Optimizing Waste Collection in an Organized Industrial Region: A Case Study

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Optimizing Waste Collection in an Organized
Industrial Region A Case Study

• Gizem Martagan
• Gürdal Ertek
• Sevket Ilker Birbil
• Murat Yasar
• Ahmet Çakir
• Nazim Okur
• Gürdal Güllü
• Ahmet Hacioglu
• Özgür Sevim

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Our Study

• Optimize the logistics of industrial waste
  collection
• Design a supply chain network for industrial
  waste collection
• Network visualization
• Visualize optimal solution
• Enchance ones understanding of optimal solution
• Enable perception of interesting insights

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A Case Study TAYSAD

• Data from TOSB (TAYSAD Organize Sanayi Bölgesi)
  (TAYSAD Organized Industrial Region)
• TAYSAD (Tasit Araçlari Yan Sanayicileri Dernegi)
  (Association of Automotive Parts and
  Manufacturers)

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Why data from TAYSAD?

• 65 market share in Turkey
• Member of CLEPA, European Association of
  Automotive Suppliers
• One of the most important industrial regions with
  2.3 km2 of land
• Close to Sabanci University
• Common projects with Sabanci University

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Why data from TAYSAD?

• Turkish automotive parts suppliers industry has
• 700 firms operating with latest technology
• Considerable contributions to Turkish economy
• 2.4 million exports in 2003

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TOSB
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Problem Description

• Two stage-supply chain problem
• Transport waste from 17 factories of TOSB to 5
  possible container locations
• Transport waste from 5 candidate container
  locations to 1 disposal center

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Problem Description
Candidate container locations
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Problem Description

• Transhipment problem
• Container selection based on capacities and
  locations
• Network model
• Objective Minimize transportation costs
• Best candidates should be selected for transfer
• Classic MIP model for two-stage supply chain

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World Class Logistics Examples
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World Class Logistics Examples
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The Model

• Network Characteristics
• 3 sets of nodes
• Factories
• Candidate container locations
• Disposal Center (DC)
• 2 sets of arcs
• Factories to container locations
• Containers to DC
• Flow of wastes
• Collection from factories
• Transfer to container locations
• Transfer to DC
• Objective Minimize total transportation cost

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The Model

• Sets
• I Set of factories, i 1,,n
• J Set of candidate container locations, j
  1,,m
• Parameters

Cost of sending one tone of waste from factory
i to container location j
Cost of sending one tone of waste from
container location j to DC
Monthly waste amount of factory i
C Maximum monthly capacity of each container
Monthly fixed cost of operating a container at
container location j
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The Model

• Variables

1, if a container is located at a container
location j 0 otherwise
Amount of monthly flow of waste from factory i
to container location j
Amount of monthly flow of waste from container
location j to DC
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The Model

s.t
(1) Capacity and linking
(2) Flow balance _at_ Containers
(3) Flow balance _at_ Factories
(4) Binary variables and nonnegativity
binary
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The Model

• Minimize Total Cost in the supply chain
• Total cost of transferring waste from factories
  to container locations.
• Total fixed cost of operating opened containers
• Total cost of transferring waste from containers
  to DC

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The Model
(1) Capacity and linking constraints

• Capacity constraint monthly flow of waste from
  container location j can not exceed the maximum
  flow capacity
• Links the selection of container location j to
  the monthly waste flow from that container to DC
• Right hand side nonzero only if a container is
  located at container location j

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The Model
(2) Flow balance constraint Containers
(3) Flow balance constraint Factories

• Total amount of waste inflowing from different
  factories to container j is equal to the total
  amount of waste outflowing from that container j
  to DC
• Outgoing waste from factory i is equal to its
  monthly amount of waste

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Modeling and Solution of the Model

• Model built and solved using GAMS (General
  Algebraic Modeling System)
• CPLEX (ILOG) as the MIP solver
• Optimal solution for TAYSAD minimum monthly cost
  of waste transfer is 70,000 YTL (approximately
  45,000)
• Three container locations (c1,c3,c4) choosen out
  of five

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Network Visualization
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Network Visualization

• Visualisation of the optimal solution through a
  simple Java program
• Visualization leads to a better interpretation of
  the optimal solution.
• Two input files problem.txt and solution.txt
• Display only the containers that will be opened

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Network Visualization

• Each factory is represented by a blue circle.
• Magnitude of flows are reflected in the thickness
  of the arcs.
• Flows in the first stage are represented by red
  arcs.
• Flows in the second stage are represented by
  black arcs.
• Arcs labels denote the amount of flows on the
  arcs in ton.

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Related Literature

• A similar model to TAYSAD problem is presented by
  Rahman et al. (1995) as theSolid Waste Transfer
  Stations (SWTS) location problem and solved for
  city of Phoenix, Arizona, USA.
• Related problems in supply chain design
• Vehicle Routing Problem (VRP)
• Vehicle Scheduling and Routing Problems(VSRP)
• Vehicle Routing Problem with Time Windows (VRPTW)

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Related Literature

• Studies on waste management in Turkey
• Koçer et al. (1993) investigated waste transfer
  costs, waste collection, waste disposal areas in
  Elazig,Turkey.
• Karagüzel and Mutlutürk (2005) considered
  geological criteria in order to select most
  suitable waste areas in Isparta, Turkey.
• Demir et al. (2001) investigated waste
  transportation in Yenimahalle, Ankara, Turkey.

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Final Words

• Optimized waste collection in an organized
  industrial region.
• A real world industrial waste collection problem.
• Values given in the paper and the solution are
  distorded due to confidentiality agreements.
• Visualization of the optimal solution leads to
  observation of several interesting issues.

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Thank you