NARLIDERE MUNICIPALITY WASTE COLLECTION PROCESS

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NARLIDERE MUNICIPALITY WASTE COLLECTION PROCESS

• SERDAR SAVASAN
• ANIL KEKLIK
• BILLUR ÇEVIKEL
• BELIZ GÜLDEN
• SINAN ÇAVDIRLI

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Summary of the First Semestre

• Aim
• Routes of dustcarts
• Data collection
• Sample
• TSP
• Two approach
• Gams and heuristics
• Result

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Data That We Obtain ctd.

• Monthly worker cost 1.500 TL
• Sunday overtime cost 174 TL
• Hourly overtime cost 10,8 TL
• Uzundere rubbish heap distance 15km, 15 minutes
• Harmandali rubbish heap distance 70 km, 2 hours
• Fuel consumption of a dustcart 56 liters per 100
  km
• Waste consumption per residence 2,1 kg
• Waste consumption of Yenikale Ward 4,505 kg per
  day

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GAMS Solution
TOTAL 2,558 meters
SATISFIED DEMAND 1204 KG
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Intuitive Solution
TOTAL 2,796 meters
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Nearest Neighborhood Solution
TOTAL 2,879 meters
Dissatisfied Node
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Result

• GAMS solution is 12 more efficient than Nearest
  Neighborhood solution.
• GAMS solution is 10 more efficient than
  Intuitive solution.

When the problem size gets bigger, the gap
between GAMS solution and intuitive will
definitely increase.
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Project Schedule ctd.
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Expanding The Model

• M

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Adding costraints

• Transform simple model to a more complex model
• Capacitated TSP

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• As a simplified VRP
• Given
• A set (fixed number) of pick-up points,
• The demand at every pick-up (determined),
• A set (fixed number) of vehicles 10
• All relevant distance information across pick-up
  points.
• Solution
• Assigning pick-up points to vehicles
• Sequencing pick-up points on the route of each
  vehicle
• Objective
• Minimizing the total distance traveled by the
  dustcarts.
• Constraints
• Every route terminates at the Uzundere garbage
  heap
• The capacity of vehicle is restricted 8 tonnes
• Each pick-up point is visited once only. It means
  all nodes will be visited
• All waste collection demand will be satisfied
• Etc.,

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Meeting with academic advisor

• Meeting every Tuesday between 100 and 130

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Defining current system

• Comparison between current systems and our
  results
• Interviewing with dustcart driver
• Traveling with dustcart may be an extreme
  solution

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Our progress

• Visit Yenikale Ward sample area
• Our route is applicable or not
• Our assumption is useful or not
• Location of container
• Problematic streets
• We observed two problems

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First problem is about the streets

• Problematic streets
• street which dustcart definitely can not
• some streets are not available actually

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• We have to eleminate these streets from the model

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Second problem is location of the containers

• Container in Yenikale
• Majority located in intersection points
• Minority located in middle of the street
• There are no containers in majority of the
  streets
• Containers in our model
• All containers are in the middle of the street
• We assigned demand each street

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If there is one container
If there are two containers
If there are three containers
If there are four containers
If there are five containers
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44
58
69
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42
50
52
68
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CONSTRUCTION AREA
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49
48
51
56
63
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THERE IS A TREE ON MIDDLE OF THE ROAD
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55
62
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46
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NARROW WAY
NO WAY
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54
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64
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45
53
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Main problem

• Our model has 44 demand points
• In actual fact, there are only 18 demand points

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• Our first model which based on our assumptions,
  will definitely be more costly than municipality
  operations.
• We can not observe whole district because of lack
  of time

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Two ways

• Working on expanding our first model by ignoring
  these findings
• Only working on two wards
• Determine location of the streets
• Determine problematic streets
• Compose a new model

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• Thank you for your attention