混合整数规划 - 在资源分配问题中编写约束的问题
Mixed Integer Programming - Problem of Writing Constraints in A Resource Allocation Problem
订单数量较多，需要发货。对于每个订单，可能有 1 到 3 个路线选项。这里的问题是找出这些路线中订单的最佳分配（组合）。
假设：
假设每种卡车的满负荷和每公里运输成本为：
7.6(size) -> 5300(weight), 4.4(cost per km);
9.6(size) -> 7100(weight), 4.81(cost per km);
15(size) -> 12000(weight), 6.34(cost per km);
假设所有路线到达目的地的中转次数为3次，则：
transit cost of each route = transit times*order quantity allocated to this route*4.5
假设这条路线的运输成本是基于使用的卡车数量和行驶距离，那么：
transport cost of each route = number of trucks allocated to this route*route distance
// 变量
x[j] -> 0-1 variables, if route x1,x2,x3....xj is used.
a[i][j] -> is the weight proportion of order i allocated to route j
t[j][s] -> truck number of certain size needed for each routes
Objective 函数:
Min (total tranpsort cost + total transit cost) -> MinΣ(x[j]*t[j][s]*Distance[j]*Cost[j] + x[j]*a[i][j]*OrderQuantity[i]*TransitTimes
受制于：
1. if a[i][j] is the proportion of weight allocation among the feasible routes of an order, then:
a1+a2+a3 = 1
2. orders combined together must choose the same route
3. orders combined together must be the same route type
4. all orders must be allocated to one or more trucks and shipped
5. the chosen of the truck size must be within the truck options for each route
6. total weight loaded on the truck must not exceed the total capacity of the truck
这是输入日期的样子：
这是输入数据的 CSV 格式：
OrderID,Order_Category,Quantity,Weight,Route1,Route1_Distance,Route1_Type,Route1_TruckOption,Route2,Route2_Distance,Route2_Type,Route2_TruckOption,Route3,Route3_Distance,Route3_Type,Route3_TruckOption
1,B,2000,4000,010W,2000,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
2,B,4000,8000,010W,2000,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
3,B,1000,2000,010W,2000,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
4,B,3500,7000,010WE,1750,1,13.5-15,022X,2200,1,13.5-15,,,,
5,B,2500,5000,020WB,2100,1,13.5-15,022X,2200,1,13.5-15,,,,
6,B,2500,5000,311W,1500,1,13.5-15,022X,2200,1,13.5-15,,,,
7,B,1000,2000,755WF,3500,2,7.6-9.6,755WE,3300,2,7.6-9.6,,,,
8,C,1000,2000,471W,3200,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
9,C,1500,3000,471W,3200,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
10,C,2000,4000,769WX,2750,2,7.6-9.6,663WA,2600,2,7.6-9.6,,,,
11,C,1000,2000,769WX,2750,2,7.6-9.6,754W,2900,2,7.6-9.6,663WA,2600,2,7.6-9.6
12,C,2000,4000,769WX,2750,2,7.6-9.6,755WE,3100,2,7.6-9.6,,,,
优化结果可能如下所示：
我的问题：
我不确定如何编写约束 2、3、5、6。
特别是，我不确定如何
1). model the relationship between weight allocated and the associated truck quantity and selection of suitable size;
2) make sure orders combined using the same route and same route type
使用 Google OR 工具或 Gurobi python 的 MIP 代码示例会有所帮助。
由于没有人提供建议的解决方案，我自己制定了一个解决方案。
public static void TruckAllocation() {
Loader.loadNativeLibraries();
// Data
// Dict<orderID, feasible paths)
Map<Integer, List<String>> myPath = new HashMap<Integer, List<String>>();
List<String> order1 = Arrays.asList("010W", "010WE", "022X");
List<String> order2 = Arrays.asList("010W", "010WE", "022X");
List<String> order3 = Arrays.asList("010W", "010WE", "022X");
List<String> order4 = Arrays.asList("010WE", "022X");
List<String> order5 = Arrays.asList("020WB", "022X");
List<String> order6 = Arrays.asList("331W", "022X");
List<String> order7 = Arrays.asList("755WF", "755WE");
List<String> order8 = Arrays.asList("010WE", "022X");
List<String> order9 = Arrays.asList("010WE", "022X");
List<String> order10 = Arrays.asList("663WA");
List<String> order11 = Arrays.asList("754W", "663WA");
List<String> order12 = Arrays.asList("755WE");
myPath.put(0, order1);
myPath.put(1, order2);
myPath.put(2, order3);
myPath.put(3, order4);
myPath.put(4, order5);
myPath.put(5, order6);
myPath.put(6, order7);
myPath.put(7, order8);
myPath.put(8, order9);
myPath.put(9, order10);
myPath.put(10, order11);
myPath.put(11, order12);
// Dict<pathname, truck options)
Map<String, List<Double>> truckOption = new HashMap<String, List<Double>>();
// List<Double> _010W = Arrays.asList(8500.0, 12000.0);
// List<Double> _010WE = Arrays.asList(8500.0, 12000.0);
// List<Double> _020WB = Arrays.asList(8500.0, 12000.0);
// List<Double> _311W = Arrays.asList(8500.0, 12000.0);
// List<Double> _755WF = Arrays.asList(5300.0, 7100.0);
// List<Double> _471W = Arrays.asList(8500.0, 12000.0);
// List<Double> _769WX = Arrays.asList(5300.0, 7100.0);
// List<Double> _022X = Arrays.asList(8500.0, 12000.0);
// List<Double> _755WE = Arrays.asList(5300.0, 7100.0);
// List<Double> _663WA = Arrays.asList(5300.0, 7100.0);
// List<Double> _754W = Arrays.asList(5300.0, 7100.0);
List<Double> _010W = Arrays.asList(13.5, 15.0);
List<Double> _010WE = Arrays.asList(13.5, 15.);
List<Double> _020WB = Arrays.asList(13.5, 15.);
List<Double> _311W = Arrays.asList(13.5, 15.);
List<Double> _755WF = Arrays.asList(7.6, 9.6);
List<Double> _471W = Arrays.asList(13.5, 15.);
List<Double> _769WX = Arrays.asList(7.6, 9.6);
List<Double> _022X = Arrays.asList(13.5, 15.);
List<Double> _755WE = Arrays.asList(7.6, 9.6);
List<Double> _663WA = Arrays.asList(7.6, 9.6);
List<Double> _754W = Arrays.asList(7.6, 9.6);
truckOption.put("010W", _010W);
truckOption.put("010WE", _010WE);
truckOption.put("020WB", _020WB);
truckOption.put("311W", _311W);
truckOption.put("755WF", _755WF);
truckOption.put("471W", _471W);
truckOption.put("769WX", _769WX);
truckOption.put("022X", _022X);
truckOption.put("755WE", _755WE);
truckOption.put("663WA", _663WA);
truckOption.put("754W", _754W);
// Dict<pathname, distance)
Map<String, Integer> dist_matrix = new HashMap<String, Integer>();
dist_matrix.put("010W", 2177);
dist_matrix.put("010WE", 2177);
dist_matrix.put("020WB", 2177);
dist_matrix.put("311W", 1749);
dist_matrix.put("755WF", 77);
dist_matrix.put("471W", 2450);
dist_matrix.put("769WX", 3);
dist_matrix.put("022X", 2125);
dist_matrix.put("755WE", 77);
dist_matrix.put("663WA", 33);
dist_matrix.put("754W", 372);
List<String> pathName = new ArrayList();
List<Double> truckOpt = new ArrayList();
List<String> routes = Arrays.asList(
"010W",
"010WE",
"020WB",
"311W",
"755WF",
"471W",
"769WX",
"022X",
"755WE",
"663WA",
"754W");
List<Double> wgt = Arrays.asList(
4000.0,
8000.0,
2000.0,
7000.0,
5000.0,
5000.0,
2000.0,
2000.0,
3000.0,
4000.0,
2000.0,
4000.0
);
List<Double> qty = Arrays.asList(
2000.0,
4000.0,
1000.0,
3500.0,
2500.0,
2500.0,
1000.0,
1000.0,
1500.0,
2000.0,
1500.0,
2000.0
);
for (String s: routes) {
List<String> temp = Collections.nCopies(10, s);
pathName.addAll(temp);
for (Double i: truckOption.get(s)) {
List<Double> cap = Collections.nCopies(5, i);
truckOpt.addAll(cap);
}
}
int numOrder = 12;
int numTruck = truckOpt.size();
System.out.println("Route size: " + routes.size());
System.out.println("truckOpt size: " + numTruck);
// Solver
// Create the linear solver with the SCIP backend.
MPSolver solver = MPSolver.createSolver("SCIP");
// Variables
// x[i][j] is an array of 0-1 variables, which will be the proportion
// of order i is assigned to path j's truck n.
MPVariable[][] x = new MPVariable[numOrder][numTruck];
for (int i = 0; i < numOrder; i++) {
String name = "wgt: " + wgt.get(i) + " feasible route: " + String.valueOf(myPath.get(i));
for (int j = 0; j < numTruck; j++) {
x[i][j] = solver.makeNumVar(0, 1, name);
// x[i][j] = solver.makeNumVar(0, 1, "");
System.out.println("x_" + i +"-"+ j);
}
}
// y[j][n] is an array of 0-1 variables, which indicates if truck j is opened.
MPVariable[] y = new MPVariable[numTruck];
for (int j = 0; j < numTruck; j++) {
// System.out.println("uy[j]: " + j);
// String name = String.valueOf(truckOpt.get(j));
String name = pathName.get(j) + "-" + String.valueOf(truckOpt.get(j)) + "-"
+ String.valueOf(GetTruckCost(truckOpt.get(j)));
y[j] = solver.makeIntVar(0, 1, name);
System.out.println("y[j]: " + y[j].name());
// System.out.println("dy[j]: " + j);
}
System.out.println("pathName: " + pathName);
System.out.println("truckOption: " + truckOpt);
// Constraints
// Each order is assigned to at least one or all paths' trucks.
for (int i = 0; i < numOrder; ++i) {
// MPConstraint constraint = solver.makeConstraint(1, numTruck, "");
MPConstraint constraint = solver.makeConstraint(1, 1, "");
for (int j = 0; j < numTruck; ++j) {
List<String> my_path = new ArrayList<String>();
my_path.addAll(myPath.get(i));
// System.out.println("my path: " + my_path);
if (my_path.contains(GetProperty(y[j].name(),0))) {
constraint.setCoefficient(x[i][j], 1);
// System.out.println("true");
}
else {
constraint.setCoefficient(x[i][j], 0);
// System.out.println("false");
}
}
}
// all orders loaded cannot exceed the loaded truck's full capacity.
double infinity = java.lang.Double.POSITIVE_INFINITY;
for (int j = 0; j < numTruck; ++j) {
MPConstraint constraint = solver.makeConstraint(-infinity, 0, "");
for (int i = 0; i < numOrder; ++i) {
constraint.setCoefficient(x[i][j], wgt.get(i));
constraint.setCoefficient(y[j], -GetFullCap(Double.parseDouble(GetProperty(y[j].name(),1))));
// System.out.println("wij_wgiht: "+wgt.get(i)+ " truck capcaity: "
// + GetFullCap(Double.parseDouble(GetProperty(y[j].name(),1))));
}
}
// all trucks can be 0 (not open) or numTruck (all open).
for (int j = 0; j < numTruck; ++j) {
MPConstraint constraint = solver.makeConstraint(0, numTruck, "");
constraint.setCoefficient(y[j], 1);
}
// Objective
MPObjective objective = solver.objective();
for (int j = 0; j < numTruck; ++j) {
double cof = Double.parseDouble(GetProperty(y[j].name(),2))*dist_matrix.get(GetProperty(y[j].name(),0));
objective.setCoefficient(y[j], cof);
}
objective.setMinimization();
// Solve
MPSolver.ResultStatus resultStatus = solver.solve();
// Print solution.
// Check that the problem has a feasible solution.
if (resultStatus == MPSolver.ResultStatus.OPTIMAL
|| resultStatus == MPSolver.ResultStatus.FEASIBLE) {
System.out.println("Total cost: " + objective.value() + "\n");
for (int i = 0; i < numOrder; ++i) {
for (int j = 0; j < numTruck; ++j) {
// Test if x[i][j] is 0 or 1 (with tolerance for floating point
// arithmetic).
if (x[i][j].solutionValue() > 0.0000001) {
System.out.println(
"Order " + i + " -> weight: " + x[i][j].name()
);
System.out.println(
"Order " + i + " assigned to truck " + j + "-> " + y[j].name() + ". weight = " + Double.valueOf(String.format("%.2f", x[i][j].solutionValue()*wgt.get(i))));
System.out.println(
"Order " + i + " assigned to truck " + j + ". % = " + Double.valueOf(String.format("%.2f", x[i][j].solutionValue())));
System.out.println("---------------");
System.out.println("");
}
}
}
// for (int j = 0; j < numTruck; ++j) {
// System.out.println(
// "truck " + j + ". isopen? " + y[j].solutionValue());
// }
}
else {
System.err.println("No solution found.");
}
System.out.println("");
System.out.println("");
System.out.println("");
}
订单数量较多，需要发货。对于每个订单，可能有 1 到 3 个路线选项。这里的问题是找出这些路线中订单的最佳分配（组合）。
假设：
假设每种卡车的满负荷和每公里运输成本为：
7.6(size) -> 5300(weight), 4.4(cost per km);
9.6(size) -> 7100(weight), 4.81(cost per km);
15(size) -> 12000(weight), 6.34(cost per km);
假设所有路线到达目的地的中转次数为3次，则：
transit cost of each route = transit times*order quantity allocated to this route*4.5
假设这条路线的运输成本是基于使用的卡车数量和行驶距离，那么：
transport cost of each route = number of trucks allocated to this route*route distance
// 变量
x[j] -> 0-1 variables, if route x1,x2,x3....xj is used.
a[i][j] -> is the weight proportion of order i allocated to route j
t[j][s] -> truck number of certain size needed for each routes
Objective 函数:
Min (total tranpsort cost + total transit cost) -> MinΣ(x[j]*t[j][s]*Distance[j]*Cost[j] + x[j]*a[i][j]*OrderQuantity[i]*TransitTimes
受制于：
1. if a[i][j] is the proportion of weight allocation among the feasible routes of an order, then:
a1+a2+a3 = 1
2. orders combined together must choose the same route
3. orders combined together must be the same route type
4. all orders must be allocated to one or more trucks and shipped
5. the chosen of the truck size must be within the truck options for each route
6. total weight loaded on the truck must not exceed the total capacity of the truck
这是输入日期的样子：
这是输入数据的 CSV 格式：
OrderID,Order_Category,Quantity,Weight,Route1,Route1_Distance,Route1_Type,Route1_TruckOption,Route2,Route2_Distance,Route2_Type,Route2_TruckOption,Route3,Route3_Distance,Route3_Type,Route3_TruckOption
1,B,2000,4000,010W,2000,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
2,B,4000,8000,010W,2000,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
3,B,1000,2000,010W,2000,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
4,B,3500,7000,010WE,1750,1,13.5-15,022X,2200,1,13.5-15,,,,
5,B,2500,5000,020WB,2100,1,13.5-15,022X,2200,1,13.5-15,,,,
6,B,2500,5000,311W,1500,1,13.5-15,022X,2200,1,13.5-15,,,,
7,B,1000,2000,755WF,3500,2,7.6-9.6,755WE,3300,2,7.6-9.6,,,,
8,C,1000,2000,471W,3200,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
9,C,1500,3000,471W,3200,1,13.5-15,010WE,1750,1,13.5-15,022X,2200,1,13.5-15
10,C,2000,4000,769WX,2750,2,7.6-9.6,663WA,2600,2,7.6-9.6,,,,
11,C,1000,2000,769WX,2750,2,7.6-9.6,754W,2900,2,7.6-9.6,663WA,2600,2,7.6-9.6
12,C,2000,4000,769WX,2750,2,7.6-9.6,755WE,3100,2,7.6-9.6,,,,
优化结果可能如下所示：
我的问题： 我不确定如何编写约束 2、3、5、6。 特别是，我不确定如何
1). model the relationship between weight allocated and the associated truck quantity and selection of suitable size;
2) make sure orders combined using the same route and same route type
使用 Google OR 工具或 Gurobi python 的 MIP 代码示例会有所帮助。
由于没有人提供建议的解决方案，我自己制定了一个解决方案。
public static void TruckAllocation() {
Loader.loadNativeLibraries();
// Data
// Dict<orderID, feasible paths)
Map<Integer, List<String>> myPath = new HashMap<Integer, List<String>>();
List<String> order1 = Arrays.asList("010W", "010WE", "022X");
List<String> order2 = Arrays.asList("010W", "010WE", "022X");
List<String> order3 = Arrays.asList("010W", "010WE", "022X");
List<String> order4 = Arrays.asList("010WE", "022X");
List<String> order5 = Arrays.asList("020WB", "022X");
List<String> order6 = Arrays.asList("331W", "022X");
List<String> order7 = Arrays.asList("755WF", "755WE");
List<String> order8 = Arrays.asList("010WE", "022X");
List<String> order9 = Arrays.asList("010WE", "022X");
List<String> order10 = Arrays.asList("663WA");
List<String> order11 = Arrays.asList("754W", "663WA");
List<String> order12 = Arrays.asList("755WE");
myPath.put(0, order1);
myPath.put(1, order2);
myPath.put(2, order3);
myPath.put(3, order4);
myPath.put(4, order5);
myPath.put(5, order6);
myPath.put(6, order7);
myPath.put(7, order8);
myPath.put(8, order9);
myPath.put(9, order10);
myPath.put(10, order11);
myPath.put(11, order12);
// Dict<pathname, truck options)
Map<String, List<Double>> truckOption = new HashMap<String, List<Double>>();
// List<Double> _010W = Arrays.asList(8500.0, 12000.0);
// List<Double> _010WE = Arrays.asList(8500.0, 12000.0);
// List<Double> _020WB = Arrays.asList(8500.0, 12000.0);
// List<Double> _311W = Arrays.asList(8500.0, 12000.0);
// List<Double> _755WF = Arrays.asList(5300.0, 7100.0);
// List<Double> _471W = Arrays.asList(8500.0, 12000.0);
// List<Double> _769WX = Arrays.asList(5300.0, 7100.0);
// List<Double> _022X = Arrays.asList(8500.0, 12000.0);
// List<Double> _755WE = Arrays.asList(5300.0, 7100.0);
// List<Double> _663WA = Arrays.asList(5300.0, 7100.0);
// List<Double> _754W = Arrays.asList(5300.0, 7100.0);
List<Double> _010W = Arrays.asList(13.5, 15.0);
List<Double> _010WE = Arrays.asList(13.5, 15.);
List<Double> _020WB = Arrays.asList(13.5, 15.);
List<Double> _311W = Arrays.asList(13.5, 15.);
List<Double> _755WF = Arrays.asList(7.6, 9.6);
List<Double> _471W = Arrays.asList(13.5, 15.);
List<Double> _769WX = Arrays.asList(7.6, 9.6);
List<Double> _022X = Arrays.asList(13.5, 15.);
List<Double> _755WE = Arrays.asList(7.6, 9.6);
List<Double> _663WA = Arrays.asList(7.6, 9.6);
List<Double> _754W = Arrays.asList(7.6, 9.6);
truckOption.put("010W", _010W);
truckOption.put("010WE", _010WE);
truckOption.put("020WB", _020WB);
truckOption.put("311W", _311W);
truckOption.put("755WF", _755WF);
truckOption.put("471W", _471W);
truckOption.put("769WX", _769WX);
truckOption.put("022X", _022X);
truckOption.put("755WE", _755WE);
truckOption.put("663WA", _663WA);
truckOption.put("754W", _754W);
// Dict<pathname, distance)
Map<String, Integer> dist_matrix = new HashMap<String, Integer>();
dist_matrix.put("010W", 2177);
dist_matrix.put("010WE", 2177);
dist_matrix.put("020WB", 2177);
dist_matrix.put("311W", 1749);
dist_matrix.put("755WF", 77);
dist_matrix.put("471W", 2450);
dist_matrix.put("769WX", 3);
dist_matrix.put("022X", 2125);
dist_matrix.put("755WE", 77);
dist_matrix.put("663WA", 33);
dist_matrix.put("754W", 372);
List<String> pathName = new ArrayList();
List<Double> truckOpt = new ArrayList();
List<String> routes = Arrays.asList(
"010W",
"010WE",
"020WB",
"311W",
"755WF",
"471W",
"769WX",
"022X",
"755WE",
"663WA",
"754W");
List<Double> wgt = Arrays.asList(
4000.0,
8000.0,
2000.0,
7000.0,
5000.0,
5000.0,
2000.0,
2000.0,
3000.0,
4000.0,
2000.0,
4000.0
);
List<Double> qty = Arrays.asList(
2000.0,
4000.0,
1000.0,
3500.0,
2500.0,
2500.0,
1000.0,
1000.0,
1500.0,
2000.0,
1500.0,
2000.0
);
for (String s: routes) {
List<String> temp = Collections.nCopies(10, s);
pathName.addAll(temp);
for (Double i: truckOption.get(s)) {
List<Double> cap = Collections.nCopies(5, i);
truckOpt.addAll(cap);
}
}
int numOrder = 12;
int numTruck = truckOpt.size();
System.out.println("Route size: " + routes.size());
System.out.println("truckOpt size: " + numTruck);
// Solver
// Create the linear solver with the SCIP backend.
MPSolver solver = MPSolver.createSolver("SCIP");
// Variables
// x[i][j] is an array of 0-1 variables, which will be the proportion
// of order i is assigned to path j's truck n.
MPVariable[][] x = new MPVariable[numOrder][numTruck];
for (int i = 0; i < numOrder; i++) {
String name = "wgt: " + wgt.get(i) + " feasible route: " + String.valueOf(myPath.get(i));
for (int j = 0; j < numTruck; j++) {
x[i][j] = solver.makeNumVar(0, 1, name);
// x[i][j] = solver.makeNumVar(0, 1, "");
System.out.println("x_" + i +"-"+ j);
}
}
// y[j][n] is an array of 0-1 variables, which indicates if truck j is opened.
MPVariable[] y = new MPVariable[numTruck];
for (int j = 0; j < numTruck; j++) {
// System.out.println("uy[j]: " + j);
// String name = String.valueOf(truckOpt.get(j));
String name = pathName.get(j) + "-" + String.valueOf(truckOpt.get(j)) + "-"
+ String.valueOf(GetTruckCost(truckOpt.get(j)));
y[j] = solver.makeIntVar(0, 1, name);
System.out.println("y[j]: " + y[j].name());
// System.out.println("dy[j]: " + j);
}
System.out.println("pathName: " + pathName);
System.out.println("truckOption: " + truckOpt);
// Constraints
// Each order is assigned to at least one or all paths' trucks.
for (int i = 0; i < numOrder; ++i) {
// MPConstraint constraint = solver.makeConstraint(1, numTruck, "");
MPConstraint constraint = solver.makeConstraint(1, 1, "");
for (int j = 0; j < numTruck; ++j) {
List<String> my_path = new ArrayList<String>();
my_path.addAll(myPath.get(i));
// System.out.println("my path: " + my_path);
if (my_path.contains(GetProperty(y[j].name(),0))) {
constraint.setCoefficient(x[i][j], 1);
// System.out.println("true");
}
else {
constraint.setCoefficient(x[i][j], 0);
// System.out.println("false");
}
}
}
// all orders loaded cannot exceed the loaded truck's full capacity.
double infinity = java.lang.Double.POSITIVE_INFINITY;
for (int j = 0; j < numTruck; ++j) {
MPConstraint constraint = solver.makeConstraint(-infinity, 0, "");
for (int i = 0; i < numOrder; ++i) {
constraint.setCoefficient(x[i][j], wgt.get(i));
constraint.setCoefficient(y[j], -GetFullCap(Double.parseDouble(GetProperty(y[j].name(),1))));
// System.out.println("wij_wgiht: "+wgt.get(i)+ " truck capcaity: "
// + GetFullCap(Double.parseDouble(GetProperty(y[j].name(),1))));
}
}
// all trucks can be 0 (not open) or numTruck (all open).
for (int j = 0; j < numTruck; ++j) {
MPConstraint constraint = solver.makeConstraint(0, numTruck, "");
constraint.setCoefficient(y[j], 1);
}
// Objective
MPObjective objective = solver.objective();
for (int j = 0; j < numTruck; ++j) {
double cof = Double.parseDouble(GetProperty(y[j].name(),2))*dist_matrix.get(GetProperty(y[j].name(),0));
objective.setCoefficient(y[j], cof);
}
objective.setMinimization();
// Solve
MPSolver.ResultStatus resultStatus = solver.solve();
// Print solution.
// Check that the problem has a feasible solution.
if (resultStatus == MPSolver.ResultStatus.OPTIMAL
|| resultStatus == MPSolver.ResultStatus.FEASIBLE) {
System.out.println("Total cost: " + objective.value() + "\n");
for (int i = 0; i < numOrder; ++i) {
for (int j = 0; j < numTruck; ++j) {
// Test if x[i][j] is 0 or 1 (with tolerance for floating point
// arithmetic).
if (x[i][j].solutionValue() > 0.0000001) {
System.out.println(
"Order " + i + " -> weight: " + x[i][j].name()
);
System.out.println(
"Order " + i + " assigned to truck " + j + "-> " + y[j].name() + ". weight = " + Double.valueOf(String.format("%.2f", x[i][j].solutionValue()*wgt.get(i))));
System.out.println(
"Order " + i + " assigned to truck " + j + ". % = " + Double.valueOf(String.format("%.2f", x[i][j].solutionValue())));
System.out.println("---------------");
System.out.println("");
}
}
}
// for (int j = 0; j < numTruck; ++j) {
// System.out.println(
// "truck " + j + ". isopen? " + y[j].solutionValue());
// }
}
else {
System.err.println("No solution found.");
}
System.out.println("");
System.out.println("");
System.out.println("");
}