Sensitivity.java
//版权所有2023,Gurobi O狗万app足彩ptimization, LLC //一个简单的灵敏度分析示例，从//文件读取MIP模型并求解它。然后利用scenario feature分析每个二元变量的目标函数设置为// 1-X时对// w.r.t.的影响，其中X为其在最优解中的值。// //用法:// java灵敏度 import gurobi.*;public类Sensitivity{//考虑的最大场景个数private static final int maxscenario = 100;public static void main(String[] args) {if (args。长度< 1){System.out。println(“使用:java灵敏度文件名”);system . exit (1);} try{//创建环境GRBEnv env = new GRBEnv();//读取GRBModel模型= new GRBModel(env, args[0]);int场景; if (model.get(GRB.IntAttr.IsMIP) == 0) { System.out.println("Model is not a MIP"); System.exit(1); } // Solve model model.optimize(); if (model.get(GRB.IntAttr.Status) != GRB.OPTIMAL) { System.out.println("Optimization ended with status " + model.get(GRB.IntAttr.Status)); System.exit(1); } // Store the optimal solution double origObjVal = model.get(GRB.DoubleAttr.ObjVal); GRBVar[] vars = model.getVars(); double[] origX = model.get(GRB.DoubleAttr.X, vars); scenarios = 0; // Count number of unfixed, binary variables in model. For each we // create a scenario. for (int i = 0; i < vars.length; i++) { GRBVar v = vars[i]; char vType = v.get(GRB.CharAttr.VType); if (v.get(GRB.DoubleAttr.LB) == 0 && v.get(GRB.DoubleAttr.UB) == 1 && (vType == GRB.BINARY || vType == GRB.INTEGER) ) { scenarios++; if (scenarios >= MAXSCENARIOS) break; } } System.out.println("### construct multi-scenario model with " + scenarios + " scenarios"); // Set the number of scenarios in the model */ model.set(GRB.IntAttr.NumScenarios, scenarios); scenarios = 0; // Create a (single) scenario model by iterating through unfixed // binary variables in the model and create for each of these // variables a scenario by fixing the variable to 1-X, where X is its // value in the computed optimal solution for (int i = 0; i < vars.length; i++) { GRBVar v = vars[i]; char vType = v.get(GRB.CharAttr.VType); if (v.get(GRB.DoubleAttr.LB) == 0 && v.get(GRB.DoubleAttr.UB) == 1 && (vType == GRB.BINARY || vType == GRB.INTEGER) && scenarios < MAXSCENARIOS ) { // Set ScenarioNumber parameter to select the corresponding // scenario for adjustments model.set(GRB.IntParam.ScenarioNumber, scenarios); // Set variable to 1-X, where X is its value in the optimal solution */ if (origX[i] < 0.5) v.set(GRB.DoubleAttr.ScenNLB, 1.0); else v.set(GRB.DoubleAttr.ScenNUB, 0.0); scenarios++; } else { // Add MIP start for all other variables using the optimal // solution of the base model v.set(GRB.DoubleAttr.Start, origX[i]); } } // Solve multi-scenario model model.optimize(); // In case we solved the scenario model to optimality capture the // sensitivity information if (model.get(GRB.IntAttr.Status) == GRB.OPTIMAL) { // get the model sense (minimization or maximization) int modelSense = model.get(GRB.IntAttr.ModelSense); scenarios = 0; for (int i = 0; i < vars.length; i++) { GRBVar v = vars[i]; char vType = v.get(GRB.CharAttr.VType); if (v.get(GRB.DoubleAttr.LB) == 0 && v.get(GRB.DoubleAttr.UB) == 1 && (vType == GRB.BINARY || vType == GRB.INTEGER) ) { // Set scenario parameter to collect the objective value of the // corresponding scenario model.set(GRB.IntParam.ScenarioNumber, scenarios); // Collect objective value and bound for the scenario double scenarioObjVal = model.get(GRB.DoubleAttr.ScenNObjVal); double scenarioObjBound = model.get(GRB.DoubleAttr.ScenNObjBound); System.out.print("Objective sensitivity for variable " + v.get(GRB.StringAttr.VarName) + " is "); // Check if we found a feasible solution for this scenario if (modelSense * scenarioObjVal >= GRB.INFINITY) { // Check if the scenario is infeasible if (modelSense * scenarioObjBound >= GRB.INFINITY) System.out.println("infeasible"); else System.out.println("unknown (no solution available)"); } else { // Scenario is feasible and a solution is available System.out.println("" + modelSense * (scenarioObjVal - origObjVal)); } scenarios++; if (scenarios >= MAXSCENARIOS) break; } } } // Dispose of model and environment model.dispose(); env.dispose(); } catch (GRBException e) { System.out.println("Error code: " + e.getErrorCode()); System.out.println(e.getMessage()); e.printStackTrace(); } } }
