Developing Economy

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Developing Economy
Planning for Industrial Estate Development
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Economy of Malaysia

• Fast Growing
• Asia Economic Crisis
• Today

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In recent days, the worlds economy is sick!

• How did the sick come into being?
• Reasons
• Not arrive at the fag end, yet

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Model of Early Warning Systems

• 1.Cross - Country Regression
• 2.Probity Models
• Frankel and Rose Model
• Krueger, Osakwe and Page Model
• Esquivel, Larrain Model
• Kamin, Schindler and Samuel Model
• 3. Signal Approach

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RAPID ECONOMIC DEVELOPMENT IN DEVELOPING
COUNTRIES INEQUALITY IN INCOME DISTRIBUTION
ACHIEVING INDUSTRIAL GROWTH AND ECONOMICAL
GROWTH DISTRIBUTION OF INCOME
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GOOD WAY OF PROMOTING GROWTH

• ESTABLISHMENT OF INDUSTRIAL ESTATES

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PROBLEM DEFINITION

• considered in 1975-1990 in Malaysia
• Purpose reach optimal development of industrial
  estates
• Industrial lands are leased out to interested
  entrepreneurs (revenue occurs)
• also cost occurs
• transportation problem

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PROBLEM DEFINITION (continue)
17 SITES

• upper limits on sites
• each site have different advantageous
• For priority site, minimum size of land must be
  developed

11 RURAL AREAS
6 URBAN AREAS
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DATA of PROBLEM

• 17 Sites, 11 of these has priority
• Cost- Revenue- Maximum amount of public land for
  year 1

• cost and revenue decrease 10 per year

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DATA of PROBLEM (continue)

• demand for industrial land

• minimum required level of development

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MODEL- Decision Variables

• dt estimated demand for industrial land in year
  t
• ai maximum amount of public land available for
  industrial development in site i
• rit discounted revenue collectable per unit area
  of industrial land leased out at site i in year
• cit unit cost of developing industrial land at
  site i in year t
• bi(TI) minimum size of industrial land that must
  be developed and leased out in priority site i by
  the targeted year Ti
• yit amount of developed industrial land leased
  out from site at year t
• eit net discounted cost of developing a unit of
  land at site i and leased out in year t
• Sites J1,2,3,17 Priority Sites
  Jp1,2,3.11
• Years K1,2T T terminal year KK U T1

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MODEL- Objective function
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MODEL-Constraints

• Demand constraint
• Limited land availability constraint

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MODEL -Constraints

• Net Discounted Cost constraint
• Lower bound constraints for priority site

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CONVERSION OF MODEL

• First model ? Transportation Model

Converted
PURE TRANSPORTATION MODEL
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PURE TRANSPORTATION MODEL
28 source nodes
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PTM
16 sink nodes
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PTM- Decision Variables

• dt estimated demand for industrial land in year
  t
• ai maximum amount of public land available for
  industrial development in site i
• bi(TI) minimum size of industrial land that must
  be developed and leased out in priority site i by
  the targeted year Ti
• eit net discounted cost of developing a unit of
  land at site i and leased out in year t (for i ?
  J, t ? K)
• eitêit for i ? J, t ? K
• zit the shipment variables of enlarged network
  for i ? J, t ? K
• âiai- bi(TI) for i ? Jp
• âiai for i ? J-Jp and â bi(TI) for i ?
  m1,,mk
• J JUm1,,mk)

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PTM- OF and Constraints
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• ALTERNATIVE MODEL

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Constructing Our Model

• next year value lt previous year value

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Constructing Our Model

• next year value lt previous year value

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Constructing Our Model

• Deleting Slack Variables
• More understandable lettering

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Constructing Our Model

• To construct an LP model, variable e(it) is
  calculated by hand

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Alternative Model - Objective Function
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Alternative Model -Decision Variables

• dt estimated demand for industrial land in year
  t
• ai maximum amount of public land available for
  industrial development in site i
• rit discounted revenue collectable per unit area
  of industrial land leased out at site i in year
• cit unit cost of developing industrial land at
  site i in year t
• bi(TI) minimum size of industrial land that must
  be developed and leased out in priority site i by
  the targeted year Ti
• yit amount of developed industrial land leased
  out from site at year t
• eit net discounted cost of developing a unit of
  land at site i and leased out in year t
• Sites J1,2,3,17 Priority Sites
  Jp1,2,3.11
• Years K1,2T T terminal year KK U T1

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Alternative Model -Constraints

• Demand constraints
• Limited land availability constraints

SAME
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Alternative Model - Constraints

• Cost constraints
• Lower bound constraints for priority sites

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To reach optimum solution

• Model is performed in GAMS 22.9

SOLUTION
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RESULT
Objective Function Value
161.09 YTL
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Analysis of Result

• X shows that, site i is leased out in year t
  (yit)
• e.g site 10 is leased out in year 12

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Analysis of GAMS output

• Demand constraint is satisfied!!

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Analysis of GAMS output

• assigned values   maximum amount of available
  land!!!

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Analysis of GAMS output

• minimum level of land is satisfied for priority
  sites!!

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Sensivity Analysis

• If the RHS of demand constraint for year 1 is
  changing,i.e.demand of first year is replaced to
  400
• New Z value can be found by the help of shadow
  price
• Z(new) Z(old) shadow price(marjinal) ?
• ? new RHS value old RHS value

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Sensivity Analysis

• Z new -163439
• Znew -161039 -24(400-300)

For minimum problems if RHS is increased and the
majinal value is negative, objective function is
decreased
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Sensivity Analysis

• If applying similar steps for positive marjinal
  value, then the objective function is increased