Description
The objective of this model is to allocate aircrafts to routes to maximize the expected profit when traffic demand is uncertain. Several problems are solved with standard LP solvers and an EMP SP solver: Also see models AIRCRAFT, AIRSP and AIRSP2.
Small Model of Type : SP
Category : GAMS EMP library
Main file : airsp3.gms
$Title Aircraft allocation - stochastic optimization (AIRSP3,SEQ=69)
$Ontext
The objective of this model is to allocate aircrafts to routes to maximize
the expected profit when traffic demand is uncertain. Several problems
are solved with standard LP solvers and an EMP SP solver:
Also see models AIRCRAFT, AIRSP and AIRSP2.
Dantzig, G B, Chapter 28. In Linear Programming and Extensions.
Princeton University Press, Princeton, New Jersey, 1963.
$Offtext
set i aircraft types and unassigned passengers / a, b, c, d /
j assigned and unassigned routes / route-1, route-2, route-3, route-4, route-5 /
;
table c(i,j) costs per aircraft (1000s)
route-1 route-2 route-3 route-4 route-5
a 18 21 18 16 10
b 15 16 14 9
c 10 9 6
d 17 16 17 15 10
;
table pcap(i,j) passenger capacity of aircraft i on route j
route-1 route-2 route-3 route-4 route-5
a 16 15 28 23 81
b 10 14 15 57
c 5 7 29
d 9 11 22 17 55
;
parameter aircraft(i) aircraft availability / a 10
b 19
c 25
d 15 /;
parameter costbumped(j) costs associated with bumping passengers
/ route-1 13
route-2 13
route-3 7
route-4 7
route-5 1 /;
set k possible outcomes /k1*k5/;
table stochasticdemand(j,k) demand distribution on route j
k1 k2 k3 k4 k5
route-1 200 220 250 270 300
route-2 50 150
route-3 140 160 180 200 220
route-4 10 50 80 100 340
route-5 580 600 620
;
table probability(j,k) "probabilities corresponding to sd(j,k)"
k1 k2 k3 k4 k5
route-1 .2 .05 .35 .2 .2
route-2 .3 .7
route-3 .1 .2 .4 .2 .1
route-4 .2 .2 .3 .2 .1
route-5 .1 .8 .1
;
Parameter rdemand(j) random demand initialize with mean;
rdemand(j) = sum(k$probability(j,k), probability(j,k)*stochasticdemand(j,k));
positive variable x(i,j) number of aircraft type i assigned to route j;
positive variable bumped(j) passengers bumped;
variable z objective variable;
equation avail(i) aircraft availability constraints;
equation demand(j) demand constraints;
equation cost objective;
cost.. z =e= sum((i,j), c(i,j)*x(i,j)) + sum(j, costbumped(j)*bumped(j));
avail(i).. sum(j, x(i,j)) =l= aircraft(i);
demand(j).. sum(i, pcap(i,j)*x(i,j)) + bumped(j) =g= rdemand(j);
model fixed /cost,avail,demand/;
file emp / '%emp.info%' /; put emp '* problem %gams.i%';
loop(j,
put / 'randvar ' rdemand.tn(j) ' discrete ';
loop(k$probability(j,k),
put / probability(j,k) stochasticdemand(j,k));
);
putclose / 'stage 2 demand bumped rdemand';
$eval scenmax card(k)**card(j)
Set s scenarios / s1*s%scenmax% /;
Parameter
srep(s,*) scenario probability / #s.prob 0 /
s_demand(s,j) demand realization by scenario
rep solution metric report;
Set dictSP / s .scenario.''
'' .opt. srep
rdemand.randvar. s_demand /;
* Solve stochastic recourse problem or here-and-now
solve fixed using emp min z scenario dictSP;
rep('here-and-now') = z.l;
* Solve expected value (EV) problem
solve fixed using lp min z;
* Compute expected result of using the EV solution by evaluating first stage decision of EV solution under all scenarios:
x.fx(i,j) = x.l(i,j);
Parameter
s_cost(s) cost by scenario;
Set dictMS / s .scenario.''
rdemand.param. s_demand
z .level. s_cost /;
solve fixed using lp min z scenario dictMS;
rep('Expected Value of EV solution') = sum(s, s_cost(s)*srep(s,'prob'));
* Now assume perfect forecast and solve the wait-and-see model
x.lo(i,j) = 0; x.up(i,j) = inf;
solve fixed using lp min z scenario dictMS;
rep('wait-and-see') = sum(s, s_cost(s)*srep(s,'prob'));
* Now calculate the different measure
rep('Expected Value of Perfect Information (EVPI)') = rep('here-and-now') - rep('wait-and-see');
rep('Value of Stochastic Solution (VSS)') = rep('Expected Value of EV solution') - rep('here-and-now');
option rep:2:0:1 display rep;