#using for the 1st time the package

install.packages("linprog") 

#using the package
library(linprog)

#inserting parameters
#profit parameters from objective function
cvec=c(40,100) 
##quantity parameters from the st (right side)
bvec=c(20,12) 
#matrix for decision variable coefficients from the constraints in the order they appear
A=matrix(c(2,4,1,3),nrow=2,ncol=2,byrow=T)

#Can solve this with geogebra 

#solving our problem 
LP<-solveLP(cvec,bvec,A,maximum=T, const.dir=rep("<=", length(bvec)))
#Showing results
summary(LP) #print(LP)




#increase of A
cvec=c(40,100) 
#before i had this values for the machines bvec=c(20,12) 
bvec=c(21,12) 
A=matrix(c(2,4,1,3),nrow=2,ncol=2,byrow=T)
LP1<-solveLP(cvec,bvec,A,maximum=T, const.dir=rep("<=", length(bvec)))
#print(LP1)
summary(LP1) 

#shadow price
dx1 =7.5-6 
dx2 =1.5-2
shadow.price1=40*dx1+100*dx2 
shadow.price1

#if i want to compare the objective function values from before and now (+1H of MA)
summary((LP1))

#decreasee of A
cvec=c(40,100) 
bvec= c(19,12) 
A=matrix(c(2,4,1,3),nrow=2,ncol=2,byrow=T)
LP2 =solveLP(cvec,bvec,A,maximum=T, const.dir=rep("<=", length(bvec)))
summary(LP2)

#shadow price
dx1 = 4.5-6 
dx2 = 2.5-2
shadow.price2=40*dx1+100*dx2 
shadow.price2

#increase of B
cvec=c(40,100) 
bvec=c(20,13) 
A=matrix(c(2,4,1,3),nrow=2,ncol=2,byrow=T)
LP3=solveLP(cvec,bvec,A,maximum=T, const.dir=rep("<=", length(bvec)))
summary(LP3)

#shadow price
dx1 = 4-6 
dx2 = 3-2
shadow.price3=40*dx1+100*dx2 
shadow.price3


#decrease of B
cvec=c(40,100) 
bvec=c(20,11) 
A=matrix(c(2,4,1,3),nrow=2,ncol=2,byrow=T)
LP4=solveLP(cvec,bvec,A,maximum=T, const.dir=rep("<=", length(bvec)))
summary(LP4)

#shadow price
dx1 = 8-6 
dx2 = 1-2
shadow.price4=40*dx1+100*dx2 
shadow.price4



#total results
results= rbind(c(440,6,2,"","initial LP"),c(450,7.5,1.5,10,"Increase 1st  constraint"),
               c(430,4.5,2.5,-10,"Decrease 1st constraint"),c(460,4,2,20,"Increase 2nd constraint"),
               c(420,8,1,-20,"Decrease 2nd constraint"))
results
#create the corresponding table for the variables above
Sensitivity=as.data.frame(results) 

Sensitivity

#R allows for full customization, here we can rename the columns so that it is more 
#clear what the values we see in the table are. So for V1 I have a column with 
#objective function values
names(Sensitivity) = c("Value of Objective Function",
                       "Quantity of X1","Quantity of X2","shadow price","Change of constraint") 
Sensitivity


#installing and using the package
install.packages("lpSolve") 
library(lpSolve)


#inserting parameters
cvec=c(40,100) ; bvec=c(20,12) 
A=matrix(c(2,4,1,3),nrow=2,ncol=2,byrow=T)

#always insert the direction for each one constraint 
constr.dir=c("<=","<=")

#counting the value of O.F.
LP5 = lp("max",cvec, A, constr.dir, bvec,compute.sens=0); LP5
#or
lp("max",cvec,A,constr.dir,bvec,compute.sens=0)$objval

#successful or no? (0 Επιτυχής εκτέλεση του αλγορίθμου/ 1,2,3,4,5 ΝΟ)
lp("max",cvec,A,constr.dir,bvec)$status

#showing the quantities of x1,x2
lp("max", cvec, A, constr.dir, bvec)$solution 

#showing shadow prices
lp("max",cvec,A,constr.dir,bvec,compute.sens=1)$duals

#lower limit of variation of the coefficients of the objective function using lpsolve
lp("max",cvec,A,constr.dir,bvec,compute.sens=1)$sens.coef.from

#upper limit of variation of the coefficients of the objective function using lpsolve
lp("max",cvec,A,constr.dir,bvec,compute.sens=1)$sens.coef.to

#changing positions of arguments inside the command
#this is the old     lp("max", cvec, A, constr.dir, bvec)      the right vector

lp("max",bvec, A, constr.dir,cvec)
lp("max",bvec, A, constr.dir,cvec)$solution

lp("max",A,cvec,constr.dir,bvec)
lp("max",A,cvec,constr.dir,bvec)$solution


#using lpSolve through linprog
library(linprog)
cvec=c(40,100) ; bvec=c(20,12) 
A=matrix(c(2,4,1,3),nrow=2,ncol=2,byrow=T)
LP6=solveLP(cvec,bvec,A,maximum=T,const.dir=c("<=","<="), lpSolve = T)
summary(LP6)

#shadow prices
LP7 =solveLP(cvec,bvec,A,maximum=T,const.dir=c("<=","<="), lpSolve = T, solve.dual = T)
print(LP7)