【车间布局优化】基于遗传算法的车间布局优化 车间设施布局优化【matlab代码】

clear clc close all %% 车间参数 L = 150; % 场所长度 W = 60; % 场所宽度 LW0 = xlsread('数据.xlsx',1,'C2:D15'); % 设施的长宽 T = xlsread('数据.xlsx',2,'B2:O15'); % 功能关系 A = xlsread('数据.xlsx',3,'B2:O15'); % 物流量 C = xlsread('数据.xlsx',4,'B2:O15'); % 物流成本 Gnum = size(LW0,1); % 设施数量 dim = 2 * Gnum; %% 遗传算法参数 NP = 100; % 种群大小 maxgen = 500; % 迭代数 Pc = 0.8; % Pm = 0.4; % 变异概率 Gap = 0.9; % 代沟(Generation gap) minbound = zeros(1,dim); % maxbound = L * ones(1,dim); % 边界 big = 1000000; %% 初始化种群:在满足最小间距的条件下，依次放置，保证初始种群满足约束 X = zeros(NP,dim); % 矩形中心坐标编码 Y = zeros(NP,Gnum); % 矩形长宽方向编码 trysteps = 10000; for i = 1 : NP disp(['初始化进程' num2str(i) '/' num2str(NP)]) trying = 1; while trying % 如果多次尝试摆放仍不能满足约束，则重新开始摆放 trying = 0; y = round(rand(1,Gnum)); y(1) = 0; y(end) = 0; LW = LW0; for j = 1 : Gnum if y(j) == 1 LW(j,1:2) = LW0(j,2:-1:1); end end % 放入第一个矩形，直至满足约束 flag = 0; while ~flag p = [L * rand,W * rand]; flag = isInRange(p,LW(1,:),L,W); end P = p; % 依次放入其他矩形 for k = 2 : Gnum dtn = 1; step = 0; % 试探次数，如果经过很多次试探无法成功，说明第一个摆放有问题 while dtn > 0 flag = 0; while ~flag % 保证放置的位置满足边界限制 p = [L * rand,W * rand]; flag = isInRange(p,LW(k,:),L,W); end Pt = [P ;p]; LWt = LW(1:k,:); d = CalDistance(Pt,LWt,big); % 计算已放置的设施之间的距离及是否满足间隔 dtag = (d == big); dtn = sum(sum(dtag)); % 不满足间隔要求的数量 step = step + 1; if step > trysteps trying = 1; break end end P = [P; p]; end end X(i,:) = P(:)'; end Pg = P; cla for j = 1 : Gnum rectangle('Position',[Pg(j,1)-LW(j,1)/2,Pg(j,2)-LW(j,2)/2,LW(j,1),LW(j,2)],'EdgeColor','r') text(Pg(j,1),Pg(j,2),num2str(j)) hold on end axis equal axis([0 L 0 W]) box on %% 进化 gen = 1; Fx = zeros(NP,1); while gen <= maxgen gen % 计算路线长度 for i = 1 : NP p = [X(i,1:Gnum); X(i,Gnum+1:end)]'; y = Y(i,:); y(1) = 0; y(end) = 0; LW = LW0; for j = 1 : Gnum if y(j) == 1 LW(j,1:2) = LW0(j,2:-1:1); end end D = CalDistance(p,LW,big); Fx(i,1) = Fitness(p,LW,D,A,C,T,L,W,big); % 计算目标函数值 end % 记录各代最优值 fgbest = min(Fx); FG(gen) = fgbest; % 各代最短路径 % 计算适应度 fit = 1 ./ (Fx+1); % 将求最小路径转为最大值fit % 选择 XSel = Select(X,fit,Gap); YSel = Select(Y,fit,Gap); % 交叉操作 XSel = XCross(XSel,Pc); YSel = YCross(YSel,Pc); % 粒子冲撞 XSel = XMutate(XSel,Pm,minbound,maxbound); YSel = YMutate(YSel,Pm); % 重插入子代的新种群 X=Reins(X,XSel,fit); Y=Reins(Y,YSel,fit); % 更新迭代次数 gen=gen+1 ; end %% 画出最优解的路线图 clc [fgbest,minInd] = min(Fx); xgbest = X(minInd(1),:); ygbest = Y(minInd(1),:); figure plot(FG) xlabel('迭代次数') ylabel('最优目标函数值') title('种群迭代曲线') figure Pg = [xgbest(1:Gnum); xgbest(Gnum+1:end)]'; ygbest(1) = 0; ygbest(end) = 0; LW = LW0; for j = 1 : Gnum if ygbest(j) == 1 LW(j,1:2) = LW0(j,2:-1:1); end end D = CalDistance(Pg,LW,big); [ff,T1,T2] = Fitness(Pg,LW,D,A,C,T,L,W,big); % fprintf('目标函数1的最优值为 %f \n', T1) fprintf('目标函数2的最优值为 %f \n', T2) disp('各区域中心对应坐标值为') disp(Pg) for i = 1 : Gnum rectangle('Position',[Pg(i,1)-LW(i,1)/2,Pg(i,2)-LW(i,2)/2,LW(i,1),LW(i,2)],'EdgeColor','r') text(Pg(i,1),Pg(i,2),num2str(i)) hold on end axis equal axis([0 L 0 W]) box on title('优化后设施布局位置')