clear; clc; close all;
tic
%% input
c101 = importdata('c101.txt');
% c101 = importdata('my_test_data.xlsx');
% depot_time_window1 = c101(1,5); % time window of depot
% depot_time_window2 = c101(1,6);depot_time_window1 = TimeTrans(c101(1,5)); % time window of depot
depot_time_window2 = TimeTrans(c101(1,6));
vertexs = c101(:,2:3);
customer = vertexs(2:end,:); % customer locations
customer_number = size(customer,1);
% vehicle_number = 25;
% time_window1 = c101(2:end,5);
% time_window2 = c101(2:end,6);time_window1 = TimeTrans(c101(2:end,5));
time_window2 = TimeTrans(c101(2:end,6));width = time_window2-time_window1; % width of time window
service_time = c101(2:end,7);
h = pdist(vertexs);
dist = squareform(h); % distance matrix
%% initialize the parameters
ant_number = floor(customer_number * 1.5); % number of ants
alpha = 4; % parameter for pheromone
beta = 5; % paremeter for heuristic information
gamma = 2; % parameter for waiting time
delta = 3; % parameter for width of time window
r0 = 0.5; % a constant to control the movement of ants
rho = 0.85; % pheromone evaporation rate
Q = 5; % a constant to influence the update of pheromene
Eta = 1./dist; % heuristic function
iter = 1; % initial iteration number
iter_max = 200; % maximum iteration numberTau = ones(customer_number+1,customer_number+1); % a matrix to store pheromone
Table = zeros(ant_number,customer_number); % a matrix to save the route
Route_best = zeros(iter_max,customer_number); % the best route
Cost_best = zeros(iter_max,1); % the cost of best routeiter_time = [];
last_dist = 0;
stop_count = 0;%% find the best route
while iter <= iter_max
%tic;
% ConstructAntSolutions
for i = 1:ant_number
for j = 1:customer_number
r = rand;
np = NextPoint(i,Table,Tau,Eta,alpha,beta,gamma,delta,r,r0,time_window1,time_window2,width,service_time,depot_time_window2,dist);
Table(i,j) = np;
end
end
%% calculate the cost for each ant
cost = zeros(ant_number,1);
NV = zeros(ant_number,1);
TD = zeros(ant_number,1);
for i=1:ant_number
VC = decode(Table(i,:),time_window1,time_window2,depot_time_window2,service_time,dist);
[cost(i,1),NV(i,1),TD(i,1)] = CostFun(VC,dist);
end
%% find the minimal cost and the best route
if iter == 1
[min_Cost,min_index] = min(cost);
Cost_best(iter) = min_Cost;
Route_best(iter,:) = Table(min_index,:);
else
% compare the min_cost in this iteration with the last iter
[min_Cost,min_index] = min(cost);
Cost_best(iter) = min(Cost_best(iter - 1),min_Cost);
if Cost_best(iter) == min_Cost
Route_best(iter,:) = Table(min_index,:);
else
Route_best(iter,:) = Route_best((iter-1),:);
end
end
%% update the pheromene
bestR = Route_best(iter,:); % find out the best route
[bestVC,bestNV,bestTD] = decode(bestR,time_window1,time_window2,depot_time_window2,service_time,dist);
Tau = updateTau(Tau,bestR,rho,Q,time_window1,time_window2,depot_time_window2,service_time,dist); %% print
disp(['Iterration: ',num2str(iter)])
disp(['Number of Robots: ',num2str(bestNV),', Total Distance: ',num2str(bestTD)]);
fprintf('\n')
%
iter = iter+1;
Table = zeros(ant_number,customer_number);
%iter_time(iter) = toc;
% if last_dist == bestTD
% stop_count = stop_count + 1;
% if stop_count > 30
% break;
% end
% else
% last_dist = bestTD;
% stop_count = 0;
% end
end
%% draw
bestRoute=Route_best(iter-1,:);
[bestVC,NV,TD]=decode(bestRoute,time_window1,time_window2,depot_time_window2,service_time,dist);
draw_Best(bestVC,vertexs);
figure(2)
plot(1:iter_max,Cost_best,'b')
xlabel('Iteration')
ylabel('Cost')
title('Change of Cost')
%% check the constraints, 1 == no violation
flag = Check(bestVC,time_window1,time_window2,depot_time_window2,service_time,dist)toc
