public Schedule(ProblemInstance prob, Random rnd = null, string slotAllocation = "FirstSlotChosen")
{
_prob = prob;
Sequence = new List<Dispatch>(prob.Dimension);
_jobs = new Jobs[_prob.NumJobs];
_macs = new Macs[_prob.NumMachines];
ReadyJobs = new List<int>(_prob.NumJobs);
for (int job = 0; job < _prob.NumJobs; job++)
{
ReadyJobs.Add(job);
int totalWork = 0;
for (int a = 0; a < _prob.NumMachines; a++)
totalWork += _prob.Procs[job, a];
_jobs[job] = new Jobs(job, totalWork, _prob.NumMachines);
_totProcTime += totalWork; // total work for all jobs (is equivalent to total work for all machines)
}
for (int mac = 0; mac < _prob.NumMachines; mac++)
{
int totalWork = 0;
for (int j = 0; j < _prob.NumJobs; j++)
totalWork += _prob.Procs[j, mac];
_macs[mac] = new Macs(mac, totalWork);
}
_slotAllocation = slotAllocation.Substring(0, 5).ToLower().Equals("first")
? (Func<int[], int, int, int>) FirstSlotChosen
: SmallestSlotChosen;
if (rnd == null)
{
int seed = (int) DateTime.Now.Ticks;
_random = new Random(seed);
}
else
_random = rnd;
}
private GRBVar[,] _x; // starting time of job j on machine a
#endregion Fields
#region Constructors
public GurobiJspModel(ProblemInstance prob, string name, int tmlim_min)
{
_n = prob.NumJobs;
_m = prob.NumMachines;
Info = "Starting gurobi optimisation";
_fileName = String.Format("jssp.{0}.log", name);
// Model
try
{
_env = new GRBEnv(_fileName);
if (tmlim_min > 0)
_env.Set(GRB.DoubleParam.TimeLimit, tmlim_min*60); // time limit is set to seconds!
_env.Set(GRB.IntParam.LogToConsole, 0);
_model = new GRBModel(_env);
_model.Set(GRB.StringAttr.ModelName, "jsp");
}
catch (GRBException e)
{
Console.WriteLine("Error code: " + e.ErrorCode + ". " + e.Message);
}
DecisionVariables();
ProcessingOrder(prob.Procs, prob.Sigma);
DisjunctiveCondition(prob.Procs);
Objective(prob.Procs, prob.Sigma);
//TrueOptimumDecVars = Optimise(out TrueOptimum);
//if (TrueOptimum > 0) // Objective cutoff
// _model.GetEnv().Set(GRB.DoubleParam.Cutoff, TrueOptimum + 0.5);
/* Indicates that you aren't interested in solutions whose objective values
* are worse than the specified value. If the objective value for the optimal
* solution is better than the specified cutoff, the solver will return the
* optimal solution. Otherwise, it will terminate with a CUTOFF status.
*/
// seems to be only for LP relaxation, not MIP objective
}
private void AddProblem(ProblemInstance prob, string givenName)
{
if (prob == null) return;
var pid = ++NumInstances;
var row = Data.NewRow();
row["Name"] = GetName(pid);
row["PID"] = pid;
row["Problem"] = prob;
row["GivenName"] = givenName;
Data.Rows.Add(row);
}
private static string Problem2String(ProblemInstance prob)
{
var thisProblem = prob.NumJobs + " " + prob.NumMachines + "\n";
for (var j = 0; j < prob.NumJobs; j++)
{
for (var m = 0; m < prob.NumMachines; m++)
{
thisProblem += prob.Sigma[j, m] + " " + prob.Procs[j, m] + " ";
}
thisProblem += "\n";
}
return thisProblem;
}
