guillermo durán rafael epstein gonzalo zamorano
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Quantitative Methods for a New Configuration of Territorial Units in a Chilean Government Agency Tender Process. Universidad de Chile Universidad de Buenos Aires. Guillermo Durán Rafael Epstein Gonzalo Zamorano. Cristian Martínez. former Chilean Vice- Minister of Education - PowerPoint PPT PresentationTRANSCRIPT
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Quantitative Methods for a New Configuration of Quantitative Methods for a New Configuration of Territorial Units in a Chilean Government Agency Territorial Units in a Chilean Government Agency
Tender ProcessTender Process
Guillermo DuránRafael Epstein
Gonzalo Zamorano
formerChilean Vice-Minister of Education
(Jan2008-March2010)
Universidad de Chile Universidad de Buenos Aires
Cristian Martínez
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Daily reaches 2 million children, from 0 to 24 years
11,000 schools along the country (4200 Km)
Private catering firms bid on supply contracts
US$600 million a year
The largest procurement process in ChileThe largest procurement process in Chile
School Meals ProgramSchool Meals Program
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Territorial Units (TUs)Territorial Units (TUs)
CHILE: 13 regions13 regions
136 TUs136 TUs
UT 1 UT
2UT 4
UT 3
UT 5
UT 6
UT 7
UT 8
UT 9
REGION 1
REGION 2
REGION 13
UT 135
CHILE
UT 136
UT 133
UT 134
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Combinatorial AuctionCombinatorial Auction
In 1997 a Combinatorial Auction is implemented• Milgrom P., Putting Auction Theory to Work,
2007, Cambridge University Press. • Cramton P., Shoham Y., Steinberg R. (editors), Combinatorial
Auctions, 2006, The MIT Press.
Each year, 1/3 of all TUs is contracted
Bids provide services for 3 years
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Combinatorial Auction BenefitsCombinatorial Auction Benefits
Cost synergies are reflected on the bids
Economies of scale Volume discounts in purchasing inputs
Economies of density Logistic savings when serving nearby units
US$3 billion awarded with this model
Cost reduction reported: 22%
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A New Challenge: BankruptcyA New Challenge: Bankruptcy
Five firms declared bankruptcy between 2004 and Five firms declared bankruptcy between 2004 and 2007 leading to serious financial and social losses for 2007 leading to serious financial and social losses for
the governmentthe government
Bankruptcy is a big problem: Meal service is interrupted, affecting the educational process Restoring service in affected TUs is more expensive Bankruptcies eliminate an actor from the market
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Bankruptcies CausesBankruptcies Causes
1° Cause: Price War among firms Tender system promotes competition Companies drop their prices to eliminate competitors After the war the prices tend to increase, higher than before
Prices Band Try to identify the abnormal low prices Eliminate these bids from the process
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Bankruptcies CausesBankruptcies Causes
2° Cause: Limited liability Aggressive bids on TUs with unknown operating conditions
When the real operation is good: The firm starts a successful business
When the real operation is unsustainable: The firm assumes the private costs JUNAEB assumes the rest of the costs
• Social costs, costly small auctions, less competitive market
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Uncertainty ProblemUncertainty Problem
Some TUs offer worse operating conditions
Firms don’t properly estimate its costs in these TUs
Firms offer low prices on “bad” TUs
The bidding process selects some of them
After that, they may not fulfill their contracts
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Proposed SolutionProposed Solution
Redesign TUs configuration
Avoid “bad conditioned” TUs
Homogenize operating conditions of TUs
Reduce the global risk of the system
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Using OR to Reconfigure TUs
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Comunas The smallest administrative units 346 comunas in Chile
Chilean Geographical DivisionChilean Geographical Division
Territorial Units Groups of comunas
Firms bid over the whole TU 136 TUs
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Number of meals: magnitude of the contract
Number of schools: fixed costs of supply
Area covered (in km2): transport costs
% Inaccessible schools: geographical conditions
Attractiveness Index of TUAttractiveness Index of TU
Number of Schools
Total Area
Accessibility
Number of meals
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Attractiveness Category Territorial Units (TU Codes) Total TUs
Below Average 101, 1004, 1007, 1005, 1010, 302, 609, 803, 805, 1101 10
Average 102, 301, 605 3Above
Average 606, 607, 1321 3
Bankruptcy: relatively “bad” TUBankruptcy: relatively “bad” TU
TUs involved in recent bankruptcies
Central Purpose:Reconfigure the Comunas into homogeneous TUs
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Based on the Analytic Hierarchy Process (Saaty, 1980)
The Attractiveness IndexThe Attractiveness Index
total attractiveness score for TU j in region r.importance of number of meals within the set of criteria weight of TU j in region r on number of meals criterionimportance of number of schools within the set of criteriaweight of TU j in region r on number of schools criterionimportance of size of area of TU within the set of criteriaweight of TU j in region r on area criterionimportance of type of access to school within the set of criteriaweight of TU j in region r on accessibility criterion
rjU ,
MarjMx ,,
rjSx ,,
ArcrjArx ,,
AcdrjAcx ,,
Sb
rjAcAcrjArArrjSSrjMMrj xdxcxbxaU ,,,,,,,,,
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Importance of each characteristicImportance of each characteristic
rjAcAcrjArArrjSSrjMMrj xdxcxbxaU ,,,,,,,,,
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Importance of each characteristicImportance of each characteristic
rjAcAcrjArArrjSSrjMMrj xdxcxbxaU ,,,,,,,,,
xM,j,r xS,j,r xAR,j,r xAC, j, r
calculated by using statistical data
Example: Region has 2 TUs UT1 = 40,000 meals UT2 = 60,000 meals Then xM,1,r =40 and xM,2,r=60
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Method 1: Local Search HeuristicMethod 1: Local Search Heuristic
Objective: minimize the standard deviation, this is, the dispersion of TU attractiveness levels in a region
Create initial solution for every possible number of TUs
Comunas are exchanged between TUs in a Region
In each iteration, only one comuna is exchanged
The best local optimum is selected
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Local Search HeuristicLocal Search Heuristic0. Initial Solution
1. First Iteration
N. After N iterations,
final solution
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Local Search Heuristic, example Local Search Heuristic, example
Lower bound fixed per UT: 15,000
Upper bound fixed per UT: 40,000
This region may have 2 or 3 TUs
Table of Characteristics: 1st RegionRegion Comunas Meals Schools Area (km²) Easy access Difficult access
Arica 25,726 62 4,799 62 0Camarones 54 8 3,927 7 1
Putre 248 6 5,903 6 0General Lagos 180 9 2,244 9 0
Iquique 9,155 38 2,262 38 0Alto Hospicio 11,387 25 573 25 0
Huara 460 12 10,475 12 0Camiña 404 9 2,200 9 0
Colchane 252 5 4,016 5 0Pica 799 5 8,934 5 0
Pozo Almonte 1,685 10 13,766 10 0Total 50,350 189 59,099 188 1
1st Region
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Example, starting with Example, starting with 2 UTs2 UTs TU 1: 55.9% TU 2: 44.1% TU 1: 51.4% TU 2: 48.6% TU 1: 49.7% TU 2: 50.3%
Arica A Iquique Arica A Iquique Arica A IquiqueArica B Pozo Almonte Arica B Pozo Almonte Arica B Pozo AlmonteArica C Huara Arica C Pica Arica C PicaGeneral Lagos Pica General Lagos Alto Hospicio A General Lagos Alto Hospicio APutre Alto Hospicio A Putre Alto Hospicio B Putre Alto Hospicio BCamarones Alto Hospicio B Camarones Camiña Camarones Colchane
Camiña Huara Colchane HuaraColchane Camiña
Std. Deviation 5.9261 Std. Deviation 1.3646 Std. Deviation 0.3324
Iteration 1TU 1: 55.9% TU 2: 44.1% TU 1: 51.4% TU 2: 48.6% TU 1: 49.7% TU 2: 50.3%
Arica A Iquique Arica A Iquique Arica A IquiqueArica B Pozo Almonte Arica B Pozo Almonte Arica B Pozo AlmonteArica C Huara Arica C Pica Arica C PicaGeneral Lagos Pica General Lagos Alto Hospicio A General Lagos Alto Hospicio APutre Alto Hospicio A Putre Alto Hospicio B Putre Alto Hospicio BCamarones Alto Hospicio B Camarones Camiña Camarones Colchane
Camiña Huara Colchane HuaraColchane Camiña
Std. Deviation 5.9261 Std. Deviation 1.3646 Std. Deviation 0.3324
Iteration 2TU 1: 55.9% TU 2: 44.1% TU 1: 51.4% TU 2: 48.6% TU 1: 49.7% TU 2: 50.3%
Arica A Iquique Arica A Iquique Arica A IquiqueArica B Pozo Almonte Arica B Pozo Almonte Arica B Pozo AlmonteArica C Huara Arica C Pica Arica C PicaGeneral Lagos Pica General Lagos Alto Hospicio A General Lagos Alto Hospicio APutre Alto Hospicio A Putre Alto Hospicio B Putre Alto Hospicio BCamarones Alto Hospicio B Camarones Camiña Camarones Colchane
Camiña Huara Colchane HuaraColchane Camiña
Std. Deviation 5.9261 Std. Deviation 1.3646 Std. Deviation 0.3324
Initial Situation
Std. Dev = 5.93 Std. Dev = 1.33 Std. Dev = 0.33
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Example, starting with Example, starting with 3 UTs3 UTs
TU 1: 38.05% TU 2: 30.37% TU 3: 31.58% TU 1: 35.69% TU 2: 32.35% TU 3: 31.96% TU 1: 33.05% TU 2: 32.43% TU 3: 34.52%Arica A Arica C Alto Hospicio B Arica A Arica C Alto Hospicio B Arica A Arica C Alto Hospicio BArica B Camarones Iquique Arica B Camarones Iquique Arica B Camarones IquiqueGeneral Lagos Huara Pozo Almonte General Lagos Huara Pozo Almonte General Lagos Huara Pozo AlmontePutre Camiña Pica Putre Camiña Pica Putre Camiña
Colchane Colchane Alto Hospicio A Colchane Alto Hospicio AAlto Hospicio A Pica
Std. Deviation 3.3722 Std. Deviation 1.6709 Std. Deviation 0.8785
Iteration 2TU 1: 38.05% TU 2: 30.37% TU 3: 31.58% TU 1: 35.69% TU 2: 32.35% TU 3: 31.96% TU 1: 33.05% TU 2: 32.43% TU 3: 34.52%
Arica A Arica C Alto Hospicio B Arica A Arica C Alto Hospicio B Arica A Arica C Alto Hospicio BArica B Camarones Iquique Arica B Camarones Iquique Arica B Camarones IquiqueGeneral Lagos Huara Pozo Almonte General Lagos Huara Pozo Almonte General Lagos Huara Pozo AlmontePutre Camiña Pica Putre Camiña Pica Putre Camiña
Colchane Colchane Alto Hospicio A Colchane Alto Hospicio AAlto Hospicio A Pica
Std. Deviation 3.3722 Std. Deviation 1.6709 Std. Deviation 0.8785
Iteration 1TU 1: 38.05% TU 2: 30.37% TU 3: 31.58% TU 1: 35.69% TU 2: 32.35% TU 3: 31.96% TU 1: 33.05% TU 2: 32.43% TU 3: 34.52%
Arica A Arica C Alto Hospicio B Arica A Arica C Alto Hospicio B Arica A Arica C Alto Hospicio BArica B Camarones Iquique Arica B Camarones Iquique Arica B Camarones IquiqueGeneral Lagos Huara Pozo Almonte General Lagos Huara Pozo Almonte General Lagos Huara Pozo AlmontePutre Camiña Pica Putre Camiña Pica Putre Camiña
Colchane Colchane Alto Hospicio A Colchane Alto Hospicio AAlto Hospicio A Pica
Std. Deviation 3.3722 Std. Deviation 1.6709 Std. Deviation 0.8785
Initial Situation
Std. Dev = 3.37 Std. Dev = 1.67 Std. Dev = 0.88
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2 TUs final solutionStd. Dev = 0,33
3 TUs final solutionStd. Dev = 0,88
The 2 TUs solution was selectedThe 2 TUs solution was selected
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Method 2: Integer ProgrammingMethod 2: Integer Programming
Objective: minimize the difference between the most and least attractive TU in each region
Good linearization of minimize the standard deviation
Algorithm generates all possible TUs, called clusters
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Integer Programming ModelInteger Programming Model
Formulation
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Integer Programming ModelInteger Programming Model
Formulation
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Results Standard DeviationResults Standard Deviation
RegionStandard Deviation Improvement
Original Heuristic Model O v/s H O v/s MI 5,93 0,33 0,29 94% 95%II 0,18 0,18 0,18 0% 0%III 0,74 0,19 0,19 75% 75%IV 5,94 1,81 0,88 69% 85%V 1,47 0,68 0,99 53% 33%VI 3,27 0,47 0,16 86% 95%VII 4,05 0,41 0,53 90% 87%VIII 2,44 0,60 0,87 75% 64%IX 3,90 0,40 0,03 90% 99%X 1,04 0,84 0,88 19% 15%
Both final solutions improve standard deviation on the situation prevailing before 2007
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Results Gap Results Gap (most and least attractive (most and least attractive TU )TU )
RegionDifferences (max - min) Improvement
Original Heuristic Model O v/s H O v/s MI 12,70 0,55 0,04 96% 100%II 0,20 0,20 0,20 0% 0%III 2,55 1,02 1,02 60% 60%IV 46,49 3,36 1,58 92% 97%V 12,62 4,82 1,26 62% 88%VI 40,84 2,58 0,37 94% 99%VII 63,48 1,14 0,17 98% 98%VIII 72,86 2,87 2,49 96% 96%IX 47,09 1,37 0,13 97% 100%X 3,81 2,51 2,25 34% 41%
Both final solutions improve gap (max-min) on the situation prevailing before 2007
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JUNAEB used our solutionJUNAEB used our solution
The configuration was adopted by JUNAEB in 2007
Since 2007, no bankruptcies have occurred
Accepted for publication in Interfaces“Quantitative Methods for a New Configuration of Territorial Units in a Chilean Government Agency Tender Process”, Guillermo Durán, Rafael Epstein, Gonzalo Zamorano, Cristian Martínez
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Improving Public Policies
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Education and OpportunitiesEducation and Opportunities
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Efficiency and QualityEfficiency and Quality
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Quantitative Methods for a New Configuration of Quantitative Methods for a New Configuration of Territorial Units in a Chilean Government Agency Territorial Units in a Chilean Government Agency
Tender ProcessTender Process
Guillermo DuránRafael Epstein
Gonzalo Zamorano
formerChilean Vice-Minister of Education
(Jan2008-March2010)
Universidad de Chile Universidad de Buenos Aires
Cristian Martínez