Workforce3.java
/* Copyright 2021, 狗万app足彩Gurobi Optimization, LLC */ /*分配工人轮班;每个工人可能在某一天有空，也可能不在。如果问题无法解决，则放松模型以确定哪些约束不能满足，以及需要放松多少约束。* /进口gurobi。*;String shift [] = new String[] {"Mon1"， "Tue2"， "Wed3"， "Thu4"， "Fri5"， "Sat6"， "Sun7"， "Mon8"， "Tue9"， "Wed10"， "Thu11"， "Fri12"， "Sat13"， "Sun14"};字符串工人新String[][] ={“艾米”、“Bob”、“凯西”、“丹”,“Ed”,“弗雷德”,“顾”};int nshift = shift .length;int nWorkers = Workers.length;//每一班双班所需的工人数double pay[] = new double[] {10, 12, 10, 8, 8, 9, 11}; // Worker availability: 0 if the worker is unavailable for a shift double availability[][] = new double[][] { { 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1 }, { 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0 }, { 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1 }, { 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1 }, { 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1 }, { 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1 }, { 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 } }; // Model GRBEnv env = new GRBEnv(); GRBModel model = new GRBModel(env); model.set(GRB.StringAttr.ModelName, "assignment"); // Assignment variables: x[w][s] == 1 if worker w is assigned // to shift s. Since an assignment model always produces integer // solutions, we use continuous variables and solve as an LP. GRBVar[][] x = new GRBVar[nWorkers][nShifts]; for (int w = 0; w < nWorkers; ++w) { for (int s = 0; s < nShifts; ++s) { x[w][s] = model.addVar(0, availability[w][s], pay[w], GRB.CONTINUOUS, Workers[w] + "." + Shifts[s]); } } // The objective is to minimize the total pay costs model.set(GRB.IntAttr.ModelSense, GRB.MINIMIZE); // Constraint: assign exactly shiftRequirements[s] workers // to each shift s for (int s = 0; s < nShifts; ++s) { GRBLinExpr lhs = new GRBLinExpr(); for (int w = 0; w < nWorkers; ++w) { lhs.addTerm(1.0, x[w][s]); } model.addConstr(lhs, GRB.EQUAL, shiftRequirements[s], Shifts[s]); } // Optimize model.optimize(); int status = model.get(GRB.IntAttr.Status); if (status == GRB.UNBOUNDED) { System.out.println("The model cannot be solved " + "because it is unbounded"); return; } if (status == GRB.OPTIMAL) { System.out.println("The optimal objective is " + model.get(GRB.DoubleAttr.ObjVal)); return; } if (status != GRB.INF_OR_UNBD && status != GRB.INFEASIBLE ) { System.out.println("Optimization was stopped with status " + status); return; } // Relax the constraints to make the model feasible System.out.println("The model is infeasible; relaxing the constraints"); int orignumvars = model.get(GRB.IntAttr.NumVars); model.feasRelax(0, false, false, true); model.optimize(); status = model.get(GRB.IntAttr.Status); if (status == GRB.INF_OR_UNBD || status == GRB.INFEASIBLE || status == GRB.UNBOUNDED ) { System.out.println("The relaxed model cannot be solved " + "because it is infeasible or unbounded"); return; } if (status != GRB.OPTIMAL) { System.out.println("Optimization was stopped with status " + status); return; } System.out.println("\nSlack values:"); GRBVar[] vars = model.getVars(); for (int i = orignumvars; i < model.get(GRB.IntAttr.NumVars); ++i) { GRBVar sv = vars[i]; if (sv.get(GRB.DoubleAttr.X) > 1e-6) { System.out.println(sv.get(GRB.StringAttr.VarName) + " = " + sv.get(GRB.DoubleAttr.X)); } } // Dispose of model and environment model.dispose(); env.dispose(); } catch (GRBException e) { System.out.println("Error code: " + e.getErrorCode() + ". " + e.getMessage()); } } }
