diet_c + + . cpp
/ * 2023年版权,Gurobi优狗万app足彩化,LLC * / / *解决经典的饮食模式,显示如何将约束添加到现有的模型。* / # include“gurobi_c + +。使用名称空间std h”;空白printSolution (GRBModel&模型、int nCategories int nFoods, GRBVar *买,GRBVar *营养);int主要(int命令行参数个数,char * argv []) {GRBEnv * env = 0;GRBVar *营养= 0;GRBVar *买= 0;尝试{/ /营养指南,根据农业部/ /为美国人设计的膳食指南,2005 / / http://www.health.gov/DietaryGuidelines/dga2005/ const int nCategories = 4;字符串类[]={“热量”、“蛋白质”,“胖”、“钠”};双minNutrition [] = {1800、91、0、0};双maxNutrition [] = {GRB_INFINITY 2200、65、1779}; // Set of foods const int nFoods = 9; string Foods[] = { "hamburger", "chicken", "hot dog", "fries", "macaroni", "pizza", "salad", "milk", "ice cream" }; double cost[] = { 2.49, 2.89, 1.50, 1.89, 2.09, 1.99, 2.49, 0.89, 1.59 }; // Nutrition values for the foods double nutritionValues[][nCategories] = { { 410, 24, 26, 730 }, // hamburger { 420, 32, 10, 1190 }, // chicken { 560, 20, 32, 1800 }, // hot dog { 380, 4, 19, 270 }, // fries { 320, 12, 10, 930 }, // macaroni { 320, 15, 12, 820 }, // pizza { 320, 31, 12, 1230 }, // salad { 100, 8, 2.5, 125 }, // milk { 330, 8, 10, 180 } // ice cream }; // Model env = new GRBEnv(); GRBModel model = GRBModel(*env); model.set(GRB_StringAttr_ModelName, "diet"); // Create decision variables for the nutrition information, // which we limit via bounds nutrition = model.addVars(minNutrition, maxNutrition, 0, 0, Categories, nCategories); // Create decision variables for the foods to buy // // Note: For each decision variable we add the objective coefficient // with the creation of the variable. buy = model.addVars(0, 0, cost, 0, Foods, nFoods); // The objective is to minimize the costs // // Note: The objective coefficients are set during the creation of // the decision variables above. model.set(GRB_IntAttr_ModelSense, GRB_MINIMIZE); // Nutrition constraints for (int i = 0; i < nCategories; ++i) { GRBLinExpr ntot = 0; for (int j = 0; j < nFoods; ++j) { ntot += nutritionValues[j][i] * buy[j]; } model.addConstr(ntot == nutrition[i], Categories[i]); } // Solve model.optimize(); printSolution(model, nCategories, nFoods, buy, nutrition); cout << "\nAdding constraint: at most 6 servings of dairy" << endl; model.addConstr(buy[7] + buy[8] <= 6.0, "limit_dairy"); // Solve model.optimize(); printSolution(model, nCategories, nFoods, buy, nutrition); } catch (GRBException e) { cout << "Error code = " << e.getErrorCode() << endl; cout << e.getMessage() << endl; } catch (...) { cout << "Exception during optimization" << endl; } delete[] nutrition; delete[] buy; delete env; return 0; } void printSolution(GRBModel& model, int nCategories, int nFoods, GRBVar* buy, GRBVar* nutrition) { if (model.get(GRB_IntAttr_Status) == GRB_OPTIMAL) { cout << "\nCost: " << model.get(GRB_DoubleAttr_ObjVal) << endl; cout << "\nBuy:" << endl; for (int j = 0; j < nFoods; ++j) { if (buy[j].get(GRB_DoubleAttr_X) > 0.0001) { cout << buy[j].get(GRB_StringAttr_VarName) << " " << buy[j].get(GRB_DoubleAttr_X) << endl; } } cout << "\nNutrition:" << endl; for (int i = 0; i < nCategories; ++i) { cout << nutrition[i].get(GRB_StringAttr_VarName) << " " << nutrition[i].get(GRB_DoubleAttr_X) << endl; } } else { cout << "No solution" << endl; } }
