MATLAB之数学建模：深圳市生活垃圾处理社会总成本分析
注：MATLAB版本--2016a,作图分析部分见《MATLAB之折线图、柱状图、饼图以及常用绘图技巧》
一.现状模式下的模型
%第一题：建立总成本分析模型/年：按现状分析
% 总成本=直接成本 +经济技术成本 + 社会成本
function dataPro = Total_Cost_Analysis(year)
%垃圾每年预测表：2017-2030
table = [ 6.4450e+06 6.8317e+06 7.2416e+06 7.6761e+06 7.9832e+06 8.3025e+06 8.6346e+06 8.9800e+06 9.3392e+06 9.6193e+06 9.9079e+06 1.0205e+07 1.0511e+07 1.0827e+07]; %垃圾总量每年数值（2017-2030）
rubbish_quantity = table(year-2016);
%将时间分期处理：2017-2020，2021-2025，2026-2030
switch year
case { 2017,2018,2019,2020} %近期
rubbish_num_burn=215*10^4;
rubbish_num_landfill = rubbish_quantity-rubbish_num_burn;
class_cost = 0;
handle_cost = rubbish_num_landfill*60+ rubbish_num_burn*100;
transport_cost = 0.5*rubbish_num_landfill*60+0.5*rubbish_num_landfill*70+...
0.5*rubbish_num_burn*60+0.5*rubbish_num_burn*70 ;
social_cost =132*rubbish_quantity;
technology_cost=1300*10^4; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 100*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity/102.49*10^8 ;
case {2021,2022,2023,2024,2025} %中期
rubbish_num_burn =215*10^4;
rubbish_num_landfill = rubbish_quantity-rubbish_num_burn;
class_cost = 0;
handle_cost = rubbish_num_landfill*60+ rubbish_num_burn*150;
transport_cost = rubbish_quantity*100;
social_cost =2*132*rubbish_quantity;
technology_cost=1300*10^4; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 50*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity*(1/104.15+1/100.1+1/317.46)*10^8 +rubbish_quantity/1.54*10^4; %不同时间 定值
case {2026,2027,2028,2029,2030} %远期
rubbish_num_burn =215*10^4;
rubbish_num_landfill = rubbish_quantity-rubbish_num_burn;
class_cost = 0;
handle_cost = rubbish_num_landfill*60+ rubbish_num_burn*180;
transport_cost = rubbish_quantity*100;
social_cost =2*132*rubbish_quantity;
technology_cost=1300*10^4; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 0*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity*(1/104.15+1/50.05+1/222.22)*10^8 +rubbish_quantity/1.54*10^4 ; %不同时间 定值
otherwise
msgbox('亲，请重新输入年份：');
end
%设施投资:
equipment_cost = 1.56*10^8;
%输出，分析:dataPro为数据集合
profit =profit *0.15;
direct_cost = class_cost + transport_cost + equipment_cost + handle_cost;
total_cost = direct_cost+technology_cost +social_cost+subsidy-profit ;
%dataPro（11）: 分类，收运，设施，处理，技术，社会，补贴，收益，直接，总，均
dataPro = [ class_cost,transport_cost,equipment_cost,handle_cost, ...
technology_cost,social_cost,subsidy,profit,direct_cost,total_cost,total_cost/rubbish_quantity];
end
二. 模式一
%模式一：总成本=直接成本 +经济技术成本 + 社会成本
function dataPro = Total_Cost_Analysis_model1(year)
%垃圾每年预测表：2017-2030
table = [ 6.4450e+06 6.8317e+06 7.2416e+06 7.6761e+06 7.9832e+06 8.3025e+06 8.6346e+06 8.9800e+06 9.3392e+06 9.6193e+06 9.9079e+06 1.0205e+07 1.0511e+07 1.0827e+07]; %垃圾总量每年数值（2017-2030）
rubbish_quantity = table(year-2016);
%将时间分期处理：2014-2020，2021-2025，2026-2030
switch year
case {2016,2017,2018,2019,2020}
rubbish_num_burn=215*10^4; %近期
rubbish_num_landfill = rubbish_quantity-rubbish_num_burn;
transport_cost = 0.5*rubbish_quantity*60+0.5*rubbish_quantity*70;
handle_cost = rubbish_num_landfill*60+rubbish_num_burn*100;
social_cost = 132*rubbish_quantity;
technology_cost=1300*10^4; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 100*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity/102.49*10^8 ; %不同时间 定值
case {2021,2022,2023,2024,2025}
transport_cost = rubbish_quantity*100;
handle_cost = rubbish_quantity*150;
social_cost =8*132*rubbish_quantity;
technology_cost=1300*10^4; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 50*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))* rubbish_quantity*(1/52.1+1/100.1 )*10^8 +rubbish_quantity/0.77*10^4;
case {2026,2027,2028,2029,2030}
transport_cost = rubbish_quantity*100;
handle_cost = rubbish_quantity*180;
social_cost =8*132*rubbish_quantity;
technology_cost=1300*10^4; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 0*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity*(1/52.07+1/50.05 )*10^8 +rubbish_quantity/0.77*10^4;
otherwise
msgbox('亲，请重新输入年份：');
end
%分类费用
class_cost = 0;
%设施投资:
equipment_cost = 0 ;
%输出，分析
profit =profit *0.15;
direct_cost = class_cost + transport_cost + equipment_cost + handle_cost;
total_cost = direct_cost+technology_cost +social_cost+subsidy-profit ;
%dataPro（11）: 分类，收运，设施，处理，技术，社会，补贴，收益，直接，总，均
dataPro = [ class_cost,transport_cost,equipment_cost,handle_cost, ...
technology_cost,social_cost,subsidy,profit,direct_cost,total_cost,total_cost/rubbish_quantity];
end
三. 模式二
%模式二：源头分类收集+湿垃圾生物处理+干垃圾焚烧+中心城区干垃圾转运
function dataPro = Total_Cost_Analysis_model2(year)
%垃圾每年预测表：2017-2030
table = [ 6.4450e+06 6.8317e+06 7.2416e+06 7.6761e+06 7.9832e+06 8.3025e+06 8.6346e+06 8.9800e+06 9.3392e+06 9.6193e+06 9.9079e+06 1.0205e+07 1.0511e+07 1.0827e+07]; %垃圾总量每年数值（2017-2030）
rubbish_quantity = table(year-2016);
switch year
case {2016,2017,2018,2019,2020}
rubbish_num_burn=215*10^4; %近期
rubbish_num_landfill = rubbish_quantity-rubbish_num_burn;
class_cost = 0;
transport_cost = 0.5*rubbish_quantity*60+0.5*rubbish_quantity*70;
handle_cost = rubbish_num_landfill*60+rubbish_num_burn*100;
social_cost =132*rubbish_quantity;
technology_cost=1300*10^4+10^8; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 100*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity/102.49*10^8 ; %不同时间 定值
case {2021,2022,2023,2024,2025}
class_cost = 10.6*10^8;
transport_cost = 0.4*rubbish_quantity*60+0.6*rubbish_quantity*100;
handle_cost = rubbish_quantity*150;
social_cost =1.2*132*rubbish_quantity; % (year-2017)
technology_cost=1300*10^4+0.7*10^8; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 50*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity*(1/72.31+1/396.83+1/100.1)*10^8 +rubbish_quantity/1.28*10^4 ; %不同时间 定值
case {2026,2027,2028,2029,2030}
class_cost = 10.6*10^8;
transport_cost = 0.5*rubbish_quantity*60+0.5*rubbish_quantity*100;
handle_cost = 0.5*rubbish_quantity*180+ 0.5*rubbish_quantity*200;
social_cost =1.2*132*rubbish_quantity; % (year-2017)
technology_cost=1300*10^4+0.4*10^8; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 0*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))* rubbish_quantity*(1/77.98+1/277.78+1/50.05)*10^8 +rubbish_quantity/1.28*10^4;
otherwise
msgbox('亲，请重新输入年份：');
end
%设施投资:
equipment_cost = 0;
%输出，分析
profit =profit *0.15;
direct_cost = class_cost + transport_cost + equipment_cost + handle_cost;
total_cost = direct_cost+technology_cost +social_cost+subsidy-profit ;
%dataPro（11）: 分类，收运，设施，处理，技术，社会，补贴，收益，直接，总，均
dataPro = [ class_cost,transport_cost,equipment_cost,handle_cost, ...
technology_cost,social_cost,subsidy,profit,direct_cost,total_cost,total_cost/rubbish_quantity];
end
四. 模式三
%模式三：混合收集+末端分类+湿垃圾生物处理+干垃圾焚烧+中心城区干垃圾转运
function dataPro = Total_Cost_Analysis_model3(year)
%垃圾每年预测表：2017-2030
table = [ 6.4450e+06 6.8317e+06 7.2416e+06 7.6761e+06 7.9832e+06 8.3025e+06 8.6346e+06 8.9800e+06 9.3392e+06 9.6193e+06 9.9079e+06 1.0205e+07 1.0511e+07 1.0827e+07]; %垃圾总量每年数值（2017-2030）
rubbish_quantity = table(year-2016);
switch year
case {2016,2017,2018,2019,2020}
rubbish_num_burn=215*10^4; %近期
rubbish_num_landfill = rubbish_quantity-rubbish_num_burn;
transport_cost = 0.5*rubbish_quantity *60+0.5*rubbish_quantity *70 ;
handle_cost = rubbish_num_landfill*60+rubbish_num_burn*100;
social_cost =132*rubbish_quantity;
technology_cost=1300*10^4+10^8; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 100*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity/102.49*10^8 ; %不同时间 定值
case {2021,2022,2023,2024,2025}
handle_cost = 0.5*rubbish_quantity*150+ 0.5*rubbish_quantity*200;
transport_cost = 0.4*rubbish_quantity*60+0.6*rubbish_quantity*100;
social_cost =132*rubbish_quantity;
technology_cost=1300*10^4+0.7*10^8; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 50*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity*(1/86.87+1/317.46+1/100.1)*10^8 +rubbish_quantity/1.54*10^4; %不同时间 定值
case {2026,2027,2028,2029,2030}
handle_cost = 0.5*rubbish_quantity*180+ 0.5*rubbish_quantity*200;
transport_cost = 0.4*rubbish_quantity*60+0.6*rubbish_quantity*100;
social_cost =132*rubbish_quantity;
technology_cost=1300*10^4+0.4*10^8; % 湿处理分期：10^8,0.7*10^8，0.4*10^8
subsidy = 0*rubbish_quantity; %前期 100，中期50，后期取消，成本计算取负
profit = (10^(-4))*rubbish_quantity*(1/86.80+1/222.22+1/50.05)*10^8 +rubbish_quantity/1.54*10^4; %不同时间 定值
otherwise
msgbox('亲，请重新输入年份：');
end
%设施投资:
equipment_cost = 0;
%分类
class_cost = 0;
%输出，分析
profit =profit *0.15;
direct_cost = class_cost + transport_cost + equipment_cost + handle_cost;
total_cost = direct_cost+technology_cost +social_cost+subsidy-profit ;
%dataPro（11）: 分类，收运，设施，处理，技术，社会，补贴，收益，直接，总，均
dataPro = [ class_cost,transport_cost,equipment_cost,handle_cost, ...
technology_cost,social_cost,subsidy,profit,direct_cost,total_cost,total_cost/rubbish_quantity];
end
五. 垃圾总量预测
%垃圾总量预测
rubbish_table = zeros(1,14);
rubbish_table(1,1) = 541.14*10^4; %2014年垃圾产量541.14万吨
for year = 2015:2020
rubbish_table(1,year-2013) = rubbish_table (1,year-2014)*(1+0.06);
end
for year = 2021:2025
rubbish_table(1,year-2013) = rubbish_table (1,year-2014)*(1+0.04);
end
for year = 2026:2030
rubbish_table(1,year-2013) = rubbish_table (1,year-2014)*(1+0.03);
end
六.各模式数据汇总
% 总成本=直接成本 +经济技术成本 + 社会成本
%数据收集data_model（从2017-2030年：现状，模式一，模式二，模式三）
clear;close;clc;
data_model0 = zeros(14, 11);
data_model1 = zeros(14, 11);
data_model2 = zeros(14, 11);
data_model3 = zeros(14, 11);
for year = 2017 : 2030
dataPro0 = Total_Cost_Analysis(year ); %现状
dataPro1 = Total_Cost_Analysis_model1(year );
dataPro2 = Total_Cost_Analysis_model2(year );
dataPro3 = Total_Cost_Analysis_model3(year );
for i = 1:11
data_model0(year-2016,i) = dataPro0(i);
data_model1(year-2016,i) = dataPro1(i);
data_model2(year-2016,i) = dataPro2(i);
data_model3(year-2016,i) = dataPro3(i);
end
end
七.最优模式评选
%优选模式计算：分类0.3，设施0.5，收运1.5，处理1，技术1，社会1.7，收益-2
%原理较复杂，优选模式以远期成本最优，并且设定不同成本的比重，所得结果为每吨垃圾的成本
%
table = [9.6193e+06 9.9079e+06 1.0205e+07 1.0511e+07 1.0827e+07]; %垃圾总量每年数值（远期2025-2030）
rubbish_quantity = sum(table,2);
x=0.3*sum(data_model0(10:14,1))+0.5*sum(data_model0(10:14,3))+1.5*sum(data_model0(10:14,2))+...
1*sum(data_model0(10:14,4))+1*sum(data_model0(10:14,5))+1.7*sum(data_model0(10:14,6))+...
2*sum(data_model0(10:14,8));
table0 =x/rubbish_quantity %现状模式 /rubbish_quantity
x=0.3*sum(data_model1(10:14,1))+0.5*sum(data_model1(10:14,3))+1.5*sum(data_model1(10:14,2))+...
1*sum(data_model1(10:14,4))+1*sum(data_model1(10:14,5))+1.7*sum(data_model1(10:14,6))+...
2*sum(data_model1(10:14,8));
table1 = x/rubbish_quantity %模式一
x=0.3*sum(data_model2(10:14,1))+0.5*sum(data_model2(10:14,3))+1.5*sum(data_model2(10:14,2))+...
1*sum(data_model2(10:14,4))+1*sum(data_model2(10:14,5))+1.7*sum(data_model2(10:14,6))+...
2*sum(data_model2(10:14,8));
table2 = x/rubbish_quantity %模式二
x=0.3*sum(data_model3(10:14,1))+0.5*sum(data_model3(10:14,3))+1.5*sum(data_model3(10:14,2))+...
1*sum(data_model3(10:14,4))+1*sum(data_model3(10:14,5))+1.7*sum(data_model3(10:14,6))+...
2*sum(data_model3(10:14,8));
table3 = x/rubbish_quantity %模式三
来源：https://www.cnblogs.com/HZL2017/p/6880648.html