I have a model for an optimization problem using three different sets of ordered pairs. Using this constraint:
subject to Constraint8 {t in T, j in J, a in A}:
sum{i in I: (i,j) in LINKS} l[i,t,a] >= sum{v in V, (ih,jh) in REV:(ih,j) not in LINKS} x[ih,j,v,t,a]+1;
Actually works and gives a correct solution. I want to alter this constraint though, so I comment it out, and then write the modified version:
subject to Constraint8 {t in T, j in J, a in A}:
sum{i in I: (i,j) in LINKS} l[i,t,a] >= sum{g in T: g <= t, v in V, (ih,jh) in REV: (ih,j) not in LINKS} x[ih,j,v,g,a] +1;
(with the difference that x is now summed over the set T as well, however on the element g in T: g <= t. However, AMPL does not seem to like this and this gives a syntax error. I've been trying it on some different "places" within the sum, but it does not work.
Does anyone have a clue regarding this error?
In AMPL, as well as in mathematical notation, you should specify the "such that" condition at the end of the indexing expression:
subject to Constraint8 {t in T, j in J, a in A}:
sum{i in I: (i,j) in LINKS} l[i,t,a] >=
sum{g in T, v in V, (ih,jh) in REV: g <= t and (ih,j) not in LINKS}
x[ih,j,v,g,a] + 1;
Related
What is the definition of f(n) = O(n^2)?
This means the following:
There exist c > 0 and n0 such that f(n) <= c*n^2 for all n >= n0.
What is the precise definition of f(E, V) = O(E + V)?
It really works in the same way in your case. One valid definition might be:
There exist c > 0 and n0 such that f(E, V) <= c*(E+V) for all E, V >= n0.
However, you can also define it differently, e.g. by introducing two variables c, d > 0 and requiring f(E,V) <= cE + dE. Both are valid definitions.
However, most likely you have encountered this definition in the context of graph algorithms, where E is the number of edges and V the number of vertices. The time complexity O(E + V) occurs a lot in this field as it really is the same thing as saying O(max(E, V)). It is still linear time complexity.
I have defined the variables w in AMPL in the following way:
var w[F,J,T] binary;
where F, J and T are sets.
After solving the problem, I would like to print the tables w[f,*,*] for each f in F. What is the command that does this?
If I just write
display w;
it returns me the tables w[*,j,*] for each j in J (I guess it's a question of the size of the results).
Thanks to anyone who will answer
By the way, for future reference, the answer is:
display {f in F} : {j in J, t in T} w[f, j, t];
I have an array of set in the Golfers problem (in each week there should be formed groups, such that no two players play together more than once, and everybody plays exactly one time each week):
int: gr; %number of groups
set of int: G=1..gr;
int: sz; %size of groups
set of int: S=1..sz;
int: n=gr*sz; %number of players
set of int: P=1..n;
int: we; % number of weeks
set of int: W=1..we;
include "globals.mzn";
array[G,W] of var set of P: X; %X[g,w] is the set of people that form group g in week w
My constraints are as follow (I'm not sure if everything works correctly yet):
constraint forall (g in G, w in W) (card (X[g,w]) = sz); %Each group should have size sz
constraint forall (w in W, g,h in G where g > h) (disjoint(X[g,w], X[h,w])); % Nobody plays twice in one week
constraint forall (w,u in W where w > u) (forall (g,h in G) (card(X[g,w] intersect X[h,u]) <= 1 )); % Two players never meet more than once
constraint forall (w in 2..we) (w+sz-1 in X[1,w] /\ 1 in X[1,w]); %Symmetries breaking: week permutations
constraint forall (w in W, g in 1..gr-1) ( min(X[g,w]) < min(X[g+1,w]) ); %Symmetries breaking: group permutations
constraint forall (g in G, s in S) ( s+sz*(g-1) in X[g,1]);
solve satisfy;
output [ show(X[i,j]) ++ if j == we then "\n" else " " endif | i in 1..gr, j in 1..we ];
My problem lies in constraint number 5. I cannot use min on "var set of int: x", I should use it on "set of int: x". Unfortunately, I do not understand the difference between those two (from what I've read this may be connected to defining the size of each set, but I'm not sure).
Could someone explain the problem to me and propose a solution? I would be very very grateful. Thanks!
First of all: A var is a decision variable. The goal of all Minizinc programs are to decide the the value of all decision variables. You don't know what the values are and you are trying to find the values. Anything that is not a var is simply a known number. (disregarding the use of sets)
Doing min(X[g,w]) of a decision variable (var) is simply not implemented in Minizinc. The reason would be that using X[g,w] < X[g+1,w] without the min makes more sense. Why only constrain the lowest number in both sets insted of all numbers. I.e {1,3,5} < {1,4} insted of 1 < 1
(I hope MiniZinc has < on sets so I don't lie, I am not sure)
I have found out the solution - we should make an array of elements of the set to make the max function possible in this case.
constraint forall (w in 2..we) ( max([i | i in X[1,w-1]]) < max([i | i in X[1,w]])); %Symmetries breaking: week permutations
constraint forall (w in W, g in 1..gr-1) ( min([i | i in X[g,w]]) < min([i | i in X[g+1,w]]));% Symmetries breaking: group permutations (I have been trying to speed up the constraint above, but it does not work with var set of int..)
Hello fellow optimizers!
I'm having some issues with the following constraint:
#The supply at node i equals what was present at the last time period + any new supply and subtracted by what has been extracted from the node.
subject to Constraint1 {i in I, t in T, c in C}:
l[i,t-1,c] + splus[i,t] - sum{j in J, v in V, a in A} x[i,j,v,t,c,a]= l[i,t,c];
which naturally causes this constraint to provide errors the first time it loops, as the t-1 is not defined (for me, l[i,0,c] is not defined. Where
var l{I,T,C} >= 0; # Supply at supply node I in time period T for company C.
param splus{I,T}; # Additional supply at i.
var x{N,N,V,T,C,A} integer >= 0; #Flow from some origin within N to a destination within N using vehicle V, in time T, for company C and product A.
and set T; (in the .mod) is a set defined as:
set T := 1 2 3 4 5 6 7; in the .dat file
I've tried to do:
subject to Constraint1 {i in I, t in T: t >= 2, c in C}:
all else same
which got me a syntax error. I've also tried to include "let l[1,0,1] := 0" for all possible combinations, which got me the error
error processing var l[...]:
no data for set I
I've also tried
subject to Constraint1 {i in I, t in T, p in TT: p>t, c in C}:
l[i,t,c] + splus[i,p] - sum{j in J, v in V, a in A} x[i,j,v,p,c,a]= l[i,p,c];
where
set TT := 2 3 4 5 6;
in the .dat file (and merely set TT; in the .mod) which also gave errors. Does someone have any idea of how to do this?
One way to fix this is to specify the condition t >= 2 at the end of the indexing expression:
subject to Constraint1 {i in I, t in T, c in C: t >= 2}:
...
See also Section A.3 Indexing expressions and subscripts for more details on syntax of indexing expressions.
I want to model that departures from a node can only take place in a "every n hours" manner. I've started to model this using two variables - starttime[i,j,k] shows when vehicle k departured i with j as destination, x[i,j,k] is a binary variable having value 1 if vehicle k drove from i to j, and 0 otherwise. The model is:
maximize maxdrive: sum{i in V, j in V, k in K} traveltime[i,j]*x[i,j,k];
subject to TimeConstraint {k in K}:
sum{i in V, j in V} (traveltime[i,j]+servicetime[i])*x [i,j,k] <= 1440;
subject to StartTime{i in V,j in V, k in K}:
starttime[i,j,k] + traveltime[i,j] - 9000 * (1 - x[i,j,k]) <= starttime[j,i,k];
subject to yvar{i in V, j in V}:
sum{k in K} x[i,j,k] <= maxVisits[i,j];
subject to Constraint1{i in V, j in V, k in K, g in V, h in K}:
starttime[i,j,k] + TimeInterval[i]*x[i,j,k] <= starttime[i,g,h];
The constraint in question is "Constraint1" where i is the origin node, j the destination node, and k is the vehicle. The index g is used to show that the later departure can be to any destination node. TimeInterval corresponds to the interval intended, i.e. if TimeInterval at i is 2 hours, the starttime of the next vehicle to departure from i must not be less than 2 hours from previous departure. The origins corresponds to specific products (only available from said origin node) whereas I want the vehicles to not be bounded to a specific origin node - they should be able to jump between nodes to utilize backhauling etc. In other words, I want to conduct this constraint without having restraints on the vehicles themselves but rather the origin nodes.
The objective function to "maximize the traveltime" may seem strange, but the objective function is rather obsolete really. If the constraints are met, the solution is adequate. To maximize traveltime is merely an attempt to "force" the x variables to become 1.
The question is: how can I do this? With this formulation, all x[i,j,k] variables dissappears from the answer (without this constraint, some of the binary variables x becomes 1 and the other 0. The solution meets the maxVisits requirement. With the constraint all x variables becomes 0 and all starttimes becomes 0 as well. MINTO (The solver) doesn't state that the problem is infeasible either.
Also, how to separate the vehicles so the program recognizes that it is a comparison between all departures? I would rather to not include time dimensions, at it would give so much more variables.
EDIT: After trying a new model using a non-linear solver I've seen some strange results. Specifically, I'm using the limit 1440 (minutes) as an upper bound as to for how long a vehicle can operate each day. Using this model below the solution is 0 for every variable, but the starttime for all combinations of i,j,k is 720 (half of 1440). Does anyone have any clue in regards of what causing this solution? How did this constraint remove the link between starttime being higher than 0 requiring that x must be 1.
subject to StartTimeSelf{i in V, j in V, k in K, g in K, h in V}:
starttime[i,j,k]*x[i,j,k] + TimeInterval[i]*x[i,j,k] + y[i,k] <= starttime[i,h,g]*x[i,j,k];