I am currently working on a problem which can be solved using linear programming according to the research I have done here, on YouTube and on other websites. I have familiarized myself with the so called Simplex Method and it’s variations, like – Big M, Dual SM, but still I can’t find any examples that resemble the formulation of my problem and respectively its solution.
My question would be: how to convert the program in standard form?
I think both minimization and maximization should work but let’s do it with minimization.
I am using ‘n’ as a denotation that as input there may be ‘n’ number of variables, ie. sometimes there will be 10, sometimes - 60, yeah ..that many. But if there is a way to solve it it should work for any number of variables, I guess.
To minimize:
Z = a1*x1 + a2*x2 + .. + an*xn,
where a1 .. an
are just random coefficients, all positive.
Subject to: (here is the part where I am not sure if it could be done like that)
N1 ≤ b1*x1 + b2*x2 + .. + bn*xn ≤ N2<br>
M1 ≤ c1*x1 + c2*x2 + .. + cn*xn ≤ M2<br>
O1 ≤ d1*x1 + d2*x2 + .. + dn*xn ≤ O2
- where
N1, N2, M1, M2, O1 & O2
are natural numbers, > 0, e.g 101, 155, 6433, etc.
and of courseN1 < N2, M1 < M2, O1 < O2
- where
b1 .. bn, c1 .. cn, d1 .. dn
are just random coefficients, all positive
Also each unknown variable – x1, x2 .. xn
is bounded like so:
X1-min ≤ x1 ≤ X1-max
X2-min ≤ x2 ≤ X2-max
..
Xn-min ≤ xn ≤ Xn-max
Of course all mins and maxes are known, positive, where Xmin < Xmax and > 0.
X1..n must always be > 0 and between its min/max.
I know about adding slack, surplus, artificial variables but I am not 100% sure if it is as simple as that. My initial thoughts were to split every inequality into two and depending on its sign add either the slack or the surplus + artificial variables and continue with the tables.
Hopefully I managed to explain my problem well enough although I am still a bit confused on how to approach it.
Thanks in advance! Hope you guys have a nice day!