广州住房和城乡建设局网站,广告推广服务平台,做英语网站,一直免费的服务器万能视频播放器Python数学规划案例一问题、模型、数据、算法、结果#xff0c;统一地表述#xff0c;是习惯也是效率。我的公众号数学规划模型表述习惯采用五个部分#xff1a;Set, Data, Variable, Objective, Constraints#xff1b;每个Notation#xff0c;采用一个主字符#xff0c… Python数学规划案例一问题、模型、数据、算法、结果统一地表述是习惯也是效率。我的公众号数学规划模型表述习惯采用五个部分Set, Data, Variable, Objective, Constraints每个Notation采用一个主字符上标表示含义下标来自Set已知的大写未知的小写下标用小写妥善选择Notation中的每一个字符对Notation、Objective、Constratins进行分组。模型即代码、即注释、即文章。数学规划模型编码习惯Ptyhon程序设计代码与模型表述严格一致 Set, Data, Variable, Objective, Constraints尽量相应“翻译”将模型、数据预处理、模型结果后处理分离开来保持模型的干净、纯粹。代码即模型、即文档、即文章。一个例子预算约束下营养配餐。取自...\IBM\ILOG\CPLEX_Studio129\python\examples\mp\modeling\diet.py模型采用五个部分Set, Data, Variable, Objective, Constraints建模。代码根据diet.py改写的代码如下。# -*- coding: utf-8 -*-from collections import namedtuplefrom docplex.mp.model import Modelfrom docplex.util.environment import get_environment# ----------------------------------------------------------------------------# Initialize the problem data# ----------------------------------------------------------------------------FOODS [ (Roasted Chicken, 0.84, 0, 10), (Spaghetti W/ Sauce, 0.78, 0, 10), (Tomato,Red,Ripe,Raw, 0.27, 0, 10), (Apple,Raw,W/Skin, .24, 0, 10), (Grapes, 0.32, 0, 10), (Chocolate Chip Cookies, 0.03, 0, 10), (Lowfat Milk, 0.23, 0, 10), (Raisin Brn, 0.34, 0, 10), (Hotdog, 0.31, 0, 10)]NUTRIENTS [ (Calories, 2000, 2500), (Calcium, 800, 1600), (Iron, 10, 30), (Vit_A, 5000, 50000), (Dietary_Fiber, 25, 100), (Carbohydrates, 0, 300), (Protein, 50, 100)]FOOD_NUTRIENTS [ (Roasted Chicken, 277.4, 21.9, 1.8, 77.4, 0, 0, 42.2), (Spaghetti W/ Sauce, 358.2, 80.2, 2.3, 3055.2, 11.6, 58.3, 8.2), (Tomato,Red,Ripe,Raw, 25.8, 6.2, 0.6, 766.3, 1.4, 5.7, 1), (Apple,Raw,W/Skin, 81.4, 9.7, 0.2, 73.1, 3.7, 21, 0.3), (Grapes, 15.1, 3.4, 0.1, 24, 0.2, 4.1, 0.2), (Chocolate Chip Cookies, 78.1, 6.2, 0.4, 101.8, 0, 9.3, 0.9), (Lowfat Milk, 121.2, 296.7, 0.1, 500.2, 0, 11.7, 8.1), (Raisin Brn, 115.1, 12.9, 16.8, 1250.2, 4, 27.9, 4), (Hotdog, 242.1, 23.5, 2.3, 0, 0, 18, 10.4)]Food namedtuple(Food, [name, unit_cost, qmin, qmax])Nutrient namedtuple(Nutrient, [name, qmin, qmax])# ----------------------------------------------------------------------------# Build the model# ----------------------------------------------------------------------------def build_diet_model(**kwargs): # Create tuples with named fields for foods and nutrients F [f[0] for f in FOODS] C {f[0]:f[1] for f in FOODS} Fmin {f[0]:f[2] for f in FOODS} Fmax {f[0]:f[3] for f in FOODS} N [n[0] for n in NUTRIENTS] Nmin {n[0]:n[1] for n in NUTRIENTS} Nmax {n[0]:n[2] for n in NUTRIENTS} D {(F[f],N[n]): FOOD_NUTRIENTS[f][n1] for f in range(len(F)) for n in range(len(N))} # Model mdl Model(namediet, **kwargs) # Decision variables, limited to be Food.qmin and Food.qmax x mdl.continuous_var_dict(F, lbFmin, ubFmax, nameF) # 2,4 # Limit range of nutrients, and mark them as KPIs for n in N: y mdl.sum(x[f] * D[f, n] for f in F) mdl.add_range(Nmin[n], y, Nmax[n]) # 3 mdl.add_kpi(y, publish_nameTotal %s % n) for f in F: mdl.add_kpi(x[f], publish_nameFood %s % f) # Minimize cost mdl.minimize(mdl.sum(x[f] * C[f] for f in F)) # 1 return mdl# ----------------------------------------------------------------------------# Solve the model and display the result# ----------------------------------------------------------------------------if __name__ __main__: mdl build_diet_model() mdl.print_information() mdl.export_as_lp() if mdl.solve(): mdl.float_precision 3 print(* model solved as function:) mdl.print_solution() mdl.report_kpis() # Save the CPLEX solution as solution.json program output with get_environment().get_output_stream(diet2.json) as fp: mdl.solution.export(fp, json) else: print(* model has no solution)扩展阅读Python数学规划之Cplex之旅Python利器Python哲学Python研究生
