Diet.java
/*版权2023,Gurobi Opt狗万app足彩imization, LLC */ /*解决经典饮食模型，展示如何向现有模型添加约束。*/进口gurobi.*;public class Diet {public static void main(String[] args) {try{//营养指南，基于//美国农业部美国人膳食指南，2005/ / http://www.health.gov/DietaryGuidelines/dga2005/ String Categories[] = new String[]{"卡路里"，"蛋白质"，"脂肪"，"钠"};int nCategories = Categories.length;double minNutrition[] = new double[] {1800, 91, 0,0};double maxNutrition[] = new double[] {2200, GRB。无穷大，65,1779};String foods [] = new String[]{"汉堡"，"鸡肉"，"热狗"，"薯条"，"通心粉"，"披萨"，"沙拉"，"牛奶"，"冰淇淋"};int nFoods = Foods.length;Double cost[] = new Double [] {2.49, 2.89, 1.50, 1.89, 2.09, 1.99, 2.49, 0.89, 1.59};/ /食品营养值双nutritionValues [] [] = new双[][]{{730年410年,24岁,26日},/ /汉堡{420年,32岁,10,1190},/ /鸡{560、20、1800},/ /热狗{380 4 270},/ /薯条{320、12、930},/ /通心粉{320、15、820},/ /比萨{12,320,31日1230},/ /沙拉{100、8,2.5,125},/ /牛奶{330、8、180}/ /冰淇淋}; // Model GRBEnv env = new GRBEnv(); GRBModel model = new GRBModel(env); model.set(GRB.StringAttr.ModelName, "diet"); // Create decision variables for the nutrition information, // which we limit via bounds GRBVar[] nutrition = new GRBVar[nCategories]; for (int i = 0; i < nCategories; ++i) { nutrition[i] = model.addVar(minNutrition[i], maxNutrition[i], 0, GRB.CONTINUOUS, Categories[i]); } // Create decision variables for the foods to buy // // Note: For each decision variable we add the objective coefficient // with the creation of the variable. GRBVar[] buy = new GRBVar[nFoods]; for (int j = 0; j < nFoods; ++j) { buy[j] = model.addVar(0, GRB.INFINITY, cost[j], GRB.CONTINUOUS, Foods[j]); } // 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 = new GRBLinExpr(); for (int j = 0; j < nFoods; ++j) { ntot.addTerm(nutritionValues[j][i], buy[j]); } model.addConstr(ntot, GRB.EQUAL, nutrition[i], Categories[i]); } // Solve model.optimize(); printSolution(model, buy, nutrition); System.out.println("JSON solution:" + model.getJSONSolution()); System.out.println("\nAdding constraint: at most 6 servings of dairy"); GRBLinExpr lhs = new GRBLinExpr(); lhs.addTerm(1.0, buy[7]); lhs.addTerm(1.0, buy[8]); model.addConstr(lhs, GRB.LESS_EQUAL, 6.0, "limit_dairy"); // Solve model.optimize(); printSolution(model, buy, nutrition); System.out.println("JSON solution:" + model.getJSONSolution()); // Dispose of model and environment model.dispose(); env.dispose(); } catch (GRBException e) { System.out.println("Error code: " + e.getErrorCode() + ". " + e.getMessage()); } } private static void printSolution(GRBModel model, GRBVar[] buy, GRBVar[] nutrition) throws GRBException { if (model.get(GRB.IntAttr.Status) == GRB.Status.OPTIMAL) { System.out.println("\nCost: " + model.get(GRB.DoubleAttr.ObjVal)); System.out.println("\nBuy:"); for (int j = 0; j < buy.length; ++j) { if (buy[j].get(GRB.DoubleAttr.X) > 0.0001) { System.out.println(buy[j].get(GRB.StringAttr.VarName) + " " + buy[j].get(GRB.DoubleAttr.X)); } } System.out.println("\nNutrition:"); for (int i = 0; i < nutrition.length; ++i) { System.out.println(nutrition[i].get(GRB.StringAttr.VarName) + " " + nutrition[i].get(GRB.DoubleAttr.X)); } } else { System.out.println("No solution"); } } }
