Scheduling algorithm in GAMS, AMPL, MFS and lp_solve

In researching scheduling problems, I came across Nathan Brixius’ blog post which utilised Microsoft solver foundation for a critical path project scheduling problem. This was a step in the right direction, but my problem was resource constrained without concern for the order in which tasks finished. After some further research I found Erwin Kalvelagen from the Amsterdam Optimization Modeling Group who had a relevant post on a conference scheduling problem that used GAMS to model a solution. My goal was to port this to MSF, which was made easier with Ewrin’s great tutorial on OML – the modelling language behind Microsoft’s Solver Foundation.

I started by porting the GAMS solution to AMPL – also a modelling language for linear optimization problems, and closer to MSF as far as syntax is concerned. One advantage GAMS has over AMPL (and indeed MSF) is its richer language syntax and support for sparse matrix’s – see this thread for a discussion on more differences. However AMPL is clean and so was a first step.

AMPL requres two files

1) the data file which defines ‘sets’ and parameter values associated with the domain:

set TASKS := T1 T2 T3 T4 T5; set PEOPLE := P1 P2 P3 P4; set SLOTS := S1 S2; param pref (tr): T1 T2 T3 T4 T5 := P1 1 0 2 0 0 P2 0 1 2 0 0 P3 0 0 1 2 0 P4 0 0 0 1 2 ; param: ntasks := T1 1 T2 1 T3 1 T4 1 T5 1 ; param: minp maxp := T1 1 5 T2 1 5 T3 1 5 T4 1 5 T5 1 5 ; param: nrooms := S1 5 S2 5 ;

2) The model file which declares constaints, and the objective function:

set TASKS;

set PEOPLE;

set SLOTS;

param pref {TASKS,PEOPLE} >= 0;

param nrooms {SLOTS} >= 0;

param ntasks {TASKS} >= 0;

param minp {TASKS} >= 0;

param maxp {i in TASKS} >= minp[i];

var x {t in TASKS, p in PEOPLE, s in SLOTS} binary;

var xts {t in TASKS, s in SLOTS} binary;

var xtp {t in TASKS, p in PEOPLE} binary;

subject to rule1 {t in TASKS, s in SLOTS}:

sum {p in PEOPLE} x[t,p,s] >= xts[t,s];

subject to rule2 {t in TASKS, p in PEOPLE, s in SLOTS}:

x[t,p,s] <= xts[t,s]; subject to rule3 {t in TASKS, p in PEOPLE}: sum {s in SLOTS} x[t,p,s] == xtp[t,p]; subject to rule4 {t in TASKS}: sum {s in SLOTS} xts[t,s] == ntasks[t]; subject to rule5 {t in TASKS}: minp[t] <= sum {p in PEOPLE} xtp[t,p] <= maxp[t]; subject to rule6 {s in SLOTS}: sum {t in TASKS} xts[t,s] <= nrooms[s]; subject to rule7 {p in PEOPLE, s in SLOTS}: sum {t in TASKS} x[t,p,s] == 1; maximize z: sum {t in TASKS, p in PEOPLE} pref[t,p] * xtp[t,p]; [/sourcecode] This can be executed by running following commands inside ampl.exe console: [sourcecode language='css'] ampl: model sched.mod; ampl: data sched.dat; ampl: solve; MINOS 5.51: ignoring integrality of 70 variables MINOS 5.51: optimal solution found. 42 iterations, objective 12 ampl: display x; x [*,*,S1] : P1 P2 P3 P4 := T1 1 0 0 0 T2 8.01124e-18 1 -8.01124e-18 0 T3 1.13258e-16 1.13258e-16 1.13258e-16 0 T4 0 0 1 1 T5 0 0 0 3.08286e-16 [*,*,S2] : P1 P2 P3 P4 := T1 1.40275e-16 -1.40275e-16 1.40275e-16 0 T2 0 2.2428e-16 -8.40105e-26 0 T3 1 1 1 0 T4 -2.21125e-16 0 2.21125e-16 2.21125e-16 T5 2.89604e-16 0 -2.78275e-16 1 ; [/sourcecode] Although in this case we are solving a binary linear optimization problem, AMPL treats all variables as double values - so you can interpret the small numbers in results as FALSE, and the 1's as TRUE. The 3d matrix x then represents the cross section of person(p1-p4) attending task(t1-t5) during slot (s1,s2). Which can more simply be represented as the following Schedule:

s1 s2 p1 t1 t3 p2 t2 t3 p3 t4 t3 p4 t4 t5

Now we are ready to port this same solution to MFS. Using the Excel 2007 foundation solver add-in, all data is defined as region linked to cells on a sheet, and the model is declare in OML. The syntax is similar defining the ‘sets’ and ‘decisions’ (variables) with constraints using Foreach to represent a sum.

Model[

Parameters[Sets,People,Tasks,Slots],

Parameters[Integers,NTasks[Tasks],MinPeople[Tasks],MaxPeople[Tasks],MaxRooms[Slots],Pref[People,Tasks]],

Decisions[Integers[0,1],x[Tasks, People, Slots], xts[Tasks, Slots], xtp[Tasks, People]],

Decisions[Reals,z],

Constraints[

// rule 1: x(t,p,s)=0 for all p => xts(t,s) = 0

Foreach[{t,Tasks}, {s,Slots}, Sum[{p,People},x[t,p,s]] >= xts[t,s]],

// rule 2: any x(t,p,s)=1 => xts(t,s) = 1

Foreach[{t,Tasks}, {p,People}, {s,Slots}, x[t,p,s] <= xts[t,s]], // rule 3: p visits talk t in any s Foreach[{t,Tasks}, {p,People}, Sum[{s,Slots},x[t,p,s]] == xtp[t,p]], // rule 4: each talk happens exactly ntalk times Foreach[{t,Tasks}, Sum[{s,Slots}, xts[t,s]] == NTasks[t]], // rule 5: task must have between min&max people Foreach[{t,Tasks}, Sum[{p,People}, xtp[t,p]] <= MaxPeople[t]], Foreach[{t,Tasks}, Sum[{p,People}, xtp[t,p]] >= MinPeople[t]],

// rule 6: timeslot can only hold up to 5 talks

Foreach[{s,Slots}, Sum[{t,Tasks}, xts[t,s]] <= MaxRooms[s]], // rule 7: p can only visit one talk in each time period Foreach[{p,People}, {s,Slots}, Sum[{t,Tasks}, x[t,p,s]] == 1], z == Sum[{t,Tasks},{p,People},Pref[p,t]*xtp[t,p]] ], Goals[Maximize[z]] ] [/sourcecode] The following could also be solved directly in C# code, which will be an exercise for another post. I did however take the solution one more step further by creating a lp_solve implementation in the form of an '.lp' file which can be solved through the lp_solve IDE. This format is much more verbose as it doesn’t abstract the data, instead deals with the 70 binary variables for each cell of each matrix (x, xtp, and xts). This simple solution is solved in 20ms – faster then the other higher level libraries.

Download the source for solutions in GAMS, AMPL, MSF /w excel, and lp_solve.