#Nasledujúci textoví súbor obsahuje zdrojové kody k obom uloham riesenym v bakalarskej praci.
#Tieto kody su urcene pre program AMPL, pricom kazda uloha obashuju tri zdrojove kody s koncovkami .run .mod .dat

#Zaciatok ulohy 1------------------------------------------------------------------------------------------------------------------------------------------------


Uloha1.run

reset;
model Uloha1.mod;
data Uloha1.dat; 
option solver "D:\ampl\ampl_mswin64 (1)\xpress"; #treba tu pridat spravnu cestu k binarnemu solveru
solve;
display y;
display x;
display r;
display t;
display p;



#--------------------------------------------------------------------------------------------------------------------------------------------------------------------

Uloha1.mod

set BV; #Boundary vertices - zmnozina zaciatocneho a konecneho vrcholou
set IV; #Internal vertices - vn
set V := IV union BV; #vsetky vrcholy

param D {V, V} >= 0; # matica nominalnych vydialenosti
param E {V, V} >= 0; # matica ktora ma 1 ak existuje hrana medzi i a j
param C {V, V} >= 0; #matica ceny z miernenie neistoty
param Delta >= 0;  # konstanta urcujuca maximalny sucet prvkov vekotru xi
param DecEff >= 0; # DecisionEfficiency efektivita rozhodnutia ktora pre x[i,j]=1
					# meni maximalnu hodnotu xi[i,j] z 1 na 1-x[i,j]*DecEff
param  LinCost >= 0; #konstanta urcujuca linearny vytah medzi cenou a dlzkou hrany 
param  fixedPrice >= 0;  #cena za znizenie neistoty kazdej hrany						

var y {i in V, j in V} binary;  # matica urcujuca cestu
var x {i in V, j in V} binary;  # nase rozhodnutia zmiernit neisotu
var t {i in V, j in V} >= 0;    # nasledujuce tri premenne v clanku spadli z neba 
var r {i in V, j in V} >= 0;    # a nedokazem ich rozumne reprezentovat
var p >= 0 ;
 
minimize totalCost: #ucelova funkcia
    sum {i in V, j in V} y[i, j] * D[i, j]+ 
	sum {i in V, j in V} x[i, j] * ((C[i,j]*LinCost)+fixedPrice)+
	sum {i in V, j in V} (t[i,j] *(DecEff))+
	sum {i in V, j in V} (r[i,j] * (1-DecEff))+
	p*Delta;
	
subject to firstNode:  # tieto tri podmienky su podmienky predchodov 
	sum {j in V} y['V1', j] - sum {i in V} y[i, 'V1'] = 1;	
subject to lastNode:    # a naslednikov v grafe
	sum {j in V} y['V5', j] - sum {i in V} y[i, 'V5'] = -1;	
subject to everyNode {v in IV}:
sum {j in V} y[v, j] - sum {i in V} y[i, v] = 0;

#nasledujuce dve podmienky su podm. pre formulaciu robustnej najkratsej cesty
subject to everyNodeConstraint1 {i in V, j in V}:
(p+t[i,j]) >= ((y[i,j]*D[i,j]-x[i,j]*D[i,j])/2);
subject to everyNodeConstraint2 {i in V, j in V}:
(p+r[i,j]) >=((y[i,j]*D[i,j])/2);


#subject to X: ak pridame tuto pod. dostavame zadanie podla prvej kapitoly
#sum {i in V, j in V} x[i,j] <=1;


#--------------------------------------------------------------------------------------------------------------------------------------------------------------------

Uloha1.dat

set IV := V2	V3	V4	V6; #Boundary vertices - zmnozina zaciatocneho a konecneho vrcholou
set BV := V1	V5; #Boundary vertices - mnozina zaciatocneho a konecneho vrcholou

param D: 		V1		V2		V3		V4			V5			V6 :=
			V1	 0		30		25		100000		100000		95		
			V2	30		0		4		40			100000		100000
			V3	25		4		0		40			70			100000		
			V4	100000 40		40		0			37			100000
			V5	100000 100000 	70		37			0			5
			V6	95		100000	 100000 100000 		5  			0;
			   #matica vzdialenosti medzi vrcholmi grafu

param C: 		V1		V2		V3		V4			V5			V6 :=
			V1	 0		30		25		100000		100000		95		
			V2	30		0		4		40			100000		100000
			V3	25		4		0		40			70			100000		
			V4	100000 40		40		0			37			100000
			V5	100000 100000 	70		37			0			5
			V6	95		100000	 100000 100000 		5  			0;
			#skopirovana matica vzdialenosti pre pripad kedy cena za znizenie neisoty je lin. funkciou jej dlzky		
param Delta:=1;
param DecEff:=0.8;
param fixedPrice:=10; #cena za znizenie neistoty kazdej hrany	
param LinCost:=0; #konstanta urcujuca linearny vytah medzi cenou a dlzkou hrany	


#koniec kodu k ulohe 1------------------------------------------------------------------------------------------------------------------------------------------------------------

#Nasleduje zdrojový kod k druhej uloha o prazkej mestkej hromadnej doprave--------------------------------------------------------------------------------------------------------


		


Praha.run

reset;
model Praha.mod;
data Praha.dat; 
option solver "D:\ampl\ampl_mswin64 (1)\xpress";  #treba tu pridat spravnu cestu k binarnemu solveru
solve;
display y;
display x;

#--------------------------------------------------------------------------------------------------------------------------------------------------------------------

Praha.dat

set IV := V1	V2	V3	V5	V6 	V7	V8	V9	V10	V11	V13	; #Internal vertices - vnutorne vrcholy grafu
set BV := V4	V12;#Boundary vertices - mnozina zaciatocneho a konecneho vrcholou


param D: 		V1		V2		V3		V4		V5		V6 		V7		V8		V9		V10		V11		V12		V13:=
		 	V1	0		210		250		10000	10000	10000	10000	10000	10000	10000	10000	10000	10000 #Florenc	
			V2	210		0		470		10000	140		10000	10000	10000	10000	10000	10000	10000	10000 #Hl. nadrazi
			V3	250		470		0		150		10000	10000	10000	10000	10000	10000	10000	10000	10000 #Masarikovo nadrazi			
			V4	10000	10000	150		0		10000	10000	355		10000	10000	10000	10000	10000	10000 #Olsanske nam
			V5	0		140		10000	10000	0		80		10000	145		270		10000	10000	10000	10000	   #Muzeum
			V6	10000	10000	10000	10000	80		0		320		230		10000	10000	10000	10000	650# Jir z Podebrad	
			V7	10000	10000	10000	355		10000	320		0		10000	10000	10000	10000	10000	10000 #Flora
			V8	10000	10000	10000	10000	145		230		10000	0		120		10000	10000	10000	10000 #Nam mieru
			V9	10000	10000	10000	10000	270		10000	10000	120		0		90		400		10000	260# I.P. Pavlova	
			V10	10000	10000	10000	10000	10000	10000	10000	10000	90		0		305		10000	10000 # Vysehrad
			V11	10000	10000	10000	10000	10000	10000	10000	10000	400		305		0		120		10000 #Karlovo namieste
			V12	10000	10000	10000	10000	10000	10000	10000	10000	10000	10000	120		0		250	   #Nar. trieda
			V13	10000	10000	10000	10000	10000	650		10000	10000	260		10000	10000	250		0;  #Mustek
   #matica vzdialenosti medzi vrcholmi grafu

	
param C: 		V1		V2		V3		V4		V5		V6 		V7		V8		V9		V10		V11		V12		V13:=
		 	V1	0		150		210		10000	10000	10000	10000	10000	10000	10000	10000	10000	10000 #Florenc	
			V2	150		0		400		10000	140		10000	10000	10000	10000	10000	10000	10000	10000 #Hl. nadrazi
			V3	210		400		0		250		10000	10000	10000	10000	10000	10000	10000	10000	10000 #Masarikovo nadrazi			
			V4	10000	10000	250		0		10000	10000	350		10000	10000	10000	10000	10000	10000 #Olsanske nam
			V5	0		140		10000	10000	0		150		10000	160		270		10000	10000	10000	10000	   #Muzeum
			V6	10000	10000	10000	10000	150		0		390		250		10000	10000	10000	10000	800# Jir z Podebrad	
			V7	10000	10000	10000	350		10000	390		0		10000	10000	10000	10000	10000	10000 #Flora
			V8	10000	10000	10000	10000	160		250		10000	0		100		10000	10000	10000	10000 #Nam mieru
			V9	10000	10000	10000	10000	270		10000	10000	100		0		90		400		10000	600# I.P. Pavlova	
			V10	10000	10000	10000	10000	10000	10000	10000	10000	90		0		350		10000	10000 # Vysehrad
			V11	10000	10000	10000	10000	10000	10000	10000	10000	400		350		0		180		10000 #Karlovo namieste
			V12	10000	10000	10000	10000	10000	10000	10000	10000	10000	10000	180		0		240	   #Nar. trieda
			V13	10000	10000	10000	10000	10000	800		10000	10000	600		10000	10000	240		0;  #Mustek
	#skopirovana matica vzdialenosti pre pripad kedy cena za znizenie neisoty je lin. funkciou jej dlzky		
			
param Delta:=5;   #velka delta urcujuca hornu medzu pre sucet neistoty
param DecEff:=0.8;  #mala delta urcujuca efektivitu rozhodnuti	
param fixedPrice:=40; #cena za znizenie neistoty kazdej hrany	


#--------------------------------------------------------------------------------------------------------------------------------------------------------------------

Praha.mod

set BV; #Boundary vertices - mnozina zaciatocneho a konecneho vrcholou
set IV; #Internal vertices - vn
set V := IV union BV; #vsetky vrcholy

param D {V, V} >= 0; # matica nominalnych vydialenosti
param C {V, V} >= 0; #matica ceny zmensenia neistoty
param Delta >= 0;  # konstanta urcujuca maximalny sucet prvkov vekotru xi
param DecEff >= 0; # DecisionEfficiency efektivita rozhodnutia ktora pre x[i,j]=1
					# meni maximalnu hodnotu xi[i,j] z 1 na 1-x[i,j]*DecEff
param  LinCost >= 0; #konstanta urcujuca linearny vytah medzi cenou a dlzkou hrany 
param  fixedPrice >= 0;  #cena za znizenie neistoty kazdej hrany					

var y {i in V, j in V} binary;  # matica urcujuca cestu
var x {i in V, j in V} binary;  # nase rozhodnutia zmiernit neisotu
var t {i in V, j in V} >= 0;    
var r {i in V, j in V} >= 0;    
var p >= 0 ;
 
minimize totalCost:
    sum {i in V, j in V} y[i, j] * D[i, j]+ 
	sum {i in V, j in V} x[i, j] * fixedPrice+
	sum {i in V, j in V} (t[i,j] *(DecEff))+
	sum {i in V, j in V} (r[i,j] * (1-DecEff))+
	p*Delta;
	
subject to firstNode:  # tieto tri podmienky su podmienky predchodcov naslednikou 
	sum {j in V} y['V4', j] - sum {i in V} y[i, 'V4'] = 1;	
subject to lastNode:    # a zarucuju ze y je cesta v grafe
	sum {j in V} y['V12', j] - sum {i in V} y[i, 'V12'] = -1;	
subject to everyNode {v in IV}:
sum {j in V} y[v, j] - sum {i in V} y[i, v] = 0;

#nasledujuce dve podmienky su podm. pre formulaciu robustnej najkratsej cesty
subject to everyNodeConstraint1 {i in V, j in V}:
(p+t[i,j]) >= ((y[i,j]*D[i,j]-x[i,j]*D[i,j])/2);
subject to everyNodeConstraint2 {i in V, j in V}:
(p+r[i,j]) >=((y[i,j]*D[i,j])/2);

#koniec kodu --------------------------------------------------------------------------------------------------------------------------------------------------------------------