 375.71 603.66
TRACTOR 1 9 92.42 10.27 539.30 628.46 92.48 10.28 410.07 628.86
TRACTOR 2 9 90.81 10.09 458.28 617.51 125.27 13.92 501.19 851.84
TRACTOR 3 9 104.02 11.56 641.07 707.34 132.71 14.75 580.89 902.43
TRACTOR 4 9 69.46 7.72 382.86 472.33 141.00 15.67 594.06 958.80
MINI TIPPER 10 93.75 9.38 420.00 573.75 144.65 14.47 618.64 885.26
AUTO 20 10.22 0.51 75.87 31.27 27.10 1.36 118.51 82.93
TATA ACE 21 4.27 0.20 22.07 12.44 138.50 6.60 631.21 403.63
TOTAL 744.01 84.61 3930.75 5,177.90 979 99.25 4285.92 6073.99
Reduction 24.00% 14.75% 8.29%
Daily Savings 896.08
Yearly Cost 16,15,506.11 18,95,083.97
Yearly Savings 2,80,473.94

14. Analysis of optimized routes for
relocated bins
RELOCATED BINS EXISTING SYSTEM
VEHICLE
MILEAGE
(KMPL)
DISTANCE
(KMS)
FUEL
(L)
TIME
(MIN)
COST
(INR)
DISTANCE
(KMS)
FUEL
(L)
TIME
(MIN)
COST
(INR)
DUMPER 1 8 87.38 10.92 399.49 668.46 98.90 12.36 455.64 756.59
DUMPER 2 8 118.39 14.80 583.56 905.68 78.91 9.86 375.71 603.66
TRACTOR 1 9 99.58 11.06 533.46 677.14 92.48 10.28 410.07 628.86
TRACTOR 2 9 79.40 8.82 451.74 539.92 125.27 13.92 501.19 851.84
TRACTOR 3 9 78.35 8.71 487.70 532.78 132.71 14.75 580.89 902.43
TRACTOR 4 9 64.45 7.16 322.88 438.26 141.00 15.67 594.06 958.80
MINI TIPPER 10 85.32 8.53 391.29 522.16 144.65 14.47 618.64 885.26
AUTO 20 48.93 2.45 250.71 149.73 27.10 1.36 118.51 82.93
TATA ACE 21 49.80 2.37 279.24 145.13 138.50 6.60 631.21 403.63
TOTAL 711.6 74.82 3700.07 4,579.26 979 99.25 4285.92 6073.99
Reduction 27.31% 24.61% 13.67%
Daily Savings 1,494.73
Yearly Cost 14,28,729.16 18,95,083.97
Yearly Savings 4,67,849.53

15. RESULTS AND DISCUSSION
• The optimized collection routes for existing bins
returned a distance reduction of 24% which translates
to 24% reduction in fuel costs. The total time for
collection & transportation was also reduced by 8.3%.
• The optimized collection routes for relocated bins
returned a distance reduction of 27% which translates
to 27% reduction in fuel costs. The total time for
collection & transportation was also reduced by
13.7%.

16. RESULTS AND DISCUSSION
• Similar Methodology can be used for optimization of
waste collection routes in other cities/towns but
would require accurate data.
• Further, This project could be used as a guideline for
configuring route optimization for any services that
travel within the city to perform a pickup or delivery.
This could be school bus, public transportation
routes, street sweeping, scheduled utility asset
management and maintenance, or any other city
service that has multiple stops along the network.

18. REFERENCES
1. http://www.thehindu.com/news/national/tamil-nadu/Vellore-makes-it-to-smart-citieslist/article14991598.ece
2. Karadimas, N. V., Kolokathi, M., Defteraiou, G., & Loumas, V., “Municipal Waste Collection of Large Items Optimized
with Arc GIS Network Analyst” 21st European Conference on Modelling and Simulation, 2007
3. Karadimas, N. V., Doukas, N., Kolokathi, M., & Defteraiou, G., “Routing Optimization Heuristics Algorithms for Urban
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4. Jovicic, N. M., Boskovic, G. B., Vujic, G. V., Jovicic, G. R., Despotovoc, M. Z., Milovanovic, D. M., et al. (2011). Route
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Transportation: Case Study of Sfax City, Tunisia” Journal of Engineering, Vol. 2016, 2016
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Optimization by using GIS - a case study of Indapur city” IJRITCC Vol. 3 Issue 5, 2015
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