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2 Pages

aw gov 2

Course: HISTORY 1101, Fall 2010
School: Georgia State
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Word Count: 304

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Day' 'Pay Article Summary The article "Pay Day: Why it makes sense to worry about executive compensation" by Irwin Stelzer details the federal government's involvement in executive compensation for banks and other companies who received federal bailout money. The appointment of "pay czar" Kenneth Feinberg and his nearly totalitarian power over bonus incentives have the financial...

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Day' 'Pay Article Summary The article "Pay Day: Why it makes sense to worry about executive compensation" by Irwin Stelzer details the federal government's involvement in executive compensation for banks and other companies who received federal bailout money. The appointment of "pay czar" Kenneth Feinberg and his nearly totalitarian power over bonus incentives have the financial community up in arms. Feinberg's decisions, even specific to one particular company, "will essentially serve as an outer boundary for what the law allows." (J. Mark Poerio, financial consultant) Government regulation of executive pay was implemented in order to coerce financial companies towards a less risk-based approach to business. Bonuses are now given out in the form of restricted shares, with longevity in mind. They cannot be sold immediately without losing a large percentage of their value, thus persuading executives to work towards the long term growth and financial of health their respective companies. The plan has financial insiders worried, however, as to the governments lack of expertise on setting compensation restrictions. Also, in companies like CitiGroup, that suffered losses, penalizing the bonuses of the executives may well cause them to leave for better paying jobs. The ailing companies may well need the bigger bonuses, and this is not being addressed by Feinberg and his ilk. The broad issue, it seems, is being overlooked. Restrictions on executive compensation will not fix the overarching problems within the financial sector, which have a basis in the need for excessive risk taking for huge profits. The government is unable or unwilling to push for a complete restructuring of the economic system, and instead is implementing restrictions on individuals income, something that may do more harm than good. Two things are clear on the issue: that something has to be done, and that no one knows yet what that something is.
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data; set ORIG := Coullard Daskin Hazen Hopp Iravani Linetsky Mehrotra Nelson Smilowitz Tamhane White ; set DEST := C118 C138 C140 C246 C250 C251 D237 D239 D241 M233 M239; p param supply default 1 ; p param demand default 1 ; param cost: C118 C138 C140 C2
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#PRODUCTION SETS AND PARAMETERS#s set prod 'products';# Members of the product groupparam pt 'production time' cfw_prod > 0; # Crew-hours to produce 1000 units param pc 'production cost' cfw_prod > 0; # Nominal production cost per 1000, used # to com
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set INPUT; s set OUTPUT;# inputs # outputsparam cost cfw_INPUT > 0; param in_min cfw_INPUT >= 0; p param in_max cfw_j in INPUT >= in_min[j]; param out_min cfw_OUTPUT >= 0; p param out_max cfw_i in OUTPUT >= out_min[i]; p param io cfw_OUTPUT,INPUT >= 0;
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# Blending problem from Section 2.2 in Course Notes # Sunoco Oil makes two types of gasoline using 3 types of crudes. set gas_types; #i set crudes; #j #parameters for the gas_types param min_octanecfw_gas_types >0; param max_sulfurcfw_gas_types >0; param
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data; p param roll_width := 110 ; param: WIDTHS: orders := 20 48 45 35 50 24 55 10 75 8;
Waterloo - CO - 370
# -# CUTTING STOCK USING PATTERNS # -param roll_width > 0; set WIDTHS; p param orders cfw_WIDTHS > 0; param nPAT integer >= 0; s set PATTERNS = 1.nPAT; # width of raw rolls # set of widths to be cut # number of each width to be cut # number of patterns #
Waterloo - CO - 370
model cut.mod; data cut.dat; option solver cplex, solution_round 6; o option display_1col 0, display_transpose -10; problem Cutting_Opt: Cut, Number, Fill; o option relax_integrality 1; problem Pattern_Gen: Use, Reduced_Cost, Width_Limit; o option relax_i
Waterloo - CO - 370
problem Cutting_Opt; # -param nPAT integer >= 0, default 0; p param roll_width; set PATTERNS = 1.nPAT; s set WIDTHS; param orders cfw_WIDTHS > 0; p param nbr cfw_WIDTHS,PATTERNS integer >= 0; check cfw_j in PATTERNS: sum cfw_i in WIDTHS i * nbr[i,j] <= ro
Waterloo - CO - 370
# # # #-GILMORE-GOMORY METHOD FOR CUTTING STOCK PROBLEM -option solver cplex; o option solution_round 6; model cut2.mod; d data cut.dat; problem Cutting_Opt; option relax_integrality 1; option presolve 0; problem Pattern_Gen; option relax_integrality 0;
Waterloo - CO - 370
data; set NUTR := A B1 B2 C ; s set FOOD := BEEF CHK FISH HAM MCH MTL SPG TUR ; param: BEEF CHK FISH HAM MCH MTL SPG TUR param: A C B1 B2 cost 3.19 2.59 2.29 2.89 1.89 1.99 1.99 2.49 n_min 700 700 700 700 f_min 0 0 0 0 0 0 0 0 f_max := 100 100 100 100 100
Waterloo - CO - 370
model diet.mod; d data diet2.dat; param N symbolic in NUTR; param nstart > 0; param nstep > 0; r read N, nstart, nstep <- ; set N_MAX default cfw_; param N_obj cfw_N_MAX; param N_dual cfw_N_MAX; o option solver_msg 0; for cfw_i in nstart . 0 by -nstep cfw
Waterloo - CO - 370
data; set NUTR := A B1 B2 C NA CAL ; s set FOOD := BEEF CHK FISH HAM MCH MTL SPG TUR ; param: BEEF CHK FISH HAM MCH MTL SPG TUR param: A C B1 B2 NA CAL cost 3.19 2.59 2.29 2.89 1.89 1.99 1.99 2.49 n_min 700 700 700 700 0 16000 f_min 2 2 2 2 2 2 2 2 f_max
Waterloo - CO - 370
data; set NUTR := A B1 B2 C NA CAL ; s set FOOD := BEEF CHK FISH HAM MCH MTL SPG TUR ; param: BEEF CHK FISH HAM MCH MTL SPG TUR param: A C B1 B2 NA CAL cost 3.19 2.59 2.29 2.89 1.89 1.99 1.99 2.49 n_min 700 700 700 700 0 16000 f_min 2 2 2 2 2 2 2 2 f_max
Waterloo - CO - 370
data; set MINREQ := A B1 B2 C CAL ; set MAXREQ := A NA CAL ; s set FOOD := BEEF CHK FISH HAM MCH MTL SPG TUR ; param: BEEF CHK FISH HAM MCH MTL SPG TUR f_min 2 2 2 2 2 2 2 2 f_max := 10 10 10 10 10 10 10 10 ;set STORE := "A&P" JEWEL VONS ; # param cost (
Waterloo - CO - 370
set MINREQ; s set MAXREQ;# nutrients with minimum requirements # nutrients with maximum requirements # nutrients # foods # storesset NUTR := MINREQ union MAXREQ; set FOOD; s set STORE;param cost cfw_STORE,FOOD > 0; param f_min cfw_FOOD >= 0; p param f_
Waterloo - CO - 370
data; set MINREQ := A B1 B2 C CAL ; set MAXREQ := A NA CAL ; s set FOOD := BEEF CHK FISH HAM MCH MTL SPG TUR ; param: BEEF CHK FISH HAM MCH MTL SPG TUR param: A C B1 B2 NA CAL cost 3.19 2.59 2.29 2.89 1.89 1.99 1.99 2.49 n_min 700 700 0 0 . 16000 f_min 2
Waterloo - CO - 370
set MINREQ; s set MAXREQ;# nutrients with minimum requirements # nutrients with maximum requirements # nutrients # foodsset NUTR = MINREQ union MAXREQ; s set FOOD;param cost cfw_FOOD > 0; param f_min cfw_FOOD >= 0; p param f_max cfw_j in FOOD >= f_min[
Waterloo - CO - 370
set MINREQ; s set MAXREQ;# nutrients with minimum requirements # nutrients with maximum requirements # nutrients # foodsset NUTR := MINREQ union MAXREQ; s set FOOD;param cost cfw_FOOD > 0; param f_min cfw_FOOD >= 0; p param f_max cfw_j in FOOD >= f_min
Waterloo - CO - 370
#SHIPPING SETS AND PARAMETERS#s set whse 'warehouses';# Locations from which demand is satisfiedset dctr 'distribution centers' within whse; # Locations from which product may be shipped param sc 'shipping cost' cfw_dctr,whse >= 0; # Shipping costs,
Waterloo - CO - 370
#SHIPPING SETS AND PARAMETERS#s set whse 'warehouses';# Locations from which demand is satisfiedset dctr 'distribution centers' within whse; # Locations from which product may be shipped param sc 'shipping cost' cfw_dctr,whse >= 0; # Shipping costs,
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# d data;DATA - 3 PRODUCTS#s set prod := 18REG 24REG 24PRO ; set whse := w01 w02 w03 w04 w05 w06 w08 w09 w12 w14 w15 w17 w18 w19 w20 w21 w24 w25 w26 w27 w28 w29 w30 w31 w32 w33 w34 w35 w36 w37 w38 w39 w40 w41 w42 w43 w44 w45 w46 w47 w48 w49 w50 w51 w53
Waterloo - CO - 370
# -# Data values for kdist model: 8 products # -d data; s set prod := 03111 02915 05710 02857 03012 07310 02600 03905 ; set whse := w01 w02 w18 w19 w32 w33 w44 w45 w57 w59 w72 w73 w84 w85 w w98 x22 w03 w20 w34 w46 w60 w74 w86 x23 w04 w21 w35 w47 w61 w75 w
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# -# Data values for kdist model: 13 products # -d data; set prod := 26179 04911 06015 32219 32318 32417 32292 14811 w5112 07419 04978 32466 16022 ; set whse := w01 w02 w03 w04 w05 w06 w08 w09 w12 w14 w15 w17 w18 w19 w20 w21 w24 w25 w26 w27 w28 w29 w30 w3
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# SHIPPING SETS AND PARAMETERS # set whse 'warehouses'; # Locations from which demand is satisfied set dctr 'distribution centers' within whse; # Locations from which product may be shipped param sc 'shipping cost' cfw_dctr,whse >= 0; # Shipping costs, to
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data; param: ACT: P1 P2 P3 P4 param: PROD: AA1 AC1 BC1 BC2 NA2 NA3 cost 2450 1850 2200 2170 := P1a P2a P3c 1290 3700 2370P2b2150;demand := 70000 80000 90000 70000 400000 800000 ; BC1 10 20 15 35 15 15 30 15 BC2 15 20 10 10 15 15 30 10 NA2 938 1180 945
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set PROD; s set ACT;# products # activities # # # # cost per unit of each activity units of demand for each product units of each product from 1 unit of each activityparam cost cfw_ACT > 0; param demand cfw_PROD >= 0; param io cfw_PROD,ACT >= 0; var Pri
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d data; param: ACT: P1 P1a P2 P2a P2b P3 P3c P4 param: PROD: AA1 AC1 BC1 BC2 NA2 NA3 cost 2450 1290 1850 3700 2150 2200 2370 2170 level_min 240 270 220 260 200 260 220 240 level_max := 1000 1000 1000 1000 1000 1000 1000 1000 ;demand := 70000 80000 90000
Waterloo - CO - 370
set PROD; s set ACT;# products # activities # # # # cost per unit of each activity units of demand for each product units of each product from 1 unit of each activityparam cost cfw_ACT > 0; param demand cfw_PROD >= 0; param io cfw_PROD,ACT >= 0;param l
Waterloo - CO - 370
set PROD; s set ACT;# products # activitiesparam cost cfw_ACT > 0; # cost per unit of each activity param demand cfw_PROD >= 0; # units of demand for each product p param io cfw_PROD,ACT >= 0; # units of each product from 1 unit of each activity param l
Waterloo - CO - 370
set PROD; s set ACT;# products # activities cost per unit of each activity units of demand for each product units of each product from 1 unit of each activityparam cost cfw_ACT > 0; # param demand cfw_PROD >= 0; # param io cfw_PROD,ACT >= 0; # # var Lev
Waterloo - CO - 370
data; param: ACT: P1 P1a P2 P2a P2b P3 P3c P4 param: PROD: AA1 AC1 BC1 BC2 NA2 NA3 cost := 2450 1290 1850 3700 2150 2200 2370 2170 ; demzero 70000 80000 90000 70000 400000 800000 BC1 10 20 15 35 15 15 30 15 demrate := 500 500 500 500 500 500 ; BC2 15 20 1
Waterloo - CO - 370
set PROD; s set ACT;# products # activities # cost per unit of each activity # units of each product from # 1 unit of each activityparam cost cfw_ACT > 0; param io cfw_PROD,ACT >= 0;param demzero cfw_PROD > 0; # intercept and slope of the demand p para
Waterloo - CO - 370
# data for egypt1.x d data; s set center := ASWAN HELWAN ASSIOUT KAFR_EL_ZT ABU_ZAABAL ABU_KIR TALKHA SUEZ ; s set port := ABU_KIR ; s set plant := ASWAN HELWAN ASSIOUT KAFR_EL_ZT ABU_ZAABAL ; set region := ALEXANDRIA BEHERA GHARBIA KAFR_EL_SH DAKAHLIA DA
Waterloo - CO - 370
# = # EGYPT: GAMS Egyptian Fertilizer Model # = # ORDERED PAIRS VERSION # Source: "On the Development of a General Algebraic Modeling # System in a Strategic Planning Environment" by Johannes Bisschop # and Alexander Meeraus # -# Sets # -s set center; s s
Waterloo - CO - 370
# EGYPT data from June 1989 version of CSTR 133 # # d data; s set center := ASWAN HELWAN ASSIOUT KAFR_EL_ZT ABU_ZAABAL ABU_KIR TALKHA SUEZ ; s set port := ABU_KIR ; s set plant := ASWAN HELWAN ASSIOUT KAFR_EL_ZT ABU_ZAABAL ; set region := ALEXANDRIA BEHER
Waterloo - CO - 370
# Model EGYPT from June 1989 version of CSTR 133 # This static production model, originally stated in the GAMS language #(Bisschop and Meeraus 1982), is based on a World Bank study of the # #Egyptian fertilizer industry (Choksi, Meeraus and Stoutjesdijk 1
Waterloo - CO - 370
var x1; var x2; var x3; var x4; var x5; var x6; var x7; var x8; minimize Expense: +(-6)*x1+(-9)*x2+(12)*x3+(-14)*x4+(-23)*x5+(5)*x6+(-18)*x7 +(2)*x8; subject to T1: +(-3)*x1+(5)*x2+(3)*x3+(4)*x4+(-2)*x5+(2)*x6+(9)*x7 <= -6; subject to T2: +(5)*x1+(2)*x2+(
Waterloo - CO - 370
# example 3 data param warehouse := 3; param customer:= 4; param cost: 1 1 1 2 2 3 5 3 2 2 param supply:= 1 250 param demand:= 1 300 22 3 4:= 1 3 1 4 2 2; 2 800 3 760; 320 3 800 4 390;
Waterloo - CO - 370
# example 3 data param warehouse := 3;param cost:= 4;param cost: 1234:=11 2132351432222;param supply:= 1 2502 8003 760;para demand:= 1 300 2 320 3 800 4 390;
Waterloo - CO - 370
# two dimension example:param warehouse;param customer; # other parametersparam costcfw_ i in 1.warehouse,j in 1.customer;param supplycfw_i in 1.warehouse;param demandcfw_ j in 1.customer;var xcfw_i in 1.warehouse, j in 1.customer;minimize Cost:
Waterloo - CO - 370
# data for example from tutorial page 9; file is integprogr1.dat set Warehouses:= Oakland San_Jose Albany; set Customers:= Home_Depot K_mart Wal_mart Ace; param cost: Oakland San_Jose Albany Home_Depot K_mart Wal_mart Ace:= 1 2 1 3 3 5 1 4 2 2 2 2;param
Waterloo - CO - 370
# example from tutorial page 9; file is integprogr1.mod set Warehouses; set Customers; param param param param costcfw_i in Warehouses, j in Customers; supplycfw_i in Warehouses; # supply at Warehouse i demandcfw_j in Customers; # demand at customer j fix
Waterloo - CO - 370
set MAT; set ACT; p param io cfw_MAT,ACT;# materials # activities # input-output coefficientsparam revenue cfw_ACT; param act_min cfw_ACT >= 0; p param act_max cfw_j in ACT >= act_min[j]; m maximize Net_Profit; s subject to Balance cfw_i in MAT: to_come
Waterloo - CO - 370
set MAT; s set ACT; p param io cfw_MAT,ACT;# materials # activities # input-output coefficientss set MATF within MAT; # finished materials p param revenue cfw_MATF >= 0; param sell_min cfw_MATF >= 0; p param sell_max cfw_i in MATF >= sell_min[i]; param
Waterloo - CO - 370
set MAT; set ACT; p param io cfw_MAT,ACT;# materials # activities # input-output coefficientsparam revenue cfw_ACT; param act_min cfw_ACT >= 0; p param act_max cfw_j in ACT >= act_min[j]; v var Run cfw_j in ACT >= act_min[j], <= act_max[j]; m maximize N
Waterloo - CO - 370
set nodes := s a b c d e t ; param orig := s ; p param dest := t ; param: arcs: sa ab ba ce cap := 1 sb 3 ad 2 bd 4 dt 4 4 3 9 sc be et 6 1 4;
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set nodes; param orig symbolic in nodes; p param dest symbolic in nodes, <> orig; s set arcs within (nodes diff cfw_dest) cross (nodes diff cfw_orig); param cap cfw_arcs >= 0; v var Flow cfw_(i,j) in arcs >= 0, <= cap[i,j]; m maximize Total_Flow: sum cfw_
Waterloo - CO - 370
set PEOPLE; s set PROJECTS; param supply cfw_PEOPLE >= 0; # hours each person is available p param demand cfw_PROJECTS >= 0; # hours each project requires check: sum cfw_i in PEOPLE supply[i] = sum cfw_j in PROJECTS demand[j]; param cost cfw_PEOPLE,PROJEC
Waterloo - CO - 370
data; set P := lite bud mich ; s set R := malt hops ; param T 3 ; param M 40 ; param a [malt,lite] 5 [malt,bud] 3 [malt,mich] 1 [hops,lite] 1 [hops,bud] 2 [hops,mich] 3 ; param b [malt] 400 [hops] 275 ; param c [lite,1] 25 [lite,2] 20 [lite,3] 10 [bud,1]
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# From Bob Fourer's TOMS paper, June 1983 # # # # # # # # # # # # # # # # A factory can manufacture some number of different products over the next T production periods. Each product returns a characteristic estimated profit per unit, which varies from pe
Waterloo - CO - 370
# AMPL model for an MPS file: this is short, but it may # change the row order (which could affect pivot choice # by simplex-based solvers). # Use the awk script "m2a" to turn an MPS file into suitable data. set Aij dimen 2; #constraint matrix indices set
Waterloo - CO - 370
# AMPL model for an MPS file: this one preserves row order. # Use the awk script "m2a" to turn an MPS file into suitable data. set Aij dimen 2; #constraint matrix indices set I1; # to allow empty rows set J := setofcfw_(i,j) in Aij j; #columns param Acfw_
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# # # #AMPL model for an MPS file with 'MARKER' lines indicating integer variables: like mps.mod, this is short, but it may change the row order (which could affect pivot choice by simplex-based solvers).# Use the awk script "m2ai" to turn an MPS file i
Waterloo - CO - 370
data; set ORIG := GARY CLEV PITT ; set DEST := FRA DET LAN WIN STL FRE LAF ; s set PROD := bands coils plate ; param supply (tr): bands coils plate param demand (tr): FRA DET bands 300 300 coils 500 750 plate 100 100 GARY 400 800 200 LAN 100 400 0 CLEV 70
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set ORIG; set DEST; s set PROD;# origins # destinations # products # amounts available at origins # amounts required at destinationsparam supply cfw_ORIG,PROD >= 0; p param demand cfw_DEST,PROD >= 0;check cfw_p in PROD: sum cfw_i in ORIG supply[i,p] =