protected void InitRMPModel()
{
cost = RMPModel.AddMaximize();
var = new Dictionary <IALPDecision, INumVar> [Data.TimeHorizon];
DualValue1 = new double[Data.TimeHorizon][];
DualValue2 = new double[Data.TimeHorizon];
lowerbound = new double[Data.TimeHorizon][];
upperbound = new double[Data.TimeHorizon][];
#region //////////////生成约束//////////////
constraint1 = new IRange[Data.TimeHorizon][];
constraint2 = new IRange[Data.TimeHorizon];
for (int i = 0; i < Data.TimeHorizon; i++)
{
DualValue1[i] = new double[Data.RS.Count];
var[i] = new Dictionary <IALPDecision, INumVar>();
constraint1[i] = new IRange[Data.RS.Count];
lowerbound[i] = new double[Data.RS.Count];
upperbound[i] = new double[Data.RS.Count];
foreach (IALPResource re in Data.RS)
{
constraint1[i][Data.RS.IndexOf(re)] = RMPModel.AddRange(double.MinValue, (Data.InitialState as IALPState)[re]);
}
//constraint1[i][0].UB -= 0.3;
constraint2[i] = RMPModel.AddRange(1, 1);
}
#endregion
RMPModel.SetOut(this.SolverTextWriter);
}
private void btn_cutStep_Click(object sender, RoutedEventArgs e)
{
mainCanvas.ClearCanvas();
int[] La = new int[node_cnt * node_cnt];
for (int i = 0; i < node_cnt; i++)
{
int deg = 0;
for (int j = 0; j < node_cnt; j++)
{
La[i * node_cnt + j] = graph[i, j] ? -1 : 0;
deg += graph[i, j] ? 1 : 0;
}
La[i * node_cnt + i] = deg;
}
Cplex model = new Cplex();
INumVar[] x = model.BoolVarArray(node_cnt);
INumVar[] eij = model.BoolVarArray(node_cnt * node_cnt);
for (int i = 0; i < node_cnt; i++)
{
for (int j = i; j < node_cnt; j++)
{
if (i != j)
{
model.AddEq(eij[i * node_cnt + j], eij[j * node_cnt + i]);
}
model.AddLe(eij[i * node_cnt + j], x[i]);
model.AddLe(eij[i * node_cnt + j], x[j]);
model.AddLe(model.Sum(x[i], x[j]), model.Sum(eij[i * node_cnt + j], eij[i * node_cnt + j], model.Constant(1)));
}
}
model.AddMaximize(model.ScalProd(La, eij));
if (model.Solve())
{
double[] vals = model.GetValues(x);
int val = (int)Math.Round(model.GetObjValue());
dc_Utility.WriteLine("");
dc_Utility.WriteLine(dc_Utility.c_stars);
dc_Utility.WriteLine("Group A size: " + vals.Count(p => p > 0.5));
dc_Utility.WriteLine("Group B size: " + vals.Count(p => p < 0.5));
dc_Utility.WriteLine("Max Cut: " + val);
dc_Utility.WriteLine(dc_Utility.c_stars);
dc_Utility.WriteLine("");
for (int i = 0; i < node_cnt; i++)
{
classes[i] = vals[i] > 0.5;
}
DrawGraph(true);
}
model.Dispose();
}
private void InitializeObjFunc()
{
var func = cplex.LinearNumExpr();
foreach (var x in numVars)
{
func.AddTerm(1, x);
}
cplex.AddMaximize(func);
}
private bool RCG(int t, out IALPState temp_s, out IMDPDecision deci_a)
{
temp_s = null;
deci_a = null;
//System.Console.WriteLine("--------开始生成问题:{0}-----", System.DateTime.Now.Millisecond);
#region 创建目标函数
SubModel.Remove(SubModel.GetObjective());
SubModel.AddMaximize(subObject[1][t]);
#endregion
foreach (IALPResource re in Data.RS)
{
constraint_sub2[Data.RS.IndexOf(re)].SetBounds(0, (Data.InitialState as IALPState)[re]);
}
//System.Console.WriteLine("--------生成问题结束:{0}-----", System.DateTime.Now.Millisecond);
if (SubModel.Solve())
{
//System.Console.WriteLine("--------求解问题结束:{0}-----", System.DateTime.Now.Millisecond);
//System.Console.WriteLine("RCG({0})求解结束\t目标值为:{1}", t, SubModel.ObjValue<0.001?0: SubModel.ObjValue);
if (SubModel.ObjValue <= Tolerance)
{
return(true);
}
else
{
//print("{0}问题违反程度{1}", t, (SubModel.ObjValue - Tolerance).ToString());
#region 用h,r生成一个State
for (int _h = 0; _h < h.Count(); _h++)
{
double temp_h = SubModel.GetValue(h[_h]);
if (temp_h == 1)
{
deci_a = Data.DS[_h];
break;
}
}
Dictionary <IALPResource, int> dic = new Dictionary <IALPResource, int>();
for (int _r = 0; _r < r.Count(); _r++)
{
dic.Add(Data.RS[_r], (int)SubModel.GetValue(r[_r]));
}
temp_s = Data.CreateOrFind(dic);
#endregion
return(false);
}
}
else
{
throw new System.Exception("子问题无解");
}
}
//Affine Funciton Approximation
public static Cplex Build_CD1_Model(IALPFTMDP aff)
{
Cplex model = new Cplex();
IObjective cost = model.AddMaximize();
IRange[][] constraint1 = new IRange[aff.TimeHorizon][];
IRange[] constraint2 = new IRange[aff.TimeHorizon];
#region //////////////生成约束//////////////
for (int i = 0; i < aff.TimeHorizon; i++)
{
constraint1[i] = new IRange[aff.RS.Count];
foreach (IALPResource re in aff.RS)
{
if (i == 0)
{
constraint1[i][aff.RS.IndexOf(re)] = model.AddRange((aff.InitialState as IALPState)[re], (aff.InitialState as IALPState)[re]);
}
else
{
constraint1[i][aff.RS.IndexOf(re)] = model.AddRange(0, 0);
}
}
constraint2[i] = model.AddRange(1, 1);
}
#endregion
#region //////////////生成变量//////////////
for (int t = 0; t < aff.TimeHorizon; t++)
{
foreach (IALPState s in aff.SS)
{
foreach (IMDPDecision a in aff.GenDecisionSpace(s))
{
Column col = model.Column(cost, aff.Rt(t, a));
foreach (IALPResource re in aff.RS)
{
col = col.And(model.Column(constraint1[t][aff.RS.IndexOf(re)], (s as IALPState)[re]));
if (t < aff.TimeHorizon - 1)
{
col = col.And(model.Column(constraint1[t + 1][aff.RS.IndexOf(re)], (aff.Qti(t, re, a)) - (s as IALPState)[re]));
}
}
col = col.And(model.Column(constraint2[t], 1));
model.NumVar(col, 0, double.MaxValue, NumVarType.Float);
}
}
}
#endregion
return(model);
}
public static Cplex BuildDualModel(IFTMDP Ida)
{
Cplex model = new Cplex();
IObjective cost = model.AddMaximize();
IRange[][] constraint = new IRange[Ida.TimeHorizon][];
#region //////////////生成约束//////////////
for (int i = 0; i < Ida.TimeHorizon; i++)
{
constraint[i] = new IRange[Ida.SS.Count];
foreach (IMDPState s in Ida.SS)
{
if (i == 0 && s.Equals(Ida.InitialState))
{
constraint[i][Ida.SS.IndexOf(s)] = model.AddRange(1, 1);
}
else
{
constraint[i][Ida.SS.IndexOf(s)] = model.AddRange(0, 0);
}
}
}
#endregion
#region //////////////生成变量//////////////
for (int t = 0; t < Ida.TimeHorizon; t++)
{
foreach (IMDPState s in Ida.SS)
{
foreach (IMDPDecision a in Ida.GenDecisionSpace(s))
{
Column col = model.Column(cost, Ida.Reward(t, s, a));
col = col.And(model.Column(constraint[t][Ida.SS.IndexOf(s)], 1)); //插入Aj
if (t < Ida.TimeHorizon - 1)
{
foreach (IMDPState k in Ida.GenStateSpace(s, a))
{
col = col.And(model.Column(constraint[t + 1][Ida.SS.IndexOf(k)], -Ida.Prob(t, s, k, a))); //插入Aj
}
}
model.NumVar(col, 0, double.MaxValue, NumVarType.Float);
}
}
}
#endregion
model.SetOut(null);
return(model);
}
private void SetTime(int t)
{
model.Remove(model.GetObjective());
cost = model.AddMaximize();
foreach (IALPDecision a in aff.DS)
{
Column col = model.Column(cost, aff.Rt(t, a));
col = col.And(model.Column(constraint, 1));
vars[aff.DS.IndexOf(a)] = model.NumVar(col, 0, 1, NumVarType.Int);
}
}
public static Cplex Build_CD3_Model(IALPFTMDP aff)
{
Cplex model = new Cplex();
IObjective cost = model.AddMaximize();
INumVar[][] var = new INumVar[aff.TimeHorizon][];
#region //////////////生成约束//////////////
IRange[][] constraint1 = new IRange[aff.TimeHorizon][];
IRange[] constraint2 = new IRange[aff.TimeHorizon];
for (int i = 0; i < aff.TimeHorizon; i++)
{
var[i] = new INumVar[aff.DS.Count];
constraint1[i] = new IRange[aff.RS.Count];
foreach (IALPResource re in aff.RS)
{
constraint1[i][aff.RS.IndexOf(re)] = model.AddRange(double.MinValue, (aff.InitialState as IALPState)[re]);
}
constraint2[i] = model.AddRange(1, 1);
}
#endregion
#region //////////////生成变量//////////////
for (int t = 0; t < aff.TimeHorizon; t++)
{
foreach (IALPDecision a in aff.DS)
{
//目标函数
Column col = model.Column(cost, aff.Rt(t, a));
//第一类约束
foreach (IALPResource re in aff.RS)
{
for (int k = t + 1; k < aff.TimeHorizon; k++)
{
col = col.And(model.Column(constraint1[k][aff.RS.IndexOf(re)], aff.Qti(t, re, a)));
}
if (a.UseResource(re))
{
col = col.And(model.Column(constraint1[t][aff.RS.IndexOf(re)], 1));
}
}
//第二类约束
col = col.And(model.Column(constraint2[t], 1));
var[t][aff.DS.IndexOf(a)] = model.NumVar(col, 0, double.MaxValue, NumVarType.Float);
}
}
#endregion
return(model);
}
private void InitRMPModel()
{
RMPModel.ClearModel();
cost = RMPModel.AddMaximize();
constraint1 = new IRange[Data.TimeHorizon][];
constraint2 = new IRange[Data.TimeHorizon];
DualValue1 = new double[Data.TimeHorizon][];
DualValue2 = new double[Data.TimeHorizon];
#region //////////////生成约束//////////////
//Parallel.For(0, Data.TimeHorizon, i =>
for (int i = 0; i < Data.TimeHorizon; i++)
{
lock (RMPModel)
{
constraint1[i] = new IRange[Data.RS.Count];
DualValue1[i] = new double[Data.RS.Count];
foreach (IALPResource re in Data.RS)
{
if (i == 0)
{
constraint1[i][Data.RS.IndexOf(re)] = RMPModel.AddRange((Data.InitialState as IALPState)[re], (Data.InitialState as IALPState)[re]);
}
else
{
constraint1[i][Data.RS.IndexOf(re)] = RMPModel.AddRange(0, 0);
}
}
constraint2[i] = RMPModel.AddRange(1, 1);
}
}
#endregion
//RMPModel.SetParam(Cplex.LongParam.RootAlgorithm, 1);
RMPModel.SetOut(SolverTextWriter);
}
public static Cplex Build_CD2_Model(IALPFTMDP aff)
{
Cplex model = new Cplex();
IObjective cost = model.AddMaximize();
#region //////////////生成约束//////////////
IRange[][] constraint1 = new IRange[aff.TimeHorizon][];
IRange[][] constraint2 = new IRange[aff.TimeHorizon][];
IRange[] constraint3 = new IRange[aff.TimeHorizon];
for (int i = 0; i < aff.TimeHorizon; i++)
{
constraint1[i] = new IRange[aff.RS.Count];
constraint2[i] = new IRange[aff.RS.Count];
foreach (IALPResource re in aff.RS)
{
if (i == 0)
{
constraint1[i][aff.RS.IndexOf(re)] = model.AddRange((aff.InitialState as IALPState)[re], (aff.InitialState as IALPState)[re]);
constraint2[i][aff.RS.IndexOf(re)] = model.AddRange(double.MinValue, 0);
}
else
{
constraint1[i][aff.RS.IndexOf(re)] = model.AddRange(0, 0);
constraint2[i][aff.RS.IndexOf(re)] = model.AddRange(double.MinValue, 0);
}
}
constraint3[i] = model.AddRange(1, 1);
}
#endregion
#region //////////////生成变量//////////////
//生成h
for (int t = 0; t < aff.TimeHorizon; t++)
{
foreach (IALPDecision a in aff.DS)
{
//目标函数
Column col = model.Column(cost, aff.Rt(t, a));
foreach (IALPResource re in aff.RS)
{
//第一类约束
if (t < aff.TimeHorizon - 1)
{
col = col.And(model.Column(constraint1[t + 1][aff.RS.IndexOf(re)], aff.Qti(t + 1, re, a)));
}
//第二类约束
if (a.UseResource(re))
{
col = col.And(model.Column(constraint2[t][aff.RS.IndexOf(re)], 1));
}
}
//第三类约束
col = col.And(model.Column(constraint3[t], 1));
model.NumVar(col, 0, double.MaxValue, NumVarType.Float);
}
foreach (IALPResource r in aff.RS)
{
Column col = model.Column(cost, 0);// cost, aff.Rt(t, a));
if (t < aff.TimeHorizon - 1)
{
col = col.And(model.Column(constraint1[t + 1][aff.RS.IndexOf(r)], -1));
}
col = col.And(model.Column(constraint1[t][aff.RS.IndexOf(r)], 1));
col = col.And(model.Column(constraint2[t][aff.RS.IndexOf(r)], -1));
model.NumVar(col, 0, double.MaxValue, NumVarType.Float);
}
}
///);
#endregion
return(model);
}
//Piece-Wise Function Approximation
public static Cplex Build_Dual_PW_Model(IALPFTMDP aff)
{
Cplex model = new Cplex();
IObjective cost = model.AddMaximize();
#region //////////////生成约束//////////////
IRange[][][] constraint1 = new IRange[aff.TimeHorizon][][];
IRange[] constraint2 = new IRange[aff.TimeHorizon];
for (int i = 0; i < aff.TimeHorizon; i++)
{
constraint1[i] = new IRange[aff.RS.Count][];
foreach (IALPResource re in aff.RS)
{
constraint1[i][aff.RS.IndexOf(re)] = new IRange[(aff.InitialState as IALPState)[re]];
for (int k = 1; k < (aff.InitialState as IALPState)[re]; k++)
{
if (i == 0)
{
constraint1[i][aff.RS.IndexOf(re)][k - 1] = model.AddRange(1, 1);
}
else
{
constraint1[i][aff.RS.IndexOf(re)][k - 1] = model.AddRange(0, 0);
}
}
}
constraint2[i] = model.AddRange(1, 1);
}
#endregion
#region //////////////生成变量//////////////
//System.Threading.Tasks.Parallel.For(0, aff.TimeHorizon, (t) =>
for (int t = 0; t < aff.TimeHorizon; t++)
{
foreach (IALPState s in aff.SS)
{
foreach (IMDPDecision a in aff.GenDecisionSpace(s))
{
//目标函数
Column col = model.Column(cost, aff.Reward(t, s, a));
// System.Console.WriteLine(aff.Reward(t, s, a));
//第一类约束
foreach (IALPResource re in aff.RS)
{
for (int k = 1; k < (aff.InitialState as IALPState)[re]; k++)
{
if (s[re] >= k)
{
col = col.And(model.Column(constraint1[t][aff.RS.IndexOf(re)][k - 1], 1));
if (t < aff.TimeHorizon - 1)
{
col = col.And(model.Column(constraint1[t + 1][aff.RS.IndexOf(re)][k - 1], -1));
if (s[re] == k)
{
col = col.And(model.Column(constraint1[t + 1][aff.RS.IndexOf(re)][k - 1], (aff.Qti(t, re, a))));
}
}
}
}
}
//第二类约束
col = col.And(model.Column(constraint2[t], 1));
model.NumVar(col, 0, double.MaxValue, NumVarType.Float);
}
}
}
///);
#endregion
model.SetOut(null);
return(model);
}
private void runModel(List <Employee> employees, List <Shift> shifts)
{
StreamWriter writer = File.CreateText(@"C:\Users\user\Desktop\cplex log\result9.txt");
try
{
Cplex model = new Cplex();
model.SetOut(writer);
//------------------------------
//---Variable initialization-----
//------------------------------
//Assignment variables
IDictionary <string, IIntVar> assignVars = new Dictionary <string, IIntVar>();
employees.ForEach(employee =>
{
shifts.ForEach(shift =>
{
string name = getAssignVarName(employee, shift);
assignVars.Add(name, model.BoolVar(name));
});
});
//Total assignment hours
INumVar totalAssignHourVar = model.NumVar(0, Double.MaxValue, getTotalAssignVarName());
//----------------------------------
//---Constraints initialization-----
//-----------------------------------
//1) Min rest constraint
//2) Goal total assigned hours constraint
ILinearNumExpr sumAssignHourExpr = model.LinearNumExpr();
sumAssignHourExpr.AddTerm(-1.0, totalAssignHourVar);
employees.ForEach(employee =>
{
shifts.ForEach(shift1 =>
{
ILinearNumExpr sumOverlapExpr = model.LinearNumExpr();
string name1 = getAssignVarName(employee, shift1);
IIntVar assignVar1 = assignVars[name1];
sumOverlapExpr.AddTerm(1.0, assignVar1);
shifts.ForEach(shift2 =>
{
if (shift1 != shift2 && this.isDurationOverlap(shift1, shift2))
{
string name2 = getAssignVarName(employee, shift2);
sumOverlapExpr.AddTerm(1.0, assignVars[name2]);
}
});
model.AddLe(sumOverlapExpr, 1.0, "MinRestConst");
sumAssignHourExpr.AddTerm((shift1.end - shift1.begin).TotalMinutes, assignVar1);
});
});
//3) No overassignment constraint
shifts.ForEach(shift =>
{
ILinearNumExpr sumAssigsExpr = model.LinearNumExpr();
employees.ForEach(employee =>
{
string name1 = getAssignVarName(employee, shift);
IIntVar assignVar1 = assignVars[name1];
sumAssigsExpr.AddTerm(1.0, assignVar1);
});
model.AddLe(sumAssigsExpr, shift.shiftNumber, "NoOverAssignConst");
});
model.AddEq(sumAssignHourExpr, 0.0, "TotalAssignedHourConst");
INumVar[] goalVars = { totalAssignHourVar };
double[] coeffs = { 1.0 };
model.AddMaximize(model.ScalProd(goalVars, coeffs));
model.ExportModel(@"C:\Users\user\Desktop\cplex log\model1.lp");
bool feasible = model.Solve();
if (feasible)
{
double objVal = model.ObjValue;
model.Output().WriteLine("Solution value = " + model.ObjValue);
shifts.ForEach(shift =>
{
ILinearNumExpr sumAssigsExpr = model.LinearNumExpr();
employees.ForEach(employee =>
{
string name = getAssignVarName(employee, shift);
});
model.AddLe(sumAssigsExpr, shift.shiftNumber, "NoOverAssignConst");
});
}
else
{
}
}
catch (System.Exception ex)
{
Response.ContentType = "application/json; charset=utf-8";
Response.Write(ex.Message);
Response.End();
}
writer.Close();
}
/// <summary>
/// return a list of words that can be played, left over letters are the last word
/// </summary>
/// <param name="chips">All of the chips to conisder, first ones are on the board</param>
/// <param name="numberOnBoard">The number of leading chips are on the board already</param>
/// <returns></returns>
public static List <Chip[]> Solve(List <Chip> chips, int numberOnBoard, bool suppress = true)
{
if (chips == null || chips.Count == 0)
{
Utility.Warning("Chips is null or empty!");
return(null);
}
int chip_count = chips.Count;
int word_count = chip_count / 3 + 1;
Cplex model = new Cplex();
IIntVar[] X = model.BoolVarArray(chip_count); // should we place the ith chip
IIntVar[][] Y = new IIntVar[chip_count][]; // which word do we place the ith chip on
IIntVar[] WO = model.BoolVarArray(word_count); // flag for if the jth word is an order word or not
IIntVar[] WC = model.BoolVarArray(word_count); // flag for if the jth word is a color word or not
IIntVar[] WN = model.BoolVarArray(word_count); // flag for if the jth word is not used or is used
IIntVar[][] UO = new IIntVar[word_count][]; // what is the number associated with this word (used only if a color word)
IIntVar[][] UC = new IIntVar[word_count][]; // what is the color associated with this word (used only if a order word)
IIntVar[][] PC = new IIntVar[word_count][]; // if i maps to word j and j is a color word, this will be 1 at [j][i]
IIntVar[][] PO = new IIntVar[word_count][]; // if i maps to word j and j is a order word, this will be 1 at [j][i] (if and only if)
// Initialize
for (int i = 0; i < chip_count; i++)
{
Y[i] = model.BoolVarArray(word_count);
}
for (int i = 0; i < word_count; i++)
{
UC[i] = model.BoolVarArray(Chip.COLORCOUNT);
}
for (int i = 0; i < word_count; i++)
{
UO[i] = model.BoolVarArray(Chip.NUMBERCOUNT);
}
for (int i = 0; i < word_count; i++)
{
PC[i] = model.BoolVarArray(chip_count);
}
for (int i = 0; i < word_count; i++)
{
PO[i] = model.BoolVarArray(chip_count);
}
// for each word which chips map to it
IIntVar[][] Yt = new IIntVar[word_count][];
for (int i = 0; i < word_count; i++)
{
Yt[i] = new IIntVar[chip_count];
for (int j = 0; j < chip_count; j++)
{
Yt[i][j] = Y[j][i];
}
}
// maximize the number of placed chips
model.AddMaximize(model.Sum(X));
// if we place i, we need to map it to some word
for (int i = 0; i < chip_count; i++)
{
model.AddLe(X[i], model.Sum(Y[i]));
}
// we can map to at most 1 value
for (int i = 0; i < chip_count; i++)
{
model.AddLe(model.Sum(Y[i]), 1);
}
// if any chip maps to word j, turn the not used flag off
for (int j = 0; j < word_count; j++)
{
for (int i = 0; i < chip_count; i++)
{
model.AddLe(Y[i][j], model.Diff(1, WN[j]));
}
}
// if a word is used, make sure it has at least 3 chips
for (int j = 0; j < word_count; j++)
{
model.AddLe(model.Prod(3, model.Diff(1, WN[j])), model.Sum(Yt[j]));
}
// first 'numberOnBoard' chips are on the board and thus must be placed
for (int i = 0; i < numberOnBoard; i++)
{
model.AddEq(X[i], 1);
}
// each word is either an order word or a color word
for (int i = 0; i < word_count; i++)
{
model.AddEq(model.Sum(WO[i], WC[i], WN[i]), 1);
}
// if i maps to word j and j is a color word make sure PC[j][i] is 1
for (int j = 0; j < word_count; j++)
{
for (int i = 0; i < chip_count; i++)
{
model.AddGe(model.Sum(1, PC[j][i]), model.Sum(WC[j], Y[i][j]));
}
}
// if i maps to word j and j is a order word, PO will be 1 at [j][i] (if and only if)
for (int j = 0; j < word_count; j++)
{
for (int i = 0; i < chip_count; i++)
{
model.AddGe(model.Sum(1, PO[j][i]), model.Sum(WO[j], Y[i][j]));
model.AddLe(PO[j][i], WO[j]);
model.AddLe(PO[j][i], Y[i][j]);
}
}
// ************************************************
// ************ TYPE CONSTRAINTS ******************
// ************************************************
for (int i = 0; i < chip_count; i++)
{
for (int j = 0; j < word_count; j++)
{
// if this is a color word and a chip maps to it then the numbers of each chip on this word must match
for (int k = 0; k < Chip.NUMBERCOUNT; k++)
{
var bnd = model.Sum(WO[j], model.Diff(1, Y[i][j]));
model.AddLe(model.Diff(UO[j][k], chips[i].number[k] ? 1 : 0), bnd);
model.AddLe(model.Diff(chips[i].number[k] ? 1 : 0, UO[j][k]), bnd);
}
// if this is a order word and a chip maps to it then the colors of each chip on this word must match
for (int k = 0; k < Chip.COLORCOUNT; k++)
{
var bnd = model.Sum(WC[j], model.Diff(1, Y[i][j]));
model.AddLe(model.Diff(UC[j][k], chips[i].color[k] ? 1 : 0), bnd);
model.AddLe(model.Diff(chips[i].color[k] ? 1 : 0, UC[j][k]), bnd);
}
}
}
// if this is a color word, ensure that at most one of each color is allowed
for (int j = 0; j < word_count; j++)
{
for (int k = 0; k < Chip.COLORCOUNT; k++)
{
int[] colstack = new int[chip_count];
for (int i = 0; i < chip_count; i++)
{
colstack[i] = chips[i].color[k] ? 1 : 0;
}
model.Add(model.IfThen(model.Eq(WC[j], 1), model.Le(model.ScalProd(colstack, PC[j]), 1)));
}
}
// ensure that the numbers in an order word are sequential
for (int j = 0; j < word_count; j++)
{
IIntVar[] V = model.BoolVarArray(Chip.NUMBERCOUNT);
for (int k = 0; k < Chip.NUMBERCOUNT; k++)
{
int[] numstack = new int[chip_count];
for (int i = 0; i < chip_count; i++)
{
numstack[i] = chips[i].number[k] ? 1 : 0;
}
// if this is an order word put the binary numbers into a vector of flags for those numbers
model.Add(model.IfThen(model.Eq(WO[j], 1), model.Eq(model.ScalProd(numstack, PO[j]), V[k])));
}
// for each number either the next flag is strictly larger, meaning the start of the sequence
// or every flag after a decrease is 0, the end of a sequence
for (int i = 0; i < Chip.NUMBERCOUNT - 1; i++)
{
IIntVar Z = model.BoolVar();
model.AddLe(model.Diff(V[i], V[i + 1]), Z);
for (int k = i + 1; k < Chip.NUMBERCOUNT; k++)
{
model.AddLe(V[k], model.Diff(1, Z));
}
}
}
List <Chip[]> words = new List <Chip[]>();
if (suppress)
{
model.SetOut(null);
}
Utility.Log("Thinking...");
if (model.Solve())
{
for (int j = 0; j < word_count; j++)
{
if (model.GetValue(WN[j]) > 0.5)
{
continue;
}
List <Chip> word = new List <Chip>();
double[] flags = model.GetValues(Yt[j]);
for (int i = 0; i < chip_count; i++)
{
if (flags[i] > 0.5)
{
word.Add(chips[i]);
}
}
// if this is a color word else it is an order word
if (model.GetValue(WC[j]) > 0.5)
{
words.Add(word.OrderBy(p => Array.IndexOf(p.color, true)).ToArray());
}
else
{
words.Add(word.OrderBy(p => Array.IndexOf(p.number, true)).ToArray());
}
}
}
else
{
Utility.Warning("No possible moves found!");
}
model.Dispose();
Chip[] notplayed = chips.Where(p => !words.Any(q => q.Contains(p))).ToArray();
words.Add(notplayed);
return(words);
}
public void AddObjective()
{
theModel.AddMaximize(theModel.Diff(theModel.ScalProd(LaneMargin, LoadedMoves), theModel.ScalProd(LaneCost, EmptyMoves)));
}
public static void Main(string[] args)
{
try {
string filename = "../../../../examples/data/steel.dat";
if (args.Length > 0)
{
filename = args[0];
}
ReadData(filename);
Cplex cplex = new Cplex();
// VARIABLES
INumVar[][] Make = new INumVar[_nProd][];
for (int p = 0; p < _nProd; p++)
{
Make[p] = cplex.NumVarArray(_nTime, 0.0, System.Double.MaxValue);
}
INumVar[][] Inv = new INumVar[_nProd][];
for (int p = 0; p < _nProd; p++)
{
Inv[p] = cplex.NumVarArray(_nTime, 0.0, System.Double.MaxValue);
}
INumVar[][] Sell = new INumVar[_nProd][];
for (int p = 0; p < _nProd; p++)
{
Sell[p] = new INumVar[_nTime];
for (int t = 0; t < _nTime; t++)
{
Sell[p][t] = cplex.NumVar(0.0, _market[p][t]);
}
}
// OBJECTIVE
ILinearNumExpr TotalRevenue = cplex.LinearNumExpr();
ILinearNumExpr TotalProdCost = cplex.LinearNumExpr();
ILinearNumExpr TotalInvCost = cplex.LinearNumExpr();
for (int p = 0; p < _nProd; p++)
{
for (int t = 1; t < _nTime; t++)
{
TotalRevenue.AddTerm(_revenue[p][t], Sell[p][t]);
TotalProdCost.AddTerm(_prodCost[p], Make[p][t]);
TotalInvCost.AddTerm(_invCost[p], Inv[p][t]);
}
}
cplex.AddMaximize(cplex.Diff(TotalRevenue,
cplex.Sum(TotalProdCost, TotalInvCost)));
// TIME AVAILABILITY CONSTRAINTS
for (int t = 0; t < _nTime; t++)
{
ILinearNumExpr availExpr = cplex.LinearNumExpr();
for (int p = 0; p < _nProd; p++)
{
availExpr.AddTerm(1.0 / _rate[p], Make[p][t]);
}
cplex.AddLe(availExpr, _avail[t]);
}
// MATERIAL BALANCE CONSTRAINTS
for (int p = 0; p < _nProd; p++)
{
cplex.AddEq(cplex.Sum(Make[p][0], _inv0[p]),
cplex.Sum(Sell[p][0], Inv[p][0]));
for (int t = 1; t < _nTime; t++)
{
cplex.AddEq(cplex.Sum(Make[p][t], Inv[p][t - 1]),
cplex.Sum(Sell[p][t], Inv[p][t]));
}
}
cplex.ExportModel("steel.lp");
if (cplex.Solve())
{
System.Console.WriteLine("Solution status = " + cplex.GetStatus());
System.Console.WriteLine();
System.Console.WriteLine("Total Profit = " + cplex.ObjValue);
System.Console.WriteLine();
System.Console.WriteLine("\tp\tt\tMake\tInv\tSell");
for (int p = 0; p < _nProd; p++)
{
for (int t = 0; t < _nTime; t++)
{
System.Console.WriteLine("\t" + p + "\t" + t + "\t" + cplex.GetValue(Make[p][t]) +
"\t" + cplex.GetValue(Inv[p][t]) + "\t" + cplex.GetValue(Sell[p][t]));
}
}
}
cplex.End();
}
catch (ILOG.Concert.Exception exc) {
System.Console.WriteLine("Concert exception '" + exc + "' caught");
}
catch (System.IO.IOException exc) {
System.Console.WriteLine("Error reading file " + args[0] + ": " + exc);
}
catch (InputDataReader.InputDataReaderException exc) {
System.Console.WriteLine(exc);
}
}
public static void Main(string[] args)
{
try {
string filename = "../../../../examples/data/steel.dat";
if ( args.Length > 0 )
filename = args[0];
ReadData(filename);
Cplex cplex = new Cplex();
// VARIABLES
INumVar[][] Make = new INumVar[_nProd][];
for (int p = 0; p < _nProd; p++) {
Make[p] = cplex.NumVarArray(_nTime, 0.0, System.Double.MaxValue);
}
INumVar[][] Inv = new INumVar[_nProd][];
for (int p = 0; p < _nProd; p++) {
Inv[p] = cplex.NumVarArray(_nTime, 0.0, System.Double.MaxValue);
}
INumVar[][] Sell = new INumVar[_nProd][];
for (int p = 0; p < _nProd; p++) {
Sell[p] = new INumVar[_nTime];
for (int t = 0; t < _nTime; t++) {
Sell[p][t] = cplex.NumVar(0.0, _market[p][t]);
}
}
// OBJECTIVE
ILinearNumExpr TotalRevenue = cplex.LinearNumExpr();
ILinearNumExpr TotalProdCost = cplex.LinearNumExpr();
ILinearNumExpr TotalInvCost = cplex.LinearNumExpr();
for (int p = 0; p < _nProd; p++) {
for (int t = 1; t < _nTime; t++) {
TotalRevenue.AddTerm (_revenue[p][t], Sell[p][t]);
TotalProdCost.AddTerm(_prodCost[p], Make[p][t]);
TotalInvCost.AddTerm (_invCost[p], Inv[p][t]);
}
}
cplex.AddMaximize(cplex.Diff(TotalRevenue,
cplex.Sum(TotalProdCost, TotalInvCost)));
// TIME AVAILABILITY CONSTRAINTS
for (int t = 0; t < _nTime; t++) {
ILinearNumExpr availExpr = cplex.LinearNumExpr();
for (int p = 0; p < _nProd; p++) {
availExpr.AddTerm(1.0/_rate[p], Make[p][t]);
}
cplex.AddLe(availExpr, _avail[t]);
}
// MATERIAL BALANCE CONSTRAINTS
for (int p = 0; p < _nProd; p++) {
cplex.AddEq(cplex.Sum(Make[p][0], _inv0[p]),
cplex.Sum(Sell[p][0], Inv[p][0]));
for (int t = 1; t < _nTime; t++) {
cplex.AddEq(cplex.Sum(Make[p][t], Inv[p][t-1]),
cplex.Sum(Sell[p][t], Inv[p][t]));
}
}
cplex.ExportModel("steel.lp");
if ( cplex.Solve() ) {
System.Console.WriteLine("Solution status = " + cplex.GetStatus());
System.Console.WriteLine();
System.Console.WriteLine("Total Profit = " + cplex.ObjValue);
System.Console.WriteLine();
System.Console.WriteLine("\tp\tt\tMake\tInv\tSell");
for (int p = 0; p < _nProd; p++) {
for (int t = 0; t < _nTime; t++) {
System.Console.WriteLine("\t" + p +"\t" + t +"\t" + cplex.GetValue(Make[p][t]) +
"\t" + cplex.GetValue(Inv[p][t]) +"\t" + cplex.GetValue(Sell[p][t]));
}
}
}
cplex.End();
}
catch (ILOG.Concert.Exception exc) {
System.Console.WriteLine("Concert exception '" + exc + "' caught");
}
catch (System.IO.IOException exc) {
System.Console.WriteLine("Error reading file " + args[0] + ": " + exc);
}
catch (InputDataReader.InputDataReaderException exc) {
System.Console.WriteLine(exc);
}
}
//A quadratic interger program for graph matching,
//When tuned with the right similarity function it can represent the GED problem.
// See Pattern Reocgnition Paper paper On the unification of graph matching and graph edit distance paper.
//The directed version has not been tested yet
public override void QAPGMGED()
{
//some important variables
int nbNode1 = graph1.ListNodes.Count;
int nbNode2 = graph2.ListNodes.Count;
int nbvar = nbNode1 * nbNode2; // number of variables
int nbcontraintes = nbNode1 + nbNode2; // number of constraints
Graphs.Label nodeepslabel;
//Adjacency matrices
MatrixLibrary.Matrix adj1 = graph1.calculateAdjacencyMatrix();
MatrixLibrary.Matrix adj2 = graph2.calculateAdjacencyMatrix();
//Similarity matrix
Double[,] S = new Double[nbvar, nbvar];
//Creation of the Similarity matrix
//4 for loops integrated
int countrow = -1;
for (int i = 0; i < nbNode1; i++)
{
Node nodei = graph1.ListNodes[i];
for (int k = 0; k < nbNode2; k++)
{
Node nodek = graph2.ListNodes[k];
countrow = countrow + 1;
int countcol = -1;
for (int j = 0; j < nbNode1; j++)
{
Node nodej = graph1.ListNodes[j];
for (int l = 0; l < nbNode2; l++)
{
Node nodel = graph2.ListNodes[l];
countcol = countcol + 1;
if (i == j && k == l) // Similarity between nodes
{
Type typeLabel = nodei.Label.GetType();
object obj = Activator.CreateInstance(typeLabel);
nodeepslabel = (Graphs.Label)obj;
nodeepslabel.Id = ConstantsAC.EPS_ID;
double costsub = nodei.Label.dissimilarity(nodek.Label);
double costdel = nodei.Label.dissimilarity(nodeepslabel);
double costins = nodeepslabel.dissimilarity(nodek.Label);
double cost = costsub - costdel - costins;
cost *= -1;
S[countrow, countcol] = cost;
}
else // Similarity between edges
{
int connect1 = (int)adj1[i, j];
int connect2 = (int)adj2[k, l];
if (connect1 == 1 && connect2 == 1) // Two edges are connected
{
Edge ij = findedge(nodei, nodej);
Edge kl = findedge(nodek, nodel);
Type typeLabel = ij.Label.GetType();
object obj = Activator.CreateInstance(typeLabel);
nodeepslabel = (Graphs.Label)obj;
nodeepslabel.Id = ConstantsAC.EPS_ID;
double costsub = ij.Label.dissimilarity(kl.Label);
double costdel = ij.Label.dissimilarity(nodeepslabel);
double costins = nodeepslabel.dissimilarity(kl.Label);
double cost = costsub - costdel - costins;
cost *= -1;
//cost *= 0.5;
S[countrow, countcol] = cost;
}
}
}
}
}
}
//We compute the constant that represents the
//deletion and insertion of the nodes and edges
//deletions of the nodes of g1
double constante = 0;
for (int i = 1; i <= nbNode1; i++)
{
Type typeLabel = graph1.ListNodes[i - 1].Label.GetType();
object obj = Activator.CreateInstance(typeLabel);
nodeepslabel = (Graphs.Label)obj;
nodeepslabel.Id = ConstantsAC.EPS_ID;
constante += (graph1.ListNodes[i - 1].Label).dissimilarity(nodeepslabel);
}
//deletions of the edges of g1
for (int ij = 1; ij <= nbEdge1; ij++)
{
Type typeLabel = graph1.ListEdges[ij - 1].Label.GetType();
object obj = Activator.CreateInstance(typeLabel);
nodeepslabel = (Graphs.Label)obj;
nodeepslabel.Id = ConstantsAC.EPS_ID;
double costEdge = (graph1.ListEdges[ij - 1].Label).dissimilarity(nodeepslabel);
constante += costEdge;
}
//insertions of the nodes of g2
for (int k = 1; k <= nbNode2; k++)
{
Type typeLabel = graph2.ListNodes[k - 1].Label.GetType();
object obj = Activator.CreateInstance(typeLabel);
nodeepslabel = (Graphs.Label)obj;
nodeepslabel.Id = ConstantsAC.EPS_ID;
constante += (graph2.ListNodes[k - 1].Label).dissimilarity(nodeepslabel);
}
//insertions of the edges of g2
for (int kl = 1; kl <= nbEdge2; kl++)
{
Type typeLabel = graph2.ListEdges[kl - 1].Label.GetType();
object obj = Activator.CreateInstance(typeLabel);
nodeepslabel = (Graphs.Label)obj;
nodeepslabel.Id = ConstantsAC.EPS_ID;
double costEdge = (graph2.ListEdges[kl - 1].Label).dissimilarity(nodeepslabel);
constante += costEdge;
}
// the number of variables is the number of columns
int nbCols = nbvar;
// the number of constraints is the number of rows
int nbRows = nbcontraintes;
List <string> colNameList = new List <string>();
//We create the name of the vrariables for all couples of nodes in g1 and g2
for (int i = 0; i < nbNode1; i++)
{
for (int k = 0; k < nbNode2; k++)
{
colNameList.Add("x_" + graph1.ListNodes[i].Id + "," + graph2.ListNodes[k].Id + "");
}
}
try
{
cplex = new Cplex(); // creation du modèle CPLEX
this.ilpMatrix = cplex.AddLPMatrix(); // matrice du pb (initialisée à 0 colonnes et 0 lignes)
ColumnArray colonnes = cplex.ColumnArray(ilpMatrix, nbCols); // définition des variables-colonnes du pb
// ajout des variables-colonnes dans la matrice du pb
// y is a vector
INumVar[] y = cplex.NumVarArray(colonnes, 0, 1, NumVarType.Bool, colNameList.ToArray());
#region création fonction objectif
// CALCUL DES COEFFICIENTS
// We create the ojective function
INumExpr[] coeffs_expr = new INumExpr[nbvar * nbvar]; // vecteur des coeffs des coûts des arrêtes
int count = 0;
for (int ik = 0; ik < nbvar; ik++)
{
for (int jl = 0; jl < nbvar; jl++)
{
coeffs_expr[count] = cplex.Prod(y[ik], y[jl]);
coeffs_expr[count] = cplex.Prod(S[ik, jl], coeffs_expr[count]);
count = count + 1;
}
}
INumExpr ff = cplex.Sum(coeffs_expr);
INumExpr gg = cplex.Sum(ff, cplex.Constant(-constante));
cplex.AddMaximize(gg);
#endregion
#region définition des contraintes du pb
// We create the constraints
// The sum of x variables by rows
for (int i = 0; i < nbNode1; i++)
{
INumVar[] sommeLignes = new INumVar[nbNode2];
for (int k = 0; k < nbNode2; k++)
{
string nameVarik = "x_" + i + "," + k;
int indexik = SolverCPLEX.GetIndexByName(y, nameVarik);
sommeLignes[k] = y[indexik];
}
cplex.AddLe(cplex.Sum(sommeLignes), 1);
}
// The sum of x variables by columns
for (int k = 0; k < nbNode2; k++)
{
INumVar[] sommeLignes = new INumVar[nbNode1];
for (int i = 0; i < nbNode1; i++)
{
string nameVarik = "x_" + i + "," + k;
int indexik = SolverCPLEX.GetIndexByName(y, nameVarik);
sommeLignes[i] = y[indexik];
}
cplex.AddLe(cplex.Sum(sommeLignes), 1);
}
#endregion
}
catch (ILOG.Concert.Exception e)
{
System.Console.WriteLine("Concert exception `" + e + "` caught");
}
}
static void Main(string[] args)
{
// Portfolio asset daily price data:
var assets = new[] { "A", "B", "C" };
var prices = new double[][] {
new double[] { .02, .03, .02, .05 },
new double[] { .01, .01, .05, .01 },
new double[] { .1, .05, .04, .02 },
};
// Pre-process data
var covmat = CovarianceMatrix(prices);
DisplayCovariance(covmat);
var expReturns = AssetReturns(prices);
DisplayReturns(assets, expReturns);
var rho = 0.05;
// Create LP model
try
{
Cplex cplex = new Cplex();
// Weights bounded 0 <= weight <= 1.0
var weights = cplex.NumVarArray(assets.Length, 0, 1.0);
// x = weights.
// Expected (mean) return of portfolio: ∑ᵢ x[i]*assetReturn[i]
var expRet = cplex.ScalProd(expReturns, weights);
// Portfolio variance : xᵀ∑x (matrix form of bi-linear: ∑ᵢ∑ⱼ x[i]*x[j]*cov(i,j)
// where ∑ is the covariance matrix m*m of m assets.
var pvar = cplex.QuadNumExpr();
for (int i = 0; i < assets.Length; ++i)
{
for (int j = 0; j < assets.Length; ++j)
{
pvar.AddTerm(covmat[i][j], weights[i], weights[j]);
}
}
// Objective (maximize): portfolio return - (Rho/2) * portfolio variance.
// Where 0 <= Rho <= 1 is the desired risk tolerance.
var obj = cplex.Diff(expRet, cplex.Prod(rho / 2, pvar));
cplex.AddMaximize(obj);
// Subject to:
// Sum of weights <= 1.0
cplex.AddEq(cplex.Sum(weights), 1.0);
// Exports model definition to readable LP format
cplex.ExportModel(@"c:\users\mike\cplexfin.lp");
// Solve and print results
if (cplex.Solve())
{
var w = new double[weights.Length];
Console.WriteLine("\nResulting Weights:");
for (int i = 0; i < weights.Length; i++)
{
Console.WriteLine($"{i + 1} : {cplex.GetValue(weights[i]) * 100:0.0}%");
w[i] = cplex.GetValue(weights[i]);
}
var totReturn = WeightedReturn(w, expReturns);
var totVariance = PortfolioVariance(w, covmat);
Console.WriteLine($"Total Return: {totReturn:0.00}, Total Variance: {totVariance:0.0000}");
}
cplex.End();
}
catch (ILOG.Concert.Exception e)
{
System.Console.WriteLine("Concert exception caught: " + e);
}
Console.WriteLine("Press any key to quit.");
Console.ReadKey();
}
/// <summary>
/// Getting maximum congruency for naming parameter sets A or B. Linear Programming optimization model.
/// Eq. (4) and Table II in DOI:10.1109/CEC.2019.8790261
/// The matrices need to be manually entered here in the script (cTopA, cTopB, cBtmA, cBtmB)
/// </summary>
internal static void IdentifyTwoBestParameterSets(string caseSolver)
{
Cplex cpl = new Cplex();
int N = 7;
//string caseSolver = "ES"; //"SGA" (default), "ES", "PSO", "FIPS"
int[] cTopA;
int[] cTopB;
int[] cBtmA;
int[] cBtmB;
switch (caseSolver)
{
default:
cTopA = new int[] { 11, 1, 1, 13, 0, 0, 2 };
cTopB = new int[] { 0, 5, 3, 0, 5, 5, 5 };
cBtmA = new int[] { 2, 12, 12, 0, 14, 14, 9 };
cBtmB = new int[] { 4, 0, 1, 4, 0, 0, 0 };
break;
case "ES":
cTopA = new int[] { 4, 6, 9, 3, 11, 11, 5 };
cTopB = new int[] { 2, 3, 1, 3, 1, 3, 3 };
cBtmA = new int[] { 4, 5, 2, 8, 0, 0, 6 };
cBtmB = new int[] { 3, 1, 3, 2, 5, 3, 2 };
break;
case "PSO":
cTopA = new int[] { 1, 3, 2, 3, 3, 7, 5 };
cTopB = new int[] { 12, 11, 3, 1, 7, 8, 5 };
cBtmA = new int[] { 7, 5, 6, 5, 5, 1, 3 };
cBtmB = new int[] { 0, 1, 9, 11, 5, 4, 7 };
break;
case "FIPS":
cTopA = new int[] { 6, 6, 7, 3, 5, 0, 8 };
cTopB = new int[] { 4, 6, 6, 9, 5, 9, 1 };
cBtmA = new int[] { 4, 4, 3, 7, 5, 10, 2 };
cBtmB = new int[] { 6, 4, 4, 1, 5, 1, 9 };
break;
}
INumVar[] xTopA = new INumVar[N];
INumVar[] xBtmB = new INumVar[N];
INumVar[] xTopB = new INumVar[N];
INumVar[] xBtmA = new INumVar[N];
ILinearNumExpr value = cpl.LinearNumExpr();
for (int n = 0; n < N; n++)
{
xTopA[n] = cpl.BoolVar();
xBtmB[n] = cpl.BoolVar();
xTopB[n] = cpl.BoolVar();
xBtmA[n] = cpl.BoolVar();
cpl.AddEq(cpl.Sum(xTopB[n], xTopA[n]), 1);
cpl.AddEq(cpl.Sum(xBtmA[n], xBtmB[n]), 1);
cpl.AddEq(cpl.Sum(xTopA[n], xBtmA[n]), 1);
cpl.AddEq(cpl.Sum(xTopB[n], xBtmB[n]), 1);
value.AddTerm(xTopA[n], cTopA[n]);
value.AddTerm(xTopB[n], cTopB[n]);
value.AddTerm(xBtmA[n], cBtmA[n]);
value.AddTerm(xBtmB[n], cBtmB[n]);
}
cpl.AddMaximize(value);
cpl.Solve();
Console.WriteLine("Parameter Grouping for Solver: {0}", caseSolver);
for (int n = 0; n < N; n++)
{
Console.WriteLine("n: {0}", n);
Console.WriteLine("xtopA: ;{0};, _____, xTopB: ;{1};", cpl.GetValue(xTopA[n]), cpl.GetValue(xTopB[n]));
Console.WriteLine("xbtmB: ;{0};, _____, xBtmA: ;{1};", cpl.GetValue(xBtmB[n]), cpl.GetValue(xBtmA[n]));
}
Console.WriteLine("cost: {0}", cpl.GetObjValue());
Console.ReadKey();
}
static void Main(string[] args)
{
double BinCap = 0;
var dict = InputReader.ReadDataFile(ref BinCap);
Cplex cplex = new Cplex();
Dictionary <string, INumVar> dictvariables = new Dictionary <string, INumVar>();
Dictionary <string, IRange> dictconstraints = new Dictionary <string, IRange>();
IObjective objective = cplex.AddMinimize();
foreach (var vari in dict.Keys)
{
var cname = "C" + vari;
dictconstraints.Add(cname, cplex.AddRange(1, Int32.MaxValue, cname));
}
Dictionary <int, Set> InitialSets = new Dictionary <int, Set>();;
InitialSets.Add(1, new Set(new List <int> {
1, 5
}));
InitialSets.Add(2, new Set(new List <int> {
2, 5
}));
InitialSets.Add(3, new Set(new List <int> {
3, 5
}));
InitialSets.Add(4, new Set(new List <int> {
4, 5
}));
//Add intial sets to the model
foreach (var vari in InitialSets)
{
var setID = vari.Key.ToString();
Column VarSet = cplex.Column(objective, 1);
foreach (var members in vari.Value.member)
{
var cname = "C" + members;
VarSet = VarSet.And(cplex.Column(dictconstraints[cname], 1));
}
dictvariables.Add(setID, cplex.NumVar(VarSet, 0, 1, NumVarType.Float));
}
cplex.Solve();
var duals = getDuals(cplex, dictconstraints);
var solution = getSolution(cplex, dictvariables);
Console.WriteLine("The objective value is {0}", cplex.GetObjValue());
int piter = 0;
int fixediter = 0;
Dictionary <string, INumVar> Fixedvar = new Dictionary <string, INumVar>();
while (true)
{
//Formulate Pricing Problem
Cplex pcplex = new Cplex();
IObjective pobjective = pcplex.AddMaximize();
piter++;
//Add Bin Capacity Constraint
IRange Knapsack = pcplex.AddRange(0, BinCap, "Bin");
Dictionary <string, INumVar> pdictvar = new Dictionary <string, INumVar>();
foreach (var vari in dict.Keys)
{
var varname = vari.ToString();
var objcoeff = duals["C" + varname];
Column item = pcplex.Column(pobjective, objcoeff);
item = item.And(pcplex.Column(Knapsack, dict[vari]));
pdictvar.Add(varname, pcplex.NumVar(item, 0, 1, NumVarType.Int));
}
pcplex.Solve();
if (pcplex.GetObjValue() > 1)
{
Console.WriteLine("Pricing Iteration: {0} and obj value is {1} ", piter, pcplex.GetObjValue());
var psolution = getSolution(pcplex, pdictvar);
List <int> sol = new List <int>();
foreach (var vari in psolution.Keys)
{
sol.Add(Convert.ToInt32(vari));
}
InitialSets.Add(InitialSets.Count + 1, new Set(sol));
var setID = (InitialSets.Count).ToString();
Column VarSet1 = cplex.Column(objective, 1);
foreach (var members in sol)
{
var cname = "C" + members;
VarSet1 = VarSet1.And(cplex.Column(dictconstraints[cname], 1));
}
dictvariables.Add(setID, cplex.NumVar(VarSet1, 0, 1, NumVarType.Float));
cplex.Solve();
Console.WriteLine("The objective value of cplex after adding column is {0}", cplex.GetObjValue());
duals = getDuals(cplex, dictconstraints);
solution = getSolution(cplex, dictvariables);
}
else
{
fixediter++;
bool fixedsomething = false;
//fix variables above 0.5
foreach (var val in solution)
{
if (val.Value > 0.5 && !Fixedvar.ContainsKey(val.Key))
{
Fixedvar.Add(val.Key, dictvariables[val.Key]);
dictvariables[val.Key].LB = 1;
fixedsomething = true;
}
}
if (!fixedsomething)
{
break;
}
cplex.Solve();
Console.WriteLine("The Fixing iterations is {0}", cplex.GetObjValue());
duals = getDuals(cplex, dictconstraints);
}
}
foreach (var vari in Fixedvar.Values)
{
vari.LB = 0;
}
IConversion IP = cplex.Conversion(dictvariables.Values.ToArray(), NumVarType.Int);
cplex.Add(IP);
cplex.SetParam(Cplex.DoubleParam.TiLim, 600);
cplex.Solve();
solution = getSolution(cplex, dictvariables);
Console.WriteLine("The objective value is {0}", cplex.GetObjValue());
}
public static void Main (string[] args) {
int nMonths = cost.Length;
int nProducts = cost[0].Length;
try {
Cplex cplex = new Cplex();
INumVar[] produce = cplex.NumVarArray(nMonths, 0, System.Double.MaxValue);
INumVar[][] use = new INumVar[nMonths][];
INumVar[][] buy = new INumVar[nMonths][];
INumVar[][] store = new INumVar[nMonths][];
for (int i = 0; i < nMonths; i++) {
use[i] = cplex.NumVarArray(nProducts, 0.0, System.Double.MaxValue);
buy[i] = cplex.NumVarArray(nProducts, 0.0, System.Double.MaxValue);
store[i] = cplex.NumVarArray(nProducts, 0.0, 1000.0);
}
for (int p = 0; p < nProducts; p++) {
store[nMonths-1][p].LB = 500.0;
store[nMonths-1][p].UB = 500.0;
}
INumExpr profit = cplex.NumExpr();
for (int i = 0; i < nMonths; i++) {
// Not more than 200 tons of vegetable oil can be refined
cplex.AddLe(cplex.Sum(use[i][v1], use[i][v2]), 200.0);
// Not more than 250 tons of non-vegetable oil can be refined
cplex.AddLe(cplex.Sum(use[i][o1], use[i][o2], use[i][o3]), 250.0);
// Constraints on food composition
cplex.AddLe(cplex.Prod(3.0,produce[i]),
cplex.Sum(cplex.Prod(8.8, use[i][v1]),
cplex.Prod(6.1, use[i][v2]),
cplex.Prod(2.0, use[i][o1]),
cplex.Prod(4.2, use[i][o2]),
cplex.Prod(5.0, use[i][o3])));
cplex.AddGe(cplex.Prod(6.0, produce[i]),
cplex.Sum(cplex.Prod(8.8, use[i][v1]),
cplex.Prod(6.1, use[i][v2]),
cplex.Prod(2.0, use[i][o1]),
cplex.Prod(4.2, use[i][o2]),
cplex.Prod(5.0, use[i][o3])));
cplex.AddEq(produce[i], cplex.Sum(use[i]));
// Raw oil can be stored for later use
if (i == 0) {
for (int p = 0; p < nProducts; p++)
cplex.AddEq(cplex.Sum(500.0, buy[i][p]),
cplex.Sum(use[i][p], store[i][p]));
}
else {
for (int p = 0; p < nProducts; p++)
cplex.AddEq(cplex.Sum(store[i-1][p], buy[i][p]),
cplex.Sum(use[i][p], store[i][p]));
}
// Logical constraints:
// The food cannot use more than 3 oils
// (or at least two oils must not be used)
cplex.AddGe(cplex.Sum(cplex.Eq(use[i][v1], 0),
cplex.Eq(use[i][v2], 0),
cplex.Eq(use[i][o1], 0),
cplex.Eq(use[i][o2], 0),
cplex.Eq(use[i][o3], 0)), 2);
// When an oil is used, the quantity must be at least 20 tons
for (int p = 0; p < nProducts; p++)
cplex.Add(cplex.Or(cplex.Eq(use[i][p], 0),
cplex.Ge(use[i][p], 20)));
// If products v1 or v2 are used, then product o3 is also used
cplex.Add(cplex.IfThen(cplex.Or(cplex.Ge(use[i][v1], 20),
cplex.Ge(use[i][v2], 20)),
cplex.Ge(use[i][o3], 20)));
// Objective function
profit = cplex.Sum (profit, cplex.Prod(150, produce[i]));
profit = cplex.Diff(profit, cplex.ScalProd(cost[i], buy[i]));
profit = cplex.Diff(profit, cplex.Prod(5, cplex.Sum(store[i])));
}
cplex.AddMaximize(profit);
if ( cplex.Solve() ) {
System.Console.WriteLine("Solution status = " + cplex.GetStatus());
System.Console.WriteLine(" Maximum profit = " + cplex.ObjValue);
for (int i = 0; i < nMonths; i++) {
System.Console.WriteLine(" Month {0}", i);
System.Console.Write(" . buy ");
for (int p = 0; p < nProducts; p++)
System.Console.Write("{0,8:F2} ", cplex.GetValue(buy[i][p]));
System.Console.WriteLine();
System.Console.Write(" . use ");
for (int p = 0; p < nProducts; p++)
System.Console.Write("{0,8:F2} ", cplex.GetValue(use[i][p]));
System.Console.WriteLine();
System.Console.Write(" . store ");
for (int p = 0; p < nProducts; p++)
System.Console.Write("{0,8:F2} ", cplex.GetValue(store[i][p]));
System.Console.WriteLine();
}
}
cplex.End();
}
catch (ILOG.Concert.Exception e) {
System.Console.WriteLine("Concert exception caught: " + e);
}
}
public override void Work()
{
//获取求解设置
decimal terminalFactor = _ctx.GetParameter("TerminalFactor", 0.001m);
int iteration = _ctx.GetParameter("Iteration", 10);
int resolution = _ctx.GetParameter("Resolution", 60);
decimal initMultipler = _ctx.GetParameter("InitMultiper", 0.1m);
string objectiveType = _ctx.GetParameter("ObjectiveType", "");
// 相对-绝对时间转化器
DiscreteTimeAdapter adapter
= new DiscreteTimeAdapter(_ctx.StartTime, _ctx.EndTime, resolution);
// 路径集
Dictionary <CustomerArrival, List <TravelPath> > pathDict
= new Dictionary <CustomerArrival, List <TravelPath> >();
#region 建立初始网络，搜索可行路径
//目标函数网络
var objgraph = ObjectNetworkFactory.Create(objectiveType, _ctx, adapter); //new ObjectTravelHyperNetwork(_ctx, adapter);
objgraph.Build();
//基础网络
var basicGraph = new BasicTravelHyperNetwork(_ctx, adapter);
basicGraph.Build();
SubTasks.Clear();
foreach (CustomerArrival customer in _ctx.Pal)
{
Task ta = factory.StartNew(() =>
{
var ori = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).OriSta;
var des = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).DesSta;
var paths = DepthFirstSearcher.FindAllPaths(basicGraph,
new TravelHyperNode()
{
Time = adapter.ConvertToDiscreteTime(customer.ArriveTime), Station = ori, Price = 0
},
new TravelHyperNode()
{
Time = adapter.Horizon + 1440, Station = des, Price = 0
});
pathDict.Add(customer, new List <TravelPath>());
foreach (var path in paths)
{
pathDict[customer].Add(new TravelPath(basicGraph, path));
}
});
SubTasks.Add(ta);
}
Task.WaitAll(SubTasks.ToArray());
#endregion
#region 构建对偶问题 with CPLEX
Cplex model = new Cplex();
model.SetOut(null);
INumVar theta = model.NumVar(double.MinValue, double.MaxValue); //θ
model.AddMaximize(theta);
Dictionary <IServiceSegment, INumVar> dual_rho = _ctx.Wor.RailwayTimeTable.Trains
.SelectMany(i => i.ServiceSegments).ToDictionary(i => i, i =>
model.NumVar(0, double.MaxValue));
Dictionary <CustomerArrival, Dictionary <TravelPath, INumVar> > dual_mu =
new Dictionary <CustomerArrival, Dictionary <TravelPath, INumVar> >();
foreach (CustomerArrival customer in _ctx.Pal)
{
dual_mu.Add(customer, new Dictionary <TravelPath, INumVar>());
foreach (var path in pathDict[customer])
{
dual_mu[customer].Add(path, model.NumVar(0, double.MaxValue));
}
}
Dictionary <IEdge <TravelHyperNode>, INumVar> dual_lambda =
new Dictionary <IEdge <TravelHyperNode>, INumVar>();
foreach (CustomerArrival customer in _ctx.Pal)
{
foreach (var path in pathDict[customer])
{
if (!dual_lambda.ContainsKey(path.ReservationArc))
{
dual_lambda.Add(path.ReservationArc, model.NumVar(0, double.MaxValue));
}
}
}
#endregion
#region 变量与乘子
//决策变量 x
Dictionary <CustomerArrival, TravelPath> x
= new Dictionary <CustomerArrival, TravelPath>();
foreach (CustomerArrival customer in _ctx.Pal)
{
x.Add(customer, new TravelPath());
}
//决策变量 w
Dictionary <ITrainTrip, Dictionary <IRailwayStation, PricePath> > w
= new Dictionary <ITrainTrip, Dictionary <IRailwayStation, PricePath> >();
foreach (var train in _ctx.Wor.RailwayTimeTable.Trains)
{
w.Add(train, new Dictionary <IRailwayStation, PricePath>());
foreach (var sta in _ctx.Wor.Net.StationCollection)
{
w[train].Add(sta, null);
}
}
//辅助变量 y
//记录每条弧在当前w的取值下是否可行(available)，值为true = 可行；false = 不可行
//超出了y记录的reservation arc 不会有人走
Dictionary <IEdge <TravelHyperNode>, bool> y
= new Dictionary <IEdge <TravelHyperNode>, bool>();
foreach (var p in pathDict.Values.SelectMany(i => i))
{
if (p.ReservationArc != null && !y.ContainsKey(p.ReservationArc))
{
y.Add(p.ReservationArc, false);
}
}
//拉格朗日乘子 rho
Dictionary <IServiceSegment, decimal> LM_rho = _ctx.Wor.RailwayTimeTable.Trains
.SelectMany(i => i.ServiceSegments).ToDictionary(i => i, i => initMultipler);
//拉格朗日乘子 rho 迭代方向
Dictionary <IServiceSegment, decimal> Grad_rho = _ctx.Wor.RailwayTimeTable.Trains
.SelectMany(i => i.ServiceSegments).ToDictionary(i => i, i => initMultipler);
//拉格朗日乘子 mu
Dictionary <CustomerArrival, Dictionary <TravelPath, decimal> > LM_mu
= new Dictionary <CustomerArrival, Dictionary <TravelPath, decimal> >();
foreach (CustomerArrival customer in _ctx.Pal)
{
LM_mu.Add(customer, new Dictionary <TravelPath, decimal>());
foreach (var path in pathDict[customer])
{
LM_mu[customer].Add(path, initMultipler);
}
}
//拉格朗日乘子 mu 迭代方向
Dictionary <CustomerArrival, Dictionary <TravelPath, decimal> > Grad_mu
= new Dictionary <CustomerArrival, Dictionary <TravelPath, decimal> >();
foreach (CustomerArrival customer in _ctx.Pal)
{
Grad_mu.Add(customer, new Dictionary <TravelPath, decimal>());
foreach (var path in pathDict[customer])
{
Grad_mu[customer].Add(path, initMultipler);
}
}
//拉格朗日乘子 lambda
Dictionary <IEdge <TravelHyperNode>, decimal> LM_lambda = new Dictionary <IEdge <TravelHyperNode>, decimal>();
//WARNING: 这里缺少了没有旅客选择的reservation arc
foreach (CustomerArrival customer in _ctx.Pal)
{
foreach (var path in pathDict[customer])
{
if (!LM_lambda.ContainsKey(path.ReservationArc))
{
LM_lambda.Add(path.ReservationArc, initMultipler);
}
}
}
//拉格朗日乘子 lambda 迭代方向
Dictionary <IEdge <TravelHyperNode>, decimal> Grad_lambda = new Dictionary <IEdge <TravelHyperNode>, decimal>();
foreach (CustomerArrival customer in _ctx.Pal)
{
foreach (var path in pathDict[customer])
{
if (!Grad_lambda.ContainsKey(path.ReservationArc))
{
Grad_lambda.Add(path.ReservationArc, initMultipler);
}
}
}
#endregion
decimal bigM1 = pathDict.Max(i => i.Value.Max(j => basicGraph.GetPathCost(j)));
decimal bigM2 = _ctx.Pal.Count();
decimal bigM = Math.Max(bigM1, bigM2);
decimal lowerBound = decimal.MinValue;
decimal upperBound = decimal.MaxValue;
bool flag = false;//对偶问题有解
PrintIterationInfo($"Iteration Number, Lower Bound, Upper Bound, Best Lower Bound, Best Upper Bound, Total Gap(%) ");
for (int iter = 0; iter < iteration; iter++)
{
Log($"--------------第{iter}轮求解开始--------------");
bool hasFeasibleSolution = true;
#region 求解LR问题
SubTasks.Clear();
foreach (CustomerArrival customer in _ctx.Pal)// 求解x
{
Task ta = factory.StartNew(() =>
{
var graph = new LRxTravelHyperNetwork(_ctx, adapter, objgraph, customer, pathDict, LM_rho, LM_mu, LM_lambda);
graph.Build();
var ori = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).OriSta;
var des = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).DesSta;
DijkstraShortestPaths <DirectedWeightedSparseGraph <TravelHyperNode>, TravelHyperNode> dijkstra
= new DijkstraShortestPaths <DirectedWeightedSparseGraph <TravelHyperNode>, TravelHyperNode>(graph, new TravelHyperNode()
{
Time = adapter.ConvertToDiscreteTime(customer.ArriveTime),
Station = ori,
Price = 0
}); //考虑该旅客到达时间
x[customer] = new TravelPath(graph, dijkstra.ShortestPathTo(
new TravelHyperNode()
{
Time = adapter.Horizon + 1440,
Station = des,
Price = 0
}));
});
SubTasks.Add(ta);
}
foreach (var train in _ctx.Wor.RailwayTimeTable.Trains)
{
foreach (IRailwayStation station in _ctx.Wor.Net.StationCollection)// 求解w
{
Task ta = factory.StartNew(() =>
{
var graph = DpnAlgorithm.BuildLRwGraph(_ctx, adapter, train, station, pathDict, basicGraph.LinkTrainDict, LM_mu, LM_lambda);
DijkstraShortestPaths <DirectedWeightedSparseGraph <string>, string> dijkstra
= new DijkstraShortestPaths <DirectedWeightedSparseGraph <string>, string>(graph, "Start");//考虑该旅客到达时间
var nodepath = dijkstra.ShortestPathTo("End");
if (nodepath == null)
{
throw new System.Exception("No path found");
}
else
{
w[train][station] = new PricePath(graph, nodepath);
}
});
SubTasks.Add(ta);
}
}
Task.WaitAll(SubTasks.ToArray());
foreach (var edge in y.Keys.ToArray())//更新y
{
var sta = edge.Source.Station;
var train = basicGraph.GetTrainByReservationLink(edge);
y[edge] = w[train][sta].GetWrapPoints(edge.Destination.Price, edge.Source.Time).Any();
}
#endregion
#region 计算拉格朗日函数值作为下界
decimal templowerBound = 0m;
decimal templowerBound_part1 = 0m;
decimal templowerBound_part2 = 0m;
decimal templowerBound_part3 = 0m;
decimal templowerBound_part4 = 0m;
Dictionary <CustomerArrival, decimal> lbValueDic
= new Dictionary <CustomerArrival, decimal>();
//1 计算在基础网络中的路径cost
foreach (CustomerArrival customer in _ctx.Pal)
{
lbValueDic.Add(customer, objgraph.GetPathCost(x[customer]));
templowerBound_part1 += lbValueDic[customer];
}
//2计算BRUE项
templowerBound_part2 += _ctx.Pal.Sum(c => pathDict[c].Sum(p =>
{
decimal secondItem = 0m;
secondItem += basicGraph.GetPathCost(x[c]) - basicGraph.GetPathCost(p) - _ctx.SitaDic[c.Customer.MarSegID];
secondItem -= (p.ReservationArc != null && y[p.ReservationArc]) ? 0 : bigM;
return(secondItem * LM_mu[c][p]);
}));
//3计算In-train Capacity项
Dictionary <IServiceSegment, int> ServiceDic = _ctx.Wor.RailwayTimeTable
.Trains.SelectMany(i => i.ServiceSegments)
.ToDictionary(i => i, i => 0);//当前service segment使用情况
foreach (var p in x.Values)
{
foreach (IServiceSegment seg in p.GetSegments(basicGraph))
{
ServiceDic[seg] += 1;
}
}
foreach (var train in _ctx.Wor.RailwayTimeTable.Trains)
{
foreach (var seg in train.ServiceSegments)
{
templowerBound_part3 += LM_rho[seg] * (ServiceDic[seg] - train.Carriage.Chairs.Count());
}
}
//4 计算reservation constraint 项
Dictionary <IEdge <TravelHyperNode>, int> reservationDic
= new Dictionary <IEdge <TravelHyperNode>, int>();
foreach (var p in x.Values)
{
if (reservationDic.ContainsKey(p.ReservationArc))
{
reservationDic[p.ReservationArc] += 1;
}
else
{
reservationDic.Add(p.ReservationArc, 1);
}
}
foreach (var pair in y.Keys)
{
//y是所有的reservation 的集合 reservationDic 是已经使用的reservation 集合
var res = reservationDic.Keys.FirstOrDefault(i => i.Source == pair.Source && i.Destination == pair.Destination);
templowerBound_part4 += LM_lambda[pair] * ((res != null ? reservationDic[res] : 0) - (y[pair] ? bigM : 0));
}
templowerBound = templowerBound_part1 + templowerBound_part2 + templowerBound_part3 + templowerBound_part4;
//Log($"Lower Bound = { Math.Round(templowerBound, 2)}," +
// $"({ Math.Round(templowerBound_part1, 2) }" +
// $"+{ Math.Round(templowerBound_part2, 2)}" +
// $"+{ Math.Round(templowerBound_part3, 2)}" +
// $"+{ Math.Round(templowerBound_part4, 2)})");
PrintLBSolution(DpnAlgorithm.GetTravelPathString(_ctx, adapter, objgraph, x, lbValueDic));
#endregion
#region 更新乘子
if (flag)
{
decimal LagLowerBound = Convert.ToDecimal(model.GetValue(theta));
Log($"LagLower Bound = { LagLowerBound }");
Log($"Lower Bound = { templowerBound }");
lowerBound = Math.Max(lowerBound, templowerBound);
decimal lagGap = LagLowerBound - templowerBound;
if (lagGap <= 0)//判断拉格朗日对偶问题是否达到最优
{
Log($"求解终止：对偶函数已最优。");
break;
}
else
{
/* 更新乘子值 */
foreach (var pair in dual_rho)
{
LM_rho[pair.Key] = Convert.ToDecimal(model.GetValue(pair.Value));
}
foreach (var pair in dual_lambda)
{
LM_lambda[pair.Key] = Convert.ToDecimal(model.GetValue(pair.Value));
}
foreach (CustomerArrival customer in _ctx.Pal)
{
foreach (var path in pathDict[customer])
{
LM_mu[customer][path] = Convert.ToDecimal(model.GetValue(dual_mu[customer][path]));
}
}
}
}
else /* 如果对偶问题无可行解，通过次梯度的方式更新乘子 */
{
//decimal step = 1.618m / (iter + 1);
//foreach (CustomerArrival c in _ctx.Pal)//更新mu
//{
// foreach (TravelPath p in pathDict[c])
// {
// Grad_mu[c][p] = basicGraph.GetPathCost(x[c]) - basicGraph.GetPathCost(p) - _ctx.SitaDic[c.Customer.MarSegID]
// - ((p.ReservationArc != null && y[p.ReservationArc]) ? 0 : bigM);
// LM_mu[c][p] = Math.Max(0, LM_mu[c][p] + step * Grad_mu[c][p]);
// }
//}
//foreach (var pair in y.Keys) //更新lambda
//{
// var res = reservationDic.Keys.FirstOrDefault(i => i.Source == pair.Source && i.Destination == pair.Destination);
// Grad_lambda[pair] = ((res != null ? reservationDic[res] : 0) - (y[pair] ? bigM : 0));
// LM_lambda[pair] = Math.Max(0, LM_lambda[pair] + step * Grad_lambda[pair]);
//}
//foreach (var train in _ctx.Wor.RailwayTimeTable.Trains)//更新rho
//{
// foreach (var seg in train.ServiceSegments)
// {
// Grad_rho[seg] = ServiceDic[seg] - train.Carriage.Chairs.Count();
// LM_rho[seg] = Math.Max(0, LM_rho[seg] + step * Grad_rho[seg]);
// }
//}
}
#endregion
#region 求解拉格朗日对偶问题
/* 求解 几何乘子 */
// 增加 一个约束
INumExpr exp = model.NumExpr();
//2 计算BRUE项
foreach (var c in _ctx.Pal)
{
foreach (var p in pathDict[c])
{
decimal secondItem = 0m;
secondItem += basicGraph.GetPathCost(x[c]) - basicGraph.GetPathCost(p) - _ctx.SitaDic[c.Customer.MarSegID];
secondItem -= (p.ReservationArc != null && y[p.ReservationArc]) ? 0 : bigM;
exp = model.Sum(exp, model.Prod(Convert.ToDouble(secondItem), dual_mu[c][p]));
}
}
//3计算In-train Capacity项 (这里直接用了计算下界时候的 Service_dic
foreach (var train in _ctx.Wor.RailwayTimeTable.Trains)
{
foreach (var seg in train.ServiceSegments)
{
exp = model.Sum(exp, model.Prod(Convert.ToDouble(ServiceDic[seg]
- train.Carriage.Chairs.Count()), dual_rho[seg]));
}
}
//4 计算reservation constraint 项 (这里直接用了计算下界时候的 reservationDic
foreach (var pair in y.Keys)
{
var res = reservationDic.Keys.FirstOrDefault(i => i.Source == pair.Source &&
i.Destination == pair.Destination);
exp = model.Sum(exp, model.Prod(Convert.ToDouble(((res != null ? reservationDic[res] : 0)
- (y[pair] ? bigM : 0))), dual_lambda[pair]));
}
model.AddGe(model.Sum(Convert.ToDouble(templowerBound_part1), exp), theta);
/* Trust-Region */
//var trExpr = model.Add()
flag = model.Solve();
Log($"Is Dual Problem Feasible: { flag }");
#endregion
#region 通过一个启发式规则计算上界(按照w模拟到达)
var pathcost = lbValueDic.ToDictionary(i => i.Key, i => i.Value);
var x_least = x.ToDictionary(i => i.Key, i => i.Value);//当前w下每个旅客的最短路径
var x_upperbound = x.ToDictionary(i => i.Key, i => i.Value);
var x_controlled = x.ToDictionary(i => i.Key, i => i.Value);
#region 1-构建当前y下的最优x值
SubTasks.Clear();
foreach (CustomerArrival customer in _ctx.Pal)// 求解x
{
Task ta = factory.StartNew(() =>
{
var controlledLRxgraph = new ControlledLRxTravelHyperNetwork(
_ctx, adapter, objgraph, customer, pathDict, LM_rho, LM_mu, LM_lambda, y);
controlledLRxgraph.Build();
var ori = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).OriSta;
var des = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).DesSta;
TravelHyperNode startNode = new TravelHyperNode()
{
Time = adapter.ConvertToDiscreteTime(customer.ArriveTime),
Station = ori,
Price = 0
};
TravelHyperNode endNode = new TravelHyperNode()
{
Time = adapter.Horizon + 1440,
Station = des,
Price = 0
};
DijkstraShortestPaths <DirectedWeightedSparseGraph <TravelHyperNode>, TravelHyperNode> dijkstra
= new DijkstraShortestPaths <DirectedWeightedSparseGraph <TravelHyperNode>, TravelHyperNode>
(controlledLRxgraph, startNode);//考虑该旅客到达时间
if (!dijkstra.HasPathTo(endNode))
{
throw new System.Exception("没有路径!");
}
else
{
x_controlled[customer] = new TravelPath(controlledLRxgraph, dijkstra.ShortestPathTo(endNode));
}
});
SubTasks.Add(ta);
}
Task.WaitAll(SubTasks.ToArray());
#endregion
# region 2-构建当前y下的出行最小值
var solutiongraph = new ControlledTravelHyperNetwork(_ctx, adapter, y);
solutiongraph.Build();
Parallel.ForEach(_ctx.Pal, customer => //foreach (var customer in _ctx.Pal)//求此网络下每个旅客的最短路径
{
var ori = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).OriSta;
var des = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).DesSta;
TravelHyperNode startNode = new TravelHyperNode()
{
Time = adapter.ConvertToDiscreteTime(customer.ArriveTime),
Station = ori,
Price = 0
};
TravelHyperNode endNode = new TravelHyperNode()
{
Time = adapter.Horizon + 1440,
Station = des,
Price = 0
};
DijkstraShortestPaths <DirectedWeightedSparseGraph <TravelHyperNode>, TravelHyperNode> dijkstra
= new DijkstraShortestPaths <DirectedWeightedSparseGraph <TravelHyperNode>, TravelHyperNode>
(solutiongraph, startNode);
if (!dijkstra.HasPathTo(endNode))
{
throw new System.Exception("没有路径!");
}
else
{
x_least[customer] = new TravelPath(solutiongraph, dijkstra.ShortestPathTo(endNode));
}
});
#endregion
#region 3-修复可行解
var solutiongraphTemp = new SimNetwork(_ctx, adapter, y);//建立仿真网络
solutiongraphTemp.Build();
foreach (var customer in _ctx.Pal)
{
x_upperbound[customer] = x_controlled[customer];
TravelPath path = x_controlled[customer];
if (!solutiongraphTemp.IsPathFeasible(path) ||
solutiongraphTemp.GetPathCost(path) > solutiongraph.GetPathCost(x_least[customer]) + _ctx.SitaDic[customer.Customer.MarSegID])//如果违反了容量约束或者BRUE约束
{
var ori = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).OriSta;
var des = (_ctx.Wor.Mar[customer.Customer.MarSegID] as IRailwayMarketSegment).DesSta;
DijkstraShortestPaths <DirectedWeightedSparseGraph <TravelHyperNode>, TravelHyperNode> dijkstra
= new DijkstraShortestPaths <DirectedWeightedSparseGraph <TravelHyperNode>, TravelHyperNode>(solutiongraphTemp, new TravelHyperNode()
{
Time = adapter.ConvertToDiscreteTime(customer.ArriveTime),
Station = ori,
Price = 0
});
//重新查找路径，如果存在路径
if (dijkstra.HasPathTo(new TravelHyperNode()
{
Time = adapter.Horizon + 1440,
Station = des,
Price = 0
}))
{
x_upperbound[customer] = new TravelPath(solutiongraphTemp, dijkstra.ShortestPathTo(new TravelHyperNode()
{
Time = adapter.Horizon + 1440,
Station = des,
Price = 0
}));
if (solutiongraphTemp.GetPathCost(x_upperbound[customer]) <= //满足BRUE约束
solutiongraph.GetPathCost(x_least[customer]) + _ctx.SitaDic[customer.Customer.MarSegID])
{
path = x_upperbound[customer];
}
else
{
hasFeasibleSolution = false;
break;
}
}
else
{
hasFeasibleSolution = false;
break;
}
}
pathcost[customer] = objgraph.GetPathCost(path);
//加载路径
foreach (var seg in path.GetSegments(basicGraph))
{
solutiongraphTemp.AddUsage(seg, 1);
}
}
var tempUpperbound = _ctx.Pal.Sum(c => objgraph.GetPathCost(x_upperbound[c]));
#endregion
//如果有最优解再更新上界
bool hasBetterUpperbound = tempUpperbound < upperBound;
if (hasFeasibleSolution)
{
upperBound = Math.Min(upperBound, tempUpperbound);
}
Log($"Upper Bound = { Math.Round(tempUpperbound, 2) },找到可行解 : { hasFeasibleSolution.ToString()}");
#endregion
#region Terminatation 判定
Log($"Upper Bound = { tempUpperbound }");
decimal absoluteGap = 0;
string gapStr = "";
//如果上限是无穷，那么此时gap也是无穷
if (upperBound == decimal.MaxValue || lowerBound == decimal.MinValue)
{
absoluteGap = decimal.MaxValue;
gapStr = $"+∞";
}
else
{
absoluteGap = upperBound - lowerBound;
gapStr = $"{ Math.Round(absoluteGap, 2)}";
}
if (absoluteGap < terminalFactor && absoluteGap > 0)
{
Log($"求解终止：Gap以满足终止条件，Gap={ absoluteGap }");
break;
}
Log($"Total Gap = { gapStr }");
#endregion
#region 输出信息
//SendMessage($"#Iteration Number, Lower Bound, Upper Bound, Best Lower Bound, Best Upper Bound, Total Gap(%) ");
PrintIterationInfo($"#{iter},{ Math.Round(templowerBound) },{ Math.Round(tempUpperbound) },{ Math.Round(lowerBound)}" +
$",{ Math.Round(upperBound) },{ gapStr }");
string ss = "###,";
foreach (var s in LM_rho)
{
ss += ($"{s.Key.ToString()}:{ s.Value.ToString() },");
}
PrintIterationInfo(ss);
if (hasFeasibleSolution && hasBetterUpperbound)
{
ObjValue = pathcost.Sum(i => i.Value);
PrintSolution(
DpnAlgorithm.GetTravelPathString(_ctx, adapter, solutiongraph, x_upperbound, pathcost),
DpnAlgorithm.GetPricingPathString(_ctx, adapter, w));
}
#endregion
Log($"--------------第{iter}轮求解结束--------------");
}
public static void Main(string[] args)
{
int nMonths = cost.Length;
int nProducts = cost[0].Length;
try {
Cplex cplex = new Cplex();
INumVar[] produce = cplex.NumVarArray(nMonths, 0, System.Double.MaxValue);
INumVar[][] use = new INumVar[nMonths][];
INumVar[][] buy = new INumVar[nMonths][];
INumVar[][] store = new INumVar[nMonths][];
for (int i = 0; i < nMonths; i++)
{
use[i] = cplex.NumVarArray(nProducts, 0.0, System.Double.MaxValue);
buy[i] = cplex.NumVarArray(nProducts, 0.0, System.Double.MaxValue);
store[i] = cplex.NumVarArray(nProducts, 0.0, 1000.0);
}
for (int p = 0; p < nProducts; p++)
{
store[nMonths - 1][p].LB = 500.0;
store[nMonths - 1][p].UB = 500.0;
}
INumExpr profit = cplex.NumExpr();
for (int i = 0; i < nMonths; i++)
{
// Not more than 200 tons of vegetable oil can be refined
cplex.AddLe(cplex.Sum(use[i][v1], use[i][v2]), 200.0);
// Not more than 250 tons of non-vegetable oil can be refined
cplex.AddLe(cplex.Sum(use[i][o1], use[i][o2], use[i][o3]), 250.0);
// Constraints on food composition
cplex.AddLe(cplex.Prod(3.0, produce[i]),
cplex.Sum(cplex.Prod(8.8, use[i][v1]),
cplex.Prod(6.1, use[i][v2]),
cplex.Prod(2.0, use[i][o1]),
cplex.Prod(4.2, use[i][o2]),
cplex.Prod(5.0, use[i][o3])));
cplex.AddGe(cplex.Prod(6.0, produce[i]),
cplex.Sum(cplex.Prod(8.8, use[i][v1]),
cplex.Prod(6.1, use[i][v2]),
cplex.Prod(2.0, use[i][o1]),
cplex.Prod(4.2, use[i][o2]),
cplex.Prod(5.0, use[i][o3])));
cplex.AddEq(produce[i], cplex.Sum(use[i]));
// Raw oil can be stored for later use
if (i == 0)
{
for (int p = 0; p < nProducts; p++)
{
cplex.AddEq(cplex.Sum(500.0, buy[i][p]),
cplex.Sum(use[i][p], store[i][p]));
}
}
else
{
for (int p = 0; p < nProducts; p++)
{
cplex.AddEq(cplex.Sum(store[i - 1][p], buy[i][p]),
cplex.Sum(use[i][p], store[i][p]));
}
}
// Logical constraints:
// The food cannot use more than 3 oils
// (or at least two oils must not be used)
cplex.AddGe(cplex.Sum(cplex.Eq(use[i][v1], 0),
cplex.Eq(use[i][v2], 0),
cplex.Eq(use[i][o1], 0),
cplex.Eq(use[i][o2], 0),
cplex.Eq(use[i][o3], 0)), 2);
// When an oil is used, the quantity must be at least 20 tons
for (int p = 0; p < nProducts; p++)
{
cplex.Add(cplex.Or(cplex.Eq(use[i][p], 0),
cplex.Ge(use[i][p], 20)));
}
// If products v1 or v2 are used, then product o3 is also used
cplex.Add(cplex.IfThen(cplex.Or(cplex.Ge(use[i][v1], 20),
cplex.Ge(use[i][v2], 20)),
cplex.Ge(use[i][o3], 20)));
// Objective function
profit = cplex.Sum(profit, cplex.Prod(150, produce[i]));
profit = cplex.Diff(profit, cplex.ScalProd(cost[i], buy[i]));
profit = cplex.Diff(profit, cplex.Prod(5, cplex.Sum(store[i])));
}
cplex.AddMaximize(profit);
if (cplex.Solve())
{
System.Console.WriteLine("Solution status = " + cplex.GetStatus());
System.Console.WriteLine(" Maximum profit = " + cplex.ObjValue);
for (int i = 0; i < nMonths; i++)
{
System.Console.WriteLine(" Month {0}", i);
System.Console.Write(" . buy ");
for (int p = 0; p < nProducts; p++)
{
System.Console.Write("{0,8:F2} ", cplex.GetValue(buy[i][p]));
}
System.Console.WriteLine();
System.Console.Write(" . use ");
for (int p = 0; p < nProducts; p++)
{
System.Console.Write("{0,8:F2} ", cplex.GetValue(use[i][p]));
}
System.Console.WriteLine();
System.Console.Write(" . store ");
for (int p = 0; p < nProducts; p++)
{
System.Console.Write("{0,8:F2} ", cplex.GetValue(store[i][p]));
}
System.Console.WriteLine();
}
}
cplex.End();
}
catch (ILOG.Concert.Exception e) {
System.Console.WriteLine("Concert exception caught: " + e);
}
}
static void Main(string[] args)
{
double[] price = { 170.0, 170.0, 170.0 };
double[] priceStorage = { 10.0, 10.0, 10.0 };
double[] hardness = { 8.4, 6.2, 2.0 };
double[] hardness3 = { 3, 3, 3 };
double[] hardness6 = { 6, 6, 6 };
double[][] oilCost = new double[2][];
oilCost[0] = new double[3] {
1.159550009798596e+02, 1.018115866059220e+02, 1.128780935294160e+02
};
oilCost[1] = new double[3] {
100, 1.067537671189185e+02, 1.098041326934309e+02
};
//oilCost = GenerateRandomVector();
INumVar[][] oilStore = new INumVar[3][];
INumVar[][] oilBuy = new INumVar[2][];
INumVar[][] oilProduce = new INumVar[2][]; // [month][A-C]
Cplex cplex = new Cplex();
for (int i = 0; i < 2; i++) // Initialize oilBuy and oilProduce
{
oilBuy[i] = new INumVar[3];
oilProduce[i] = new INumVar[3];
}
for (int i = 0; i < 3; i++) // Initialize oilStore
{
oilStore[i] = new INumVar[3];
for (int j = 0; j < 3; j++)
{
oilStore[i][j] = cplex.NumVar(0, 800); // Ograniczenia na pojemność magazynu
}
}
for (int i = 0; i < 2; i++) // Ograniczenia na miesiące
{
for (int j = 0; j < 3; j++) // Ograniczenia na możliwości rafinacji
{
if (j != 2)
{
oilProduce[i][j] = cplex.NumVar(0, 220); // Rafinacja roślinnego
}
else
{
oilProduce[i][j] = cplex.NumVar(0, 270); // Rafinacja nie-roślinnego
}
oilBuy[i][j] = cplex.NumVar(0, 1070);
}
cplex.AddRange(0, cplex.Sum(oilProduce[i][0], oilProduce[i][1]), 220);
cplex.AddGe(cplex.ScalProd(hardness, oilProduce[i]), cplex.ScalProd(hardness3, oilProduce[i])); // Hardness greater than 3
cplex.AddLe(cplex.ScalProd(hardness, oilProduce[i]), cplex.ScalProd(hardness6, oilProduce[i])); // Hardness less than 6
}
for (int i = 0; i < 3; i++) // Ograniczenia na oleje
{
cplex.AddEq(oilStore[0][i], 200.0); // Ograniczenie na stan magazynu w grudniu
cplex.AddEq(oilStore[2][i], 200.0); // Ograniczenie na stan magazynu w lutym
cplex.AddEq(oilStore[1][i], cplex.Sum(cplex.Diff(oilBuy[0][i], oilProduce[0][i]), oilStore[0][i])); // (Kupowane + zmagazynowane - produkowane) w tym miesiacu = zmagazynowane w nastepnym miesiacu
cplex.AddEq(oilStore[2][i], cplex.Sum(cplex.Diff(oilBuy[1][i], oilProduce[1][i]), oilStore[1][i]));
}
// Funkcja Celu: zyski ze sprzedaży - koszta magazynowania - koszta kupowania materiału do produkcji
cplex.AddMaximize(
cplex.Diff(
cplex.Diff(
cplex.Sum(
cplex.ScalProd(price, oilProduce[0]),
cplex.ScalProd(price, oilProduce[1])),
cplex.Sum(
cplex.ScalProd(priceStorage, oilStore[1]),
cplex.ScalProd(priceStorage, oilStore[2]))),
cplex.Sum(
cplex.ScalProd(oilCost[0], oilBuy[0]),
cplex.ScalProd(oilCost[1], oilBuy[1]))));
if (cplex.Solve())
{
System.Console.WriteLine();
System.Console.WriteLine("Solution status = " + cplex.GetStatus());
System.Console.WriteLine();
System.Console.WriteLine(" = " + cplex.ObjValue);
Console.WriteLine();
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < 3; j++)
{
Console.WriteLine(" oilCost[" + i + "][" + j + "] = " + oilCost[i][j]);
}
}
Console.WriteLine();
for (int j = 0; j < 2; j++)
{
double hardnessTotal = 0;
double sum = 0;
for (int i = 0; i < 3; i++)
{
System.Console.WriteLine(" oilProduce[" + j + "][" + i + "] = " + cplex.GetValue(oilProduce[j][i]));
hardnessTotal += cplex.GetValue(oilProduce[j][i]) * hardness[i];
sum += cplex.GetValue(oilProduce[j][i]);
}
System.Console.WriteLine(" hardnessTotal[" + j + "] = " + hardnessTotal / sum);
Console.WriteLine();
}
for (int j = 0; j < 2; j++)
{
for (int i = 0; i < 3; i++)
{
System.Console.WriteLine(" oilBuy[" + j + "][" + i + "] = " + cplex.GetValue(oilBuy[j][i]));
}
}
Console.WriteLine();
for (int j = 0; j < 3; j++)
{
for (int i = 0; i < 3; i++)
{
System.Console.WriteLine(" oilStore[" + j + "][" + i + "] = " + cplex.GetValue(oilStore[j][i]));
}
}
System.Console.WriteLine();
}
Console.ReadKey();
}
static void Main(string[] args)
{
try
{
graphFile = args[0];
int timeLimit = Convert.ToInt32(args[1]);
Timer myTimer = new Timer();
myTimer.Start();
myTimer.Interval = timeLimit * 1000;
startTime = DateTime.Now;
myTimer.Elapsed += MyTimer_Elapsed;
MyGraph graph = new MyGraph(graphFile, "DIMACS");
int maxTime = 100;
int targetCliqueSize = graph.NumberNodes;
//Эвристически найдем хотя бы что-то похожее на максимальную клику (в пределах 5% ошибки - чтобы вернуть, если не успеет обсчитаться основной обход)
maxClique = FindMaxClique(graph, maxTime, targetCliqueSize);
int ub = maxClique.Count;
Bound = ub;
List <List <int> > clique = new List <List <int> >();
//Сортируем вершины по числу соседей, будем вызывать алгоритм для тех вершин, у которых количество соседей наибольшее
Dictionary <int, int> nodeAndNeighbors = new Dictionary <int, int>();
for (int i = 0; i < graph.NumberNodes; ++i)
{
int numberNeighbors = graph.NumberNeighbors(i);
nodeAndNeighbors.Add(i, numberNeighbors);
}
//Сортируем вершины по уменьшению количества соседей
nodeAndNeighbors = nodeAndNeighbors.OrderByDescending(pair => pair.Value).ToDictionary(pair => pair.Key, pair => pair.Value);
List <int> colors = new List <int>()
{
1
};
//Раскраска графа
int top = (from v in nodeAndNeighbors.Keys.ToList() select v).ToList <int>()[0];
Dictionary <int, int> colorizedGraph = new Dictionary <int, int>();
//Раскрасим граф
colorizedGraph = colorize(nodeAndNeighbors.Keys.ToList <int>(), graph);
int cntr = 0;
//Зададим базовую модель
Cplex cplex = new Cplex();
IRange[][] rng = new IRange[1][];
INumVar[][] var = new INumVar[1][];
rng[0] = new IRange[graph.NumberNodes * graph.NumberNodes];
// add the objective function
double[] objvals = new double[graph.NumberNodes];
string[] varname = new string[graph.NumberNodes];
for (int i = 0; i < graph.NumberNodes; i++)
{
objvals[i] = 1.0;
varname[i] = "x" + (i + 1);
}
INumVar[] x = cplex.NumVarArray(graph.NumberNodes, 0.0, 1.0, varname);
var[0] = x;
//Ограничение, что х лежит от нуля до единицы задали при инициализации
cplex.AddMaximize(cplex.ScalProd(x, objvals));
//Получим номер максимального цвета = это количество цветов, в которые окрашен граф
//Будем иметь в виду, что количество цветов - это верхняя оценка на размер клики, а найденная эвристически клика на первом этапе - нижняя оценка.
int colorCount = colorizedGraph.Values.Max();
List <int> colorizedNodes = new List <int>();
int pointer = 1;
//Добавим ограничение, что вершины, входящие в один цветовой класс, не связаны между собой
for (int i = 1; i <= colorCount; ++i)
{
colorizedNodes = (from t in colorizedGraph where t.Value == i select t.Key).ToList <int>();
if (colorizedNodes.Count() != 1)
{
INumExpr[] constraint = new INumExpr[colorizedNodes.Count()];
int counter = 0;
colorizedNodes.ForEach(node =>
{
constraint[counter] = cplex.Prod(1.0, x[node]);
counter++;
});
rng[0][pointer] = cplex.AddLe(cplex.Sum(constraint), 1.0, "c" + (pointer));
pointer++;
}
}
for (int i = 0; i < graph.NumberNodes; i++)
{
for (int j = i + 1; j < graph.NumberNodes; j++)
{
if (!graph.AreAdjacent(i, j))
{
rng[0][pointer] = cplex.AddLe(cplex.Sum(cplex.Prod(1.0, x[i]), cplex.Prod(1.0, x[j])), 1.0, "c" + (pointer));
pointer++;
}
}
}
//------------------------------------------------------------------------
//-----Пробуем решать задачу ровно до тех пор, пока не получим клику------
//-----Помним про ограничения на размер клики-----------------------------
int countOfConstraint = colorCount;
globalClick = maxClique;
Branching(cplex, x);
cplex.End();
////Максимальная клика, которую можно найти для вершины - это количество различных цветов, в которые окрашены все ее соседи плюс она сама
//foreach (KeyValuePair<int,int> pair in nodeAndNeighbors)
//{
// List<int> neighbors = graph.GetNeighbors(pair.Key);
// neighbors.Add(pair.Key);
// var cols = (from n in colorizedGraph where neighbors.Exists(t => t == n.Key) select n.Value).Distinct().ToList<int>();
// if (cols.Count() >= Bound && cols.Count() >= globalClick.Count())
// {
// clique.Add(new List<int>());
// List<int> cur = new List<int>();
// RecursiveSearch(pair.Key, ref cur, ref neighbors, graph);
// clique[cntr] = cur;
// cntr++;
// }
//}
TimeSpan time = (DateTime.Now - startTime);
Console.WriteLine("Time to find " + time);
Console.WriteLine(globalClick.Count());
Console.WriteLine(String.Join(" ", globalClick));
WriteResults(time, globalClick, false);
}
catch (System.Exception ex)
{
Console.WriteLine("Fatal: " + ex.Message);
Console.WriteLine(ex.StackTrace);
Console.ReadKey();
}
}
