1. GIS Workshop Project BY ARUNKUMAR GUNASEKARAN
Districts for residential trash pickup crew City of Garland Environmental Waste services
GIS Workshop Project BY
ARUNKUMAR GUNASEKARAN

2. ABOUT ENVIRONMENTAL WASTE SERVICES
The department provides weekly solid, brush, bulky and recyclable waste pickup for approximately homes.
It competes with the private sector for the provision of both commercial dumpster collection and roll of collection services for approximately 6000 commercial business located in garland.

3. As part of the Solid Waste Operation the city operates a landfill at Hilton, Rowlett to process the trash collected.
In short EWS works hard to keep the city clean.

4. Initial study on residential Districts
City is divided into four main Districts.
Refuse is collected four days a week (Tuesday-Friday)
Each District is subdivided into16 districts totaling 64 sub districts for all four days.
City operates 20 side loader collection vehicles with 4 as backup.

6. Draw Backs Current Districting has lot of drawbacks.
They where not balanced.
Extra containers owned by residents were not included while routing.
Types of trucks and their maximum capacity are not taken into account.
More than every thing else the route was last optimized 9 years ago.
SUMMARY OF NUMBER OF HOMES PER ROUTE
TUE
WED
THU
FRI
MINIMUM
578
789
874
649
MAXIMUM
1230
1241
1348
1498
SUBTOTAL
14,871
17068
17192
16694
T0TAL
65817
AVERAGE
929
1066
1075
1043

7. Due to the growth of the city in the past 9 years and due to the substantial difference in the size of each Districts with respect to work load (number of carts), the districts seems to be more heavier to work with.
The districts were not balanced resulting in operators spending extra time in helping others route and in extra wear and tear on vehicles running on heavy routes.
Wednesday and Friday found to be more heavier than Tuesday and Thursday since the were not balanced either.

8. Project Objectives
The objective of this project is to collect data regarding the extra containers through out the city using GPS and use the data collected to come up with balanced districts which are effective and efficient to work with.

9. Literature
Poot A, Kant G, Wagelmans APM. A savings based method for real-life vehicle routing problems. Journal of the Operational Research Society 2002; 53:57–68.
Byung-In Kima, Seongbae Kimb, Surya Sahoob. Waste collection vehicle routing problem with time windows. Computers & Operations Research 33 (2006) 3624–3642
Rochat Y, Taillard ED. Probabilistic diversification and intensification in local search for vehicle routing. Journal of Heuristics 1995; 1:147–67.
[4] Taillard ED, Badeau P, Gendreau M, Guertin F, Potvin JY. A tabu search heuristic for the vehicle routing problem with soft time windows. Transportation Science 1997; 31(1):170–86.

10. Literature
Each of these algorithms is problem specific depending on the limitations on which the clustering has to be developed. Hence each of them used their own set of criteria’s to come up with final product.

11. Criteria’s used
Some key criteria’s have been set to see if the end result is actually optimized.
1.The four main collection areas should have a fair balance.
2. Capacity of the 16 trucks (in number of carts) x the total trips they make should balance the total number of carts in the field
3. Heavier districts should be closer to the disposal station
4. The number of carts serviced on each district should be fairly close to the other districts which are serviced by trucks with the same capacity

12. Methodology Phase 1: 1. Collecting existing data
2. Creating new data (Extra containers shape file)
3. Collecting other data like capacity of the trucks (in number of carts), number of trucks used for collection on each type, tonnage of trash on each route.

13. Methodology Phase 2: 1. Calculating total load on field
2. Calculating maximum capacity of the truck in a single trip
3. Calculating maximum turnarounds a truck can make on its given districts.
4. Setting maximum number of cars for districts serviced by the two different trucks
5. Using Select feature tool to group carts together (n number of carts = a district) and form districts

14. Data used 1.Garland streets shape file.
2.Current residential districts shape file.
3.DCAD residential points shape file.
4.Extra containers shape file.
5.City boundary shape file

15. Other data collected
Data indicating the number of carts picked on a single trip by the two different trucks were collected and averaged
Truck type
Number of carts collected per trip
Heil Rapid Rail
369
Wayne Curbtender
451
The number shown above represents the average taken for 10 trips on Heil Rapid Rail and average of 10 trips for Wayne Curbtender.

16. A summary of total number of trucks in each kind and the number of trucks which are planned to be used as back up is given in the table below
Truck type
Total
number
Backups
Active trucks
Wayne 10 3 7
Heil 12 9

17. Phase 1 Collecting Extra containers data. Why extra containers?
It is important to collect the extra containers data because it had a great impact in calculating the work load on the residential districts.

18. How did we collect it?
Used Arcpad with GPS unit to collect extra containers data
A form is designed in Arcpad and tied up to the extracontainers.shp layer.
A GPS is fixed to the dash of the truck and is connected to a laptop with Arcpad ,to show the vehicles location on Arcpad map.
Extra containers data are captured and added to the map as they are found.

20. Phase 2
Step 1:
The difference between the maximum carts picked on each trip on the Wayne and Heil trucks seemed to help in some way to reduce the fourth trip made by the 30 trucks in a week.
3 trips of Wayne can accommodate 1353 carts
3 trips of Heil can accommodate 1107 carts
Since there are other factors that can affect these numbers at times. They are reduced by 100 and were set as the maximum limits on each district depending on the type of truck that will run on the route and is tabulated.
Routes with Wayne
Routes with Heil
Maximum
number of carts
1253
1007
Truck type
Total number
Backups
Active trucks
Wayne 10 3 7
Heil 12 9

21. Step 2:
In order to place the heavier routes ran by the Wayne trucks near to the transfer station, a 3 mile buffer around the transfer station was created.
Maximum effort was taken to see that all the heavier routes fall within this buffer range.
since I couldn’t divide the days exactly so that the buffer falls equally on all four days, there are heavy routes seen outside the buffer.

22. Phase 2 1. Calculating total load on field
Each residential point in DCAD represents a cart
Total number of regular containers in the city is found to be equal to 61698
Number of extra containers in the city = 11970
Set-out rate for extra containers was 50%
Load from extra containers =11970/2 = 5985
Total load (number of carts) in field = 67683

23. 2. Calculating maximum capacity of the truck in a single trip
A single trip of Wayne can accommodate 451 carts
A single trip of Heil can accommodate 369 carts
Since there are other factors that can affect these numbers. The numbers are reduced by 50 and were set as the capacity of a truck in a single trip.
Districts with Wayne
Districts with Heil
Maximum
Number of
carts
401
319

24. 3. Calculating maximum turnarounds a truck can make on its given districts.
A summary of total number of trucks in each kind and the number of trucks which are planned to be used as back up is given in the table.
Maximum number of carts that can be handled by 7 Wayne trucks and 9 Heil trucks on a given day in a single trip
= (7x401) + (9x319) = 5741
Total carts throughout the city =67683
The load (number of carts) is going to be equally shared by the four service days (Tuesday – Friday).
Load on a single day = / 4 =16921 carts
Total number of trips each of these trucks should make to finish their districts =16921 / 5741
= approximately 3 trips
Truck type
Total number
Backups
Active trucks
Wayne 10 3 7
Heil 16 9

25. 4.Setting maximum number of cars for districts serviced by the two different trucks
3 trips of Wayne can accommodate 1353 carts
3 trips of Heil can accommodate 1107 carts
Since there are other factors that can affect these numbers at times. They are reduced by 100 and were set as the maximum limits on each district depending on the type of truck that will run on the districts and is tabulated.
So the new districts will have 7 districts with load greater than 1000 and less than 1253 and nine districts with load greater than 800 and less than 1007 on each give day from Tuesday thru Friday.
Districts with Wayne
Districts with Heil
Maximum
number of carts
1253
1107

26. 5. Using Select feature tool to group carts together (n number of carts = a district) and form districts
Select feature tool is used to group the number of carts required to form the districts. (One residential point from DCAD = 1 cart and two extra containers from extra containers = 1 cart)
New districts were designed using select feature tool and counting the number of carts in each districts were shown in the map below.

27. Results

28. PROBLEMS
Data for extra containers can only be collected on trash pick up days which delayed the data collection a little.
Rain was an issue on GPS data collection.
Collecting data consumed 75 % of the total time.

29. conclusion
A new optimized districting for the residential crew is developed
Currently trucks running on 30 of 64 Districts in a week make their fourth turn around, which will be reduced to three turnarounds and hence the number of trips made by the trucks will be reduced from to 9984 an year. Means approximately 15 % less trips an year which means 15 % less wear and tear, less operation and maintenance.
An average of 7-10% reduction in operation and maintenance can be attained .
The drivers work load will be considerably reduced.
And it would be convenient for the supervisors to observe the performance of their drivers since all the districts has fairly equal load.

30. References
R.W.Beck (Hired consultant). Study on solid waste operation review for the city of garland, march 2007
Poot A, Kant G, Wagelmans APM. A savings based method for real-life vehicle routing problems. Journal of the Operational Research Society 2002; 53:57–68.
Byung-In Kima, Seongbae Kimb, Surya Sahoob. Waste collection vehicle routing problem with time windows. Computers & Operations Research 33 (2006) 3624–3642
Rochat Y, Taillard ED. Probabilistic diversification and intensification in local search for vehicle routing. Journal of Heuristics 1995; 1:147–67.
[4] Taillard ED, Badeau P, Gendreau M, Guertin F, Potvin JY. A tabu search heuristic for the vehicle routing problem with soft time windows. Transportation Science 1997; 31(1):170–86.