airlift.gms : Airlift operations schedule

Description

In scheduling monthly airlift operations, demands for specific routes
can be predicted. Actual requirements will be known in the future, and
they may not agree with predicted requirements. Recourse actions are
then required to meet the actual requirements. The actual requirements
are expressed in tons, or any other appropriate measure, and they can
be represented by a random variable. Aircraft of several different
types are available for service.  Each of these types of aircraft has
its own restriction on number of flight hours available during the
month.

The recourse actions available include allowing available flight time
to go unused, switching aircraft from one route to another, and buying
commercial flights. Each of these has its associated cost, depending
on the type(s) of aircraft involved.


Small Model of Type : SP


Category : GAMS EMP library


Main file : airlift.gms

$Title  Airlift operations schedule (AIRLIFT,SEQ=86)
$Ontext
In scheduling monthly airlift operations, demands for specific routes
can be predicted. Actual requirements will be known in the future, and
they may not agree with predicted requirements. Recourse actions are
then required to meet the actual requirements. The actual requirements
are expressed in tons, or any other appropriate measure, and they can
be represented by a random variable. Aircraft of several different
types are available for service.  Each of these types of aircraft has
its own restriction on number of flight hours available during the
month.

The recourse actions available include allowing available flight time
to go unused, switching aircraft from one route to another, and buying
commercial flights. Each of these has its associated cost, depending
on the type(s) of aircraft involved.


Midler, J L, and Wollmer, R D, Stochastic programming models
for airlift operations, Naval Research Logistics Quarterly 16,
315-330, 1969.

Ariyawansa, K A, and Felt, A J, On a New Collection of Stochastic
Linear Programming Test Problems, INFORMS Journal on Computing 16(3),
291-299, 2004

$Offtext

Sets
   i  aircraft type  / f1*f2 /
   j  routes         / r1*r2 /
Alias (j,k);

Parameter
* Felt has 7200 but that's never binding ...
   F(i)      maximum number of flight hours for aircraft type / #i 7200 /
   a(i,j)    flying hours per round trip
   b(i,j)    carrying capacity in tons
   c(i,j)    cost per flight in $
   d(j)      demand / r1 1000, r2 1500 /
   as(i,j,k) flying hours per round trip - switched flights
   cs(i,j,k) cost per flight in $ - switched flights
   pplus(j)  cost for commerically contracted flight in $ / r1 500, r2 250 /
   pminus(j) cost for unused capacity in $ / #j 0 /;

Table data(i,j,*,*)
        a.''   b.''  c.''  as.r1  as.r2  cs.r1 cs.r2
f1.r1   24     50     7200         19           7000
f2.r1   49     20     7200         36           5500
f1.r2   14     75     6000    29          8200
f2.r2   29     20     4000    56          8700
;

a(i,j)    = data(i,j,'a' ,'');
b(i,j)    = data(i,j,'b' ,'');
c(i,j)    = data(i,j,'c' ,'');
as(i,j,k) = data(i,j,'as',k);
cs(i,j,k) = data(i,j,'cs',k);

Variables
   obj          objective
   x(i,j)       number of flights originally planned
   xs(i,j,k)    increase in number of flights on k switched from j
   slackp(j)    contracted flights
   slackn(j)    unused capacity
Positive variable x, xs, slackp, slackn;

Equations
   defobj       objective
   defcap(i)    capacity
   defcaps(i,j) limit the number of switches by original assignment
   defdem(j)    demand fullfilment;

defobj..         sum((i,j), c(i,j)*x(i,j) + sum(k$(not sameas(j,k)),
                  (cs(i,j,k)-c(i,j)*as(i,j,k)/a(i,j))*xs(i,j,k)))
                  + sum(j, (pplus(j)*slackp(j) + pminus(j)*slackn(j))) =e= obj;

defcap(i)..      sum(j, a(i,j)*x(i,j)) =l= F(i);

defcaps(i,j)..   sum(k$(not sameas(j,k)), as(i,j,k)*xs(i,j,k)) =l= a(i,j)*x(i,j);

defdem(j)..      sum(i, b(i,j)*x(i,j) - sum(k$(not sameas(j,k)),
                  b(i,j)*as(i,j,k)/a(i,j)*xs(i,j,k) - b(i,j)*xs(i,k,j)))
                  + slackp(j) - slackn(j) =e= d(j);

model airlift /all/;

file emp / '%emp.info%' /; put emp '* problem %gams.i%'/;
$onput
randvar d('r1') lognormal 1000  50
randvar d('r2') lognormal 1500 300
stage 2 d xs slackp slackn defcaps defdem
$offput
putclose emp;

Set s            scenarios / s1*s36 /;
Parameter
    s_d(s,j)     demand by scenario
    s_x(s,i,j), s_xs(s,i,j,k) flights planned and switched;

Set dict / s     .scenario.''
           d     .randvar. s_d
           x     .level.   s_x
           xs    .level.   s_xs /;

$ifi %gams.emp%==lindo
solve airlift min obj using emp scenario dict;

* Stochastic demand as Independent random variables
Set r realization / x1*x5 /
Table dRV(r,*) stochastic demand
         r1    pr1      r2    pr2
x1   988.16 0.0668 1428.94 0.0668
x2   989.98 0.2417 1439.86 0.2417
x3   994.92 0.3830 1469.54 0.3830
x4  1008.37 0.2417 1550.22 0.2417
x5  1044.92 0.0668 1769.54 0.0668;

put emp '* problem %gams.i%';
put / "randvar d('r1') discrete "; loop(r, put dRV(r,'pr1') dRV(r,'r1'));
put / "randvar d('r2') discrete "; loop(r, put dRV(r,'pr2') dRV(r,'r2'));
put / 'stage 2 d xs slackp slackn defcaps defdem'
putclose emp;

solve airlift min obj using emp scenario dict;