I have been trying to get my generalized network flow problem in AMPL but I keep running into this error:
presolve: constraint flow_balance['c5'] cannot hold:
body >= 0 cannot be <= -2500; difference = 2500
presolve: constraint flow_balance['p1'] cannot hold:
body <= 0 cannot be >= 4500; difference = -4500
presolve: constraint flow_balance['c5'] cannot hold:
body >= 0 cannot be <= -5300; difference = 5300
presolve: constraint flow_balance['p1'] cannot hold:
body <= 0 cannot be >= 4800; difference = -4800```
reset;
option solver cplex;
set NODES; # nodes in the network
set ARCS within {NODES, NODES}; # arcs in the network
param b {NODES} default 0; # supply/demand for node i
param c {ARCS} default 0; # cost of one of flow on arc(i,j)
param l {ARCS} default 0; # lower bound on flow on arc(i,j)
param u {ARCS} default Infinity; # upper bound on flow on arc(i,j)
param mu {ARCS} default 1; # multiplier on arc(i,j) -- if one unit leaves i, mu[i,j] units arrive
var x {ARCS}; # flow on arc (i,j)
data Prob3.dat
maximize profit: sum{(i,j) in ARCS} c[i,j] * x[i,j]; #objective: maximize arc flow profit
# Flow Out(i) - Flow In(i) = b(i)
subject to flow_balance {i in NODES}: sum{j in NODES: (i,j) in ARCS} x[i,j] - sum{j in NODES: (j,i) in ARCS} mu[j,i] * x[j,i] = b[i];
subject to capacity {(i,j) in ARCS}: l[i,j] <= x[i,j] <= u[i,j];
#subject to demand {i in NODES}: sum{j in NODES: (j,i) in ARCS} mu[j,i] * x[j,i] - sum{j in NODES: (i,j) in ARCS} x[i,j] = b[i];
solve;
display profit;
display NODES;
display ARCS;
display x;
#note: default arc costs and lower bounds are 0
# default arc upper bounds are infinity
# default node requirements are 0
# default multiplier is 1
set NODES := p1 p2 p3 p4 #product time period nodes
r1 r2 r3 r4 #raw material time period nodes
c1 c2 c3 c4 c5 c5p; #cash flow time period nodes
set ARCS := (p1,p2) (p2,p3) (p3,p4) #inventory arcs
(r1,r2) (r2,r3) (r3,r4) #raw inventory arcs
(c1,c2) (c2,c3) (c3,c4) (c4,c5) #cash flow arcs
(c5,c5p) #virtual arc
(p1,c2) (p2,c3) (p3,c4) (p4,c5) #buy arcs final
(r1,c2) (r2,c3) (r3,c4) (r4,c5) #buy arcs raw
(c1,p2) (c2,p3) (c3,p4) #sell arcs final
(c1,r2) (c2,r3) (c3,r4); #sell arcs raw
param b:= p1 2000 #ending final product on-hand
p4 2000; #initial final product on-hand
#specify costs, upper bound, and multipliers for each arc
param: c u mu l:=
[p1, p2] 1.30 3000 0.94 . #holding cost, capacity, 1-spoilage rate
[p2, p3] 1.30 3000 0.94 .
[p3, p4] 1.30 3000 0.94 .
[c1, c2] . . . . #final product period carry over cost
[c2, c3] . . . .
[c3, c4] . . . .
[c4, c5] . . . .
[r1, r2] 11 7500 0.45 . #raw material conversion
[r2, r3] 11 9000 0.45 .
[r3, r4] 11 8500 0.45 .
[p1, c2] . 3000 38 2000 #final price
[p2, c3] . 3000 40 2500
[p3, c4] . 5000 42 2800
[p4, c5] . 5000 42 2500
[c1, p2] . . -0.02631579 . #1/final price
[c2, p3] . . -0.025 .
[c3, p4] . . -0.02380952 .
[r1, c2] . 7500 0.4 . #raw price
[r2, c3] . 9000 0.4 .
[r3, c4] . 8500 0.333 .
[r4, c5] . 9200 0.286 .
[c1, r2] . . -2.5 . #1/raw price
[c2, r3] . . -2.5 .
[c3, r4] . . -3.0 .
[c5, c5p] -1 . 0 .; #virtual arc has negative cost to incentavize flow
I have posted my dat and mod files for reference. I know that the error is due somehow to the balance constraint but I am unsure why. I have tried to add min demand constraints but that seemed to make everything worse, I have tired redesigning the network flow chart multiple times and I've had no success. If anyone could provide some insight into why I can't figure this error out I would be extremely grateful.
With these messages, AMPL's presolve phase is telling you that your problem has no feasible solution: there is no way to assign values to the variables that are within the variables' bounds and that also satisfy all of the constraints. For a detailed analysis, see the reply to your question in the AMPL user forum.
Related
I'm trying to write a program that calculates the value of propositional logic. I can do it for 4 to 6 variables but for 100 I have no idea.
a propositional formula with n = 100 variables A1, A2, . . . , specified. The formula is in the Conjunctive normal form. A clause C of the formula is replaced by a subset
TC ⊆ {1, 2, . . . , n} ∪ {−1, −2, . . . , −n}
coded as follows:
• Ai occurs in C if and only if i ∈ TC.
• ¬Ai occurs in C if and only if −i ∈ TC.
An occupancy is sought
b : {A1, A2, . . . , An} → {true, false},
such that the number τ (b) of clauses in the formula that are true under assignment b is as large as possible.
does anyone have an idea for an iterative procedure
for this optimization problem
How do i choose the starting assignment b0?
How do i get an assignment bi+1 with τ (bi) < τ (bi+1) from an existing assignment bi?
When do i stop the iteration?
Thank you
I've been experiencing with GAMS but I still haven't got a clue what I'm doing.
Can someone take a look at this short model and try to point me in the right direction?
I have problems in the compilation at the equations, getting a few of these:
Dimension different - The symbol is referenced with more/less
indices as declared
Uncontrolled set entered as constant
Sets
i months / 1, 2, 3 /
j months / 1, 2, 3 /;
Parameters
cp(i) production cost in month i
/ 1 1.08
2 1.11
3 1.10
/
rh(i) number of necessary workers in month i
/ 1 3
2 4
3 6
/
cap(i) production capacity in month i
/ 1 25
2 20
3 25
/
q(j) number of motors to deliver in month j
/ 1 10
2 15
3 25
/
Scalar ca cost to store motors for a month /0.15/ ;
variables
mc(i,j) cost of production of motors in month i to be delivered in month j
x(i,j) number of motors produced in month i to be delivered in month j;
free variables
wf workforce
z cost of production
hr human resources;
Equations
cost cost
human_resources human resources
r1 restriction1
r2 restriction2 ;
cost .. z =e= sum((i,j), (cp(i)+(j-i)*ca)*x(i,j)) ;
human_resources .. hr =e= sum(i, sum(j, rh(i)*x(i, j))) ;
*lower than
r1.. sum(j, x(i,j)) =l= cap(i) ;
*greater than
r2.. sum(i, x(i,j)) =g= q(j) ;
Model
motors 'temp' /all/;
Solve motors using mip minimizing mc;
Display mc, x;
This works but check the solution. I added the positive variable x because otherwise you would have negative productions.
The main problem was the fact that you were optimizing a variable that is declared but that you never use in the equations. Also, the variable you are optimizing cannot have to dimensions (I think).
Then, for constraints r1 and r2 you need to add an index because they must be verified for each month, so r1(i) and r2(j). They are actually a "family of constraints".
You cannot subtract the indexes of the months (can't explain why), but you can subtract their order in the set.
And finally, calculate the mc(i,j) as a parameter after you have obtained the solution.
Sets
i months / 1, 2, 3 /
j months / 1, 2, 3 /;
Parameters
cp(i) production cost in month i
/ 1 1.08
2 1.11
3 1.10
/
rh(i) number of necessary workers in month i
/ 1 3
2 4
3 6
/
cap(i) production capacity in month i
/ 1 25
2 20
3 25
/
q(j) number of motors to deliver in month j
/ 1 10
2 15
3 25
/
Scalar ca cost to store motors for a month /0.15/ ;
variables
* mc(i,j) cost of production of motors in month i to be delivered in month j
x(i,j) number of motors produced in month i to be delivered in month j;
positive variable x;
free variables
wf workforce
z cost of production
hr human resources;
Equations
cost cost
human_resources human resources
r1(i) restriction1
r2(j) restriction2 ;
cost .. z =e= sum((i,j), (cp(i)+(ord(j)-ord(i))*ca)*x(i,j)) ;
human_resources .. hr =e= sum(i, sum(j, rh(i)*x(i, j))) ;
*lower than
r1(i).. sum(j, x(i,j)) =l= cap(i) ;
*greater than
r2(j).. sum(i, x(i,j)) =g= q(j) ;
Model
motors 'temp' /all/;
Solve motors using mip minimizing z;
Parameter mc(i,j);
mc(i,j)= (cp(i)+(ord(j)-ord(i))*ca)*x.l(i,j);
Display mc, x.l;
I have recently been introduced to AMPL in a class and I am currently working on an optimization problem that requires me to find the minimal cost for the demands required. The actual lines I have in question are these:
1. This is in my model file:
minimize Total_Cost:
sum{i in GENS, j in LOADS} cost[i,j] * Allocate[i,j];
subject to GenConst {i in GENS}:
sum {j in LOADS} Allocate[i,j] <= Generation[i];
subject to DemConst {j in LOADS}:
sum {i in GENS} Allocate [i,j] >= Demand[j];
in my data file:
param: GENS:
GenerationMin GenerationMax := #defines set "GENS" and param "Generation"
GEN1 10 90
GEN2 10 100
GEN3 5 85 ;
We have only ever worked with having problems where our demand=generation, but none with having minimum, maximum along with demand != supply. I get the following error when running my data file within ampl GenerationMin is not a subscripted param . When running this script with only the max value it runs fine. The issue, and I am only guessing, are with the lines above. Could someone explain to me where I am going wrong and how to fix this issue?
EDIT: I can include all of my code, although in case anyone wants to reproduce the problem. .mod file:
set GENS;
set LOADS;
param Generation {GENS} >=0;
param Demand {LOADS} >=0;
param cost {GENS, LOADS} >= 0;
var Allocate {GENS, LOADS} >= 0; #{GEN1, LOAD1}, {GEN1, LOAD2... etc}
minimize Total_Cost:
sum{i in GENS, j in LOADS} cost[i,j] * Allocate[i,j];
subject to GenConst {i in GENS}:
sum {j in LOADS} Allocate[i,j] <= Generation[i];
subject to DemConst {j in LOADS}:
sum {i in GENS} Allocate [i,j] >= Demand[j];
.data file:
data;
param: GENS:
GenerationMin GenerationMax := #defines set "GENS" and param "Generation"
GEN1 10 90
GEN2 10 100
GEN3 5 85 ;
param: LOADS: Demand := #Defining set "LOADS" and param "Demand"
Load1 70
Load2 20
Load3 30
Load4 60;
param cost:
Load1 Load2 Load3 Load4 :=
GEN1 39 14 11 14
GEN2 27 9 12 9
GEN3 24 14 17 13;
option solver cplex;
solve;
display Allocate;
display Allocate, Total_Cost > Output.txt
The correct AMPL syntax is:
set GENS;
param GenerationMin {GENS} >=0;
param GenerationMax {GENS} >=0;
data;
param: GENS:
GenerationMin GenerationMax :=
GEN1 10 90
GEN2 10 100
GEN3 5 85 ;
display GENS,GenerationMin,GenerationMax;
i.e. use both GenerationMin,GenerationMax in the model and in the data section.
I have this code:
If I use NLP i get the results, but using QCP as it was asked to me, I can not get results
anyone can help me finding the reason?
code:
sets g generators / P1*P5 /
properties generator properties / a,b,c,max,min /
cc(properties) cost categories / a,b,c /
table data(g,properties) generator cost characteristics and limits
a b c max min
P1 0.19 58.3 1800 155 35
P2 0.13 39.3 3250 195 60
P3 0.08 11.5 4600 165 95
P4 0.07 42.6 5100 305 170
P5 0.14 8.9 3850 280 130
parameter exp(cc) exponent for cost function / a 2, b 1, c 0 /;
scalar demand total power demand in MW / 730 / ;
variables
p(g) power generation level in MW
cost total generation cost - the objective function ;
positive variables p;
p.up(g) = data(g,"max") ;
p.lo(g) = data(g,"min") ;
equations
Q_Eq1 total cost calculation
Q_Eq2 constraint - total generation must equal demand ;
Q_Eq1 .. cost =e= sum((g,cc), data(g,cc)*power(p(g),exp(cc)));
Q_Eq2 .. sum(g,p(g)) =g= demand ;
model problem /all/ ;
solve problem using QCP minimizing cost ;
Seems as if the function "power" is treated as nonlinear in general without analyzing the value of "exp", so that it is not allowed for a QCP. You could reformulate Q_Eq1 like this to make it work:
Q_Eq1 .. cost =e= sum((g,cc), data(g,cc)*(1 $(exp(cc)=0) +
p(g) $(exp(cc)=1) +
sqr(p(g))$(exp(cc)=2)));
Best,
Lutz
I have a problem with my prescription of a mathematical problem to AMPL.
I trying to solve this problem:
In a network with a set of nodes N and a set of edges E each node has storage to cache content. There is a set O of content objects that are accesses by the clients and can be cached if needed. Let the size of o ∈ O be equal to h_o storage units. Assume each node n ∈ N has clients that request object o ∈ O so that the traffic towards n to download o equals d_n;o. In a managed content delivery network (CDN), the CDN operator can adopt various policies to allocate content copies among the caches. These policies may depend on many factors that can be technical or business in nature, which gives rise to dierent optimisation problems. Let h_max be the maximum total storage that can be used by the CDN (i.e., the sum of storage used bythe CDN over all nodes). Find: the allocation of copies of each object o ∈ O such that the limit on the total storage used by CDN is satised while minimising the overall trac in the network (i.e., the routing cost from the caches to client nodes)
Ampl Files:
#Model for 'CDN allocation copies' problem
#sets
#-------------------------------------------------------------------------------------
set K; #index of nodes with group of clients
set N; #nodes
set E; #edges
set O; #objects
#parameters
#-------------------------------------------------------------------------------------
param d {K,O}; #demands for object o
param t {K,O} symbolic; #destination nodes
param r {N,K} binary; #1 if node n is ancestor of node k, 0 otherwise
param a {N,E} binary; #1 if edge begins in vertex, 0 otherwise
param b {N,E} binary; #1 if edge ends in vertex, 0 otherwise
param c {E}; #cost of using an edge
param Hmax; #available capacity for allocation object in proxy servers
#variables
#-------------------------------------------------------------------------------------
var f {N,O} binary; #1 if object saved at node k, 0 otherwise
var x {E,K,O}; #value of the demand realised over edge for object
#goal function
#-------------------------------------------------------------------------------------
#The function minimizes cost of routing
#By saving copies at CDN proxies we minimizing all traffic from all demands
#with all objects
minimize goal:
sum{e in E}
sum{k in K}
sum{o in O}
(x[e,k,o]*c[e]);
#constraints
#-------------------------------------------------------------------------------------
subject to c0 {e in E, k in K, o in O}:
x[e,k,o]>=0;
subject to c1a {k in K, o in O, n in N: n!=t[k,o]}:
(r[n,k]==1 and f[n,o]==1)
==>
sum{e in E}
(a[n,e]*x[e,k,o]) -
sum{e in E}
(b[n,e]*x[e,k,o]) =
d[k,o]*(1-f[k,o])
else
sum{e in E}
(a[n,e]*x[e,k,o]) -
sum{e in E}
(b[n,e]*x[e,k,o]) =
0;
subject to c1c {k in K, o in O, n in N: n==t[k,o]}:
sum{e in E}
(a[n,e]*x[e,k,o]) -
sum{e in E}
(b[n,e]*x[e,k,o]) =
-d[k,o]*(1-f[k,o]);
subject to c2:
sum{k in K}
sum{o in O}
f[k,o] <= Hmax;
subject to c3 {k in K, o in O}:
sum{n in N}
r[n,k]*f[n,o] <= 2;
subject to c4 {o in O}:
f[1,o]=1;
And my data file:
#Data file for 'CDN allocation copies' problem simple example
#indices
set K := 2 3; #index of nodes with group of clients
set N := 1 2 3; #nodes
set E := 1_2 1_3; #edges
set O := o1 o2 o3 o4 o5 o6 o7 o8 o9 o10; #objects
#parameters
param d (tr): #demands for object o
2 3 :=
o1 2560 512
o2 1280 256
o3 640 128
o4 320 64
o5 160 32
o6 80 16
o7 40 8
o8 20 4
o9 10 2
o10 5 1;
#opt= 63 + 75 = 138
param t (tr): #destination nodes
2 3 :=
o1 2 3
o2 2 3
o3 2 3
o4 2 3
o5 2 3
o6 2 3
o7 2 3
o8 2 3
o9 2 3
o10 2 3;
param r (tr): #1 if node n is ancestor of node k, 0 otherwise
1 2 3 :=
2 1 0 0
3 1 0 0;
param a (tr): #1 if edge begins in vertex, 0 otherwise
1 2 3 :=
1_2 1 0 0
1_3 1 0 0;
param b (tr): #1 if edge ends in vertex, 0 otherwise
1 2 3 :=
1_2 0 1 0
1_3 0 0 1;
param c := #cost of using an edge
1_2 1
1_3 1;
param Hmax := 10; #available capacity for allocation object in proxy servers
When I try to solve my problem i see this bug:
Error at _cmdno 15 executing "let" command
(file C:\Program Files\AMPLDevX64 Evaluation\plugins\com.ampldev_2.3.0.201211162252 \include/writesol.ampl, line 22, offset 783):
Can't evaluate _con[92]:
subscript not in 1 .. 91
The error was caused by AMPL incorrectly including the number of logical constraints in _ncons. It is fixed in AMPL version 20130510 (see http://www.netlib.org/ampl/changes). The logical constraint in your model is the indicator constraint c1a.