![](data:image/png;base64,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)
2.3.2 Текст компьютерного решения задачи.
Programkurs9k;
uses crt;
type armarka=record
c1:real; {odezda}
c2:real; {obyv}
c3:real; {knigi}
c4:real; {igryshki}
end;
var
a:array [1..4] of armarka;
n1,n2,n3,n4:real; {chisla dlya min (minZij)}
codezda, cobyv, cknigi, cigryshki:real; {ceni za tovar}
b:array[1..4] of armarka;
i:integer;
z:integer; {dni}
r1,r2,r3,r4:real; {chisla dlya max (max b[i])}
min:real;
{shapka}
procedure shapka;
begin
writeln('___________________________________________________');
writeln('| | tovar | Zij - minZij |');
writeln('| |___________________________|__________________|');
writeln('| den | odezda | obyv | knigi |igryshki| c1 | c2 | c3 | c4 |');
writeln('|_____|______|_______|_____|_______|____|_____|____|____|');
end;
{vvod cen na tovar}
begin
writeLn('vvedite ceny na odezgy ');
readln(codezda);
writeLn('vvedite ceny na obyv ');
readln(cobyv);
writeLn('vvedite ceny na knigi ');
readln(cknigi);
writeLn('vvedite ceny na igryshki ');
readln(cigryshki);
{naxogdenie cluchainim obrazom pokupateley}
for i:=1 to 4 do
begin
a[i].c1:=random(100)+1;
a[i].c2:=random(100)+1;
a[i].c3:=random(100)+1;
a[i].c4:=random(100)+1;
end;
{podshet cen- pokupateli*ctovara}
for i:=1 to 4 do
begin
a[i].c1:= a[i].c1*codezda;
a[i].c2:= a[i].c2*cobyv;
a[i].c3:= a[i].c3*cknigi;
a[i].c4:= a[i].c4*cigryshki;
end;
{naxogdenie min v kagdom stolbce}
n1:=a[1].c1;
for i:=1 to 4 do
begin
if n1>a[i+1].c1 then
n1:=a[i+1].c1;
end;
n2:=a[1].c2;
for i:=1 to 4 do
begin
if n2>a[i+1].c2 then
n2:=a[i+1].c2;
end;
n3:=a[1].c3;
for i:=1 to 4 do
begin
if n3>a[i+1].c3 then
n3:=a[i+1].c3;
end;
n4:=a[1].c4;
for i:=1 to 4 do
begin
if n4>a[i+1].c4 then
n4:=a[i+1].c4;
end;
{nahogdenie (zij-minzij)}
writeln;
for i:=1 to 4 do
begin
b[i].c1:=a[i].c1-n1;
b[i].c2:=a[i].c2-n2;
b[i].c3:=a[i].c3-n3;
b[i].c4:=a[i].c4-n4;
end;
r1:=b[1].c1;
for i:=1 to 4 do
begin
if r1<b[i+1].c1 then
r1:=b[i+1].c1;
end;
r2:=b[1].c2;
for i:=1 to 4 do
begin
if r2<b[i+1].c2 then r2:=b[i+1].c2;
end;
r3:=b[1].c3;
for i:=1 to 4 do
begin
if r3<b[i+1].c3 then r3:=b[i+1].c3;
end;
r4:=b[1].c4;
for i:=1 to 4 do
begin
if r4<b[i+1].c4 then r4:=b[i+1].c4;
end;
{nahogdenie otveta}
min:=r1;
z:=1;
if min> r2 then
begin
min:=r2;
z:=2;
end;
if min> r3 then
begin
min:=r3;
z:=3;
end;
if min> r4 then
begin
min:=r4;
z:=4;
end;
shapka;
{vivod podschitannogo}
for i:=1 to 4 do
begin
writeln('| day ',i:1 ,'|',a[i].c1:8:2,'|',a[i].c2:8:2,'|',a[i].c3:8:2,'|',a[i].c4:8:2,'|',
b[i].c1:7:2,'|',b[i].c2:7:2,'|',b[i].c3:7:2,'|',b[i].c4:7:2,'|' );
writeln('|_________________________________________________________________|');
end;
writeln;
writeln;
writeln(' ________________________________________________________________ ');
writeln('| |') ;
writeln('|Max(Zij-minZij):|', r1:13:2,'| ',r2:13:2,'| ',r3:13:2,'| ',r4:13:2,'|' );
writeln('|_________________________________________________________________|');
writeln;
writeln('Otvet:Optimalni variant (po kriteriu Sevidga) coctavliyaet za 3 den s zna4eniem ', min:7:2);
readln ;
repeat until keypressed;
readln;
end.
{konec}
2.3.2. Инструкция по использованию решения задачи.
Во время запроса нужно ввести цены на товар (1)
Рисунок 1
![](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACoAVMDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwC7428S67Y+M76zstVu4YEClY4zwPkBNcyfHXiAzCFNfvzJsDlWBXCnoeRzmtj4i6TPc+M72cXUFup2lWN4kLY2BT1IOOorkbfw8ts2Y76wY7dg36lG2BknAy3qSfxr6XDTpfu7unyWV725r21/E8erGfvW5ua7tvbc7W6vPGVrfRWzeIpGWSYwGYSkKjhwhByM8Fl6A8GrYj8cC3M0niGWNUTfICzErwxxgLyflPA9q5i+m1PUXJvNT0t2563cA2ktuJGDwSepHJq7NrHiC6MztrOm7pE2SvHdQKWUAgZIPYFvzNdLlh7K0qd+uxmlVu7qf4m5HB45kmMQ8QsWDmPIlJG/eUIyF9Rk+xFRj/hN/s0FydfcQSwmYv5pIjUIH+bjjg9emRWBZX+rafZ/ZbfVtLWETeeA13AxEnZgSSQaSS91aWJIn1rT9iQNbgC+hGY2AUqcHkEKOvpSvQv8dO3yFapb4Z/iboufF2LgN4klWWCaWF4yzZ3RoXODtxgquQfpVi2j8a3UFpKniJwLpFdNztgBtnU7cZ/eDjqcHFc619q/m+a+rabuZ/MJN3Bhjs8sk885Xg+tTRaxrcTxmPWdMAiGEQXcG1R8uBtzjjYuPTFEpULaSp/+S/5AlU6qf4mxNJ4ygC7/ABBIGljMkKmQgyAR+YwxjghfWqtlqviq9tbS4XxFMgu52t4Q7nlwAcE4wM7gBWa2o6o0JhOraWIwnlqq3cA8tdu0heflyvBxTYru+h8vy9R0hTFI0sRFzb5jZgASvPHQdPSqUsNbWVO//bv9dvxE1Wvop/ia19qfirT7aWabxDM3lXBtmVHJxIFBYZxjjOPqDWZ/wl/iP/oNXn/ff/1qjuri9vYmiutT0qQNJ5hLXcAbfgKTnOckKM+uOapixycC+0wn0F9F/wDFVrTngVH33C//AG6Zzjib+6pW+Zo/8Jf4j/6DV5/33/8AWp3/AAl3iL/oNXf/AH3/APWrONjtOGvtNB9DfRA/+hUpstpIa+00EdQb6IH/ANCq/aZd3h/5KRy4vtL8TR/4S7xF/wBBm7/77/8ArUv/AAlviH/oM3f/AH3/APWrOFmCDi/0045OL6Lj/wAeo+yr/wBBDTP/AAOi/wDiqXtMv7w/8lHy4v8Avfiaf/CWeIf+gzd/99//AFqX/hLPEP8A0GLv/vus37Kn/QQ0z/wOi/8AiqX7Mg63+mjP/T9F/wDFUvaZf3h/5KHLi/734mn/AMJX4g/6DF3/AN90o8V+IP8AoMXf/fdZpt0U4bUNMB9DfRf/ABVKII+n9oaZ/wCB0X/xVHtMv7w/8lDlxf8Ae/E0v+Er8Qf9Be6/77pf+Eq1/wD6C91/33Wb5Ef/AEENM46/6dF/8VR5MX/QR0z/AMDov/iqXtMv7w/8lDlxf978TT/4SnX/APoL3X/fVOHinXv+gvdf99VmiBDjF/pxycDF7HyfzpfJiAydQ03HTP22P/Gl7TL+8P8AyUOXF/3vxNL/AISjXv8AoLXX/fVO/wCEn13/AKC11/31WYIosZ/tDTcZxn7bH19OtL5cOdv9oabuzjH22PP86PaYDvD/AMlDlxf978TT/wCEn13/AKC11/31S/8ACT67/wBBa6/76rMMcIOG1HTQR1BvY/8AGnBIcE/2jpuB1P22Pj9aXPgO8P8AyUOXF/3vxNL/AISbXP8AoK3X/fVL/wAJLrn/AEFbn/vqszFv/wBBLTP/AAOi/wAadiAYzqOmj63sf+NLnwHeH4By4v8AvfiaY8S63/0Fbn/vqnf8JJrf/QUuf++qy8W69dR00fW9j/xpQbb/AKCemf8AgbH/AI0ufAd4fgHLi/734mp/wkmt/wDQUuf++qX/AISPWv8AoKXP/fVZe61/6Cemf+Bsf+NLvtR11PTP/A2P/GjnwHeH4By4v+9+Jq/8JHrX/QTuf++qX/hIta/6Cdz/AN9Vl77XGf7T03H/AF+x/wCNL5lp/wBBPTP/AANj/wAaXPgO8PwDlxf978TU/wCEi1n/AKCdz/31S/8ACQ6z/wBBO4/76rM32uM/2lpuD3+2R4/nR5tn/wBBTTP/AANj/wAaXPge8PwDlxf978TqPD2p6jqOsw2tzqN00TK5IWTB4Ukc10uqp/Ztstw1zqJRWJdROdzKEZsDPf5a4HSNXstK1FLs3+my7VZdgv4xnII6596vw+IdAtbUWlrb6XFatKZJYv7URhJlCpHJJ5Brw8ycXWXsGrW6W8z1MEpKm/ap3v1ua93qd4sflta6zp0+6NlN1cxuHQyBWxsZuee9TX2tWEWoX+kQXmrf2rbQySmNmbCoq5EhPTYSQAe54rn7rxDo80QWA6dDJvQmRtVWQ7VYNt+Zu+Kfd+IPD91Lc3Edno8d9P5h+0jUIzJuddhOep4wMdOK471ORaq931Xl/wAE6bQ5no/x8yjq8jzaiZJGLu8UTMx6k+WtFLrMT2+ptDKNskccSsPQiNaK5alud27m8L8qucx8X9Ra18Y3Fq1pc3UUotZTGmfLZUZt6H/e3KPy9q86nvtOWzuRb+H3VisYEssRPlLhwzZBGGbKkdsrXpHxkutTtddv7izvTFFAttlBFlgWLHIfsMxJkd9q15Y/jDWZIriPz0VJipKpGFC7RgAAdsDpVrYGdBrVzbXH26efw0Y7m4tmuvOZ1BCuXCkjsQSvu2znrWPpV1qPhuHVLdtPuUvJIoyu+IlYgGzuYHtjPtVWbxVqNxLcSSrbs09u1s/7v+AuXI/76Oc0Hxbqx+1nzIwbuNY5yqAF9qlQxx3wx/OmBd/tHRrhWFpoEp2WjRsQA5B6hz6EdCcdu1SW93pgvoppPDU8sC2ozCE4/hBbOMsOGwx6E+1YFjqtzp8FzBCUMdyoEiuuemcEe4ya0x411sTRy/aELxoiAlAeEZWHH1VfyoAr6ndLPoel24gula1DqZJV+Vg53DFS+HrmbR9RWabTriaO5i2qqqQzAFWBXIOeVH4E1V1DXbi/0yz09htgtgOpyzkDAJPoBwB25qzB4v1e3+zeXLH/AKMoERMYJXCKgI98Iv8AkmgC/FrWlXdza7vD/m3A84zqi7jK7BsH6AkH220sbpLZWWoLoPl+VfjzWgjGHJ3NsVTljwD7DbWFbazd2mqTahEYxNNv8wFflYP94Y/GtFPGmtIVbzoywZm3GMZOfMz0/wCusnT+97CgDQa/04jzB4auJGa5kIkkTO4sZMA8ckFlGP8ApnnqTWLcSyQa22o2Nnc2MKSB1VAQYxxnBxx1H0yKni8WX6SI0gjYCZpmKjazlt2cnv8AeI5zxxTr/wAW3d9aX0Hkxxi9l8yQqc7R8uVX0B2Jn12jpQAf29avr0eqywTO1vPCbeHcCvkpwFYnktgLz65qr4h1aLWL+KeGBoljgWL5jyxGeT+f6Vk0UAbmk61b2Omy2k0Ex3SmQtC4QygoVMbnB+XnP4n1yNE6x4akS+lOlBHZFWCLaOThwxyOFxuTp12Z7muSooA6q51vQIoZUttGt5pWiXDuh2CQn5sDIbaF4HfJPoKfZaz4eYQfbrFm8izER3IDudWZvlA4Gdwz9OvJrkqKAOsbxPpckYWXSg8v2RbZp2VGb5Y4lDYIIzlG59G9qwtH1BNNv/Pki8xSjJlTh49wxvQ9mHUVQooA34tZ06C402eCyeNrVf3qrtHnMFAOT1IY5yD0BIFVdF1SDTvEMOozwZiSQuYY1GOc8Dd0FZVFAHV6b4tisLSxtXtTNHbySEsygHYUdUA+hkYnoTwM8A1De+ILHUILuCSG5jju74XDYYN5KbnJCDjkhvbkflzVFAHTWPiS3s9PXTxHOYFvBKki7VkSIEnAPTeTj5scYwOtUZ9Xil8Ux6rtk8pJo5NuAGwuOP06nn1yax6KAOv1PxRo+peY76QTMSoSViCcBlP6AMMc53c9BVSTXdMeXVhHp7WtrdxCKGG34AAlDgtknLYXbkeufrzdFAHUXOueHpEnWHQQnmJtBLD5SFcAj0OShOOu0+tUfEWr2mrS2ps7U20VvE0Sx8YCmRnAGPTdj3xnvWLRQB1H/CQ6VIym60x7kC0gtgZGGV2IVYr6ZO0+2KSPX9GMiSXGixykWSQbdqqokUKC424zuAPJ5Ga5iigDp4Nf0SI26SaBBIkcEaSMR87uGTeeuMFVYDjILZpLXXNEtorRRo/7xI5Y5pGCuX3oVzhhjKnkZHfHvXM0UAdDY6xpMNlaw3Vg87QyzOeF2/OFC8dyu3v1z7CpDregt5A/sIII5N/ysDkZkOCT94fNF1/uHpmuaooA3b/V9Pn0m8srW2mgWW+F1DHuBSJdpGz1/i6+wrCoooAKKKKACpLf/j5i/wB8fzqOpLf/AI+Yv98fzoA+mvFv/I0X3+8v/oIoo8W/8jRff7y/+giisSji/jLDpsnjGVr3VLq3IhiV7eJSQylhhh2PHmZz/dX1rzmTSfDqQXRGtu8sWwJtjyrE/eIzgkDp68V6R8aL6SPxHe2McF7I9zbIFMYzEBuGRjH3iQuT1+6BjJz40bK6CM5tptqAFm8s4APQn61qtiWdNe2Xh3zLhLS5h2x6fiN2kP7ycSEZ+pQA+gqK503wy0188GpyJEkCPapncXYqc7sgYIIUbevze1ZM+g6jbAma3KYthcnJ6ITgZ9Dnt1qGfSdRtZpIZ7G4jkjUO6mM5VT3PoKYEumWthcQXjXl2YZY0BhTON55zzg9OOO+a2E0jwwZYlbW5djQoWfYBh2ZQeMHgAscdfl965qK3nn3+TDJJsXc2xSdo9TjoKlGn3rfds7g5TzBiJvu/wB7p096AL+paZY2ekWVzFdO91cAM0DAZQY+Yn0G7p6jNXINK8OyGx8zWXjDqDcgrkqfLVjt47MWXnrt681z7288cYkeGRUbGGZSAcjI5+lSfYLwmIC0nzMMxfuz8468ev4UAXLGy0+XWZba7vtlqm8JMvAkIzt55xn1raGleHLu3tIIL5vtAleNmjU7pV3vtYqfu4UKT7HpmuasrGe/uTb26gyhHfazAcKpY9fYGnDS78yRIbOdTM+yPdGQGb05oA09W03Q7O0drLVHublHEXl7eCRnewPQr93B788Vg1aOm3okKi2lbEhj3KhKls4wD061DcQSWtzLbygCSJyjAHPIODQBHRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFSW//AB8xf74/nUdSW/8Ax8xf74/nQB9NeLf+Rovv95f/AEEUUeLf+Rovv95f/QRRWJRyvxdn1BPEepG1vZrdba0juBiIEEh1BCv1BJ2Egf3Fryh/F+syRzxm4VUmKkqsYULtGBtA4AwOlemfGaDSpfGcrX2o3MLiGIPbxLncu4YYduB5mc+i+tedSaX4bSC626zK8sWwJtj+VifvEZAJA6djxWq2JZWm8U6jPLPJKIGae3a2fMQ+4WLkD/gRzmg+LNWP2r97GDdxrHOVjAL7QVDHHfDEfjWle2nhwyXCWlxDtj0/Ebs5/eTiQjPTqUAPoKhudO8MNNfPBqMqRJCj2qbtxdipzuJUYIIUY/2uvFMDFstUubCC5ghKGK4AEiuoPIzgj3GTWmPGetiaOX7SpeNEQEoDwjKw/VV/KqGmW2n3EF215dGKWNAYUzgOec84PTjjvnrWwmleFjLEra1NsaFCz7AMOWUNxg8AFjjr8vXmgDKv9cuL/TLPT2G2C2HHOWc4wCT6AcAdhmrEHi7V7f7P5c0Y+zgCImMZXCKg/HCL/kmo9S02ws9IsriG6d7u4AZoGx8gxyTjplunqM1cg0vw3IbHzdYkjDKDcjbkqfLVjt47MWHPXb15oAyrbWLyz1Z9UgdUu2LneF+6XBBIHY8nHpVybxXqlwYvMaErFKkqoIgBuUuQePeRj+NVrCCxHiOCC5nRrBbkK8xOAYwfvflzj8K24vD+hG2t7i51XyllnMblHXCj958wGCSBtT/vvt3AKVx4tvLnTzayQwEtcCdmweSGZsAdhudjj3rIv72XUtQnvZwnnTuZJNi4BY8k49zW7BpGgS2YvJL+5hieby0SXaHYZHPGeAN2T67fWrVjaeFwLdLi7iLQ3rksxYiaAPEMPwOxlIIwePpQBx9FdDdWeiJYam1peJI6tC1sZCQzKQS4244IOO5/rXPUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUlv8A8fMX++P51HUlv/x8xf74/nQB9NeLf+Rovv8AeX/0EUUeLf8AkaL7/eX/ANBFFYlHFfGPXE07xdqunvao4vtORBKvDg78jJ7qNvT1NeLV6f8AHn/kov8A26R/zavMK1WxLCiiimAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVJb/8fMX++P51HUlv/wAfMX++P50AfTXi3/kaL7/eX/0EUUeLf+Rovv8AeX/0EUViUeYfHn/kov8A26R/zavMK9P+PP8AyUX/ALdI/wCbV5hWq2JYUUUUwCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACpLf/j5i/3x/Oo6kt/+PmL/AHx/OgD6a8W/8jRff7y/+giijxb/AMjRff7y/wDoIorEo8w+PP8AyUX/ALdI/wCbV5hXp/x5/wCSi/8AbpH/ADavMK1WxLCiiimAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVJb/APHzF/vj+dR1Jb/8fMX++P50AfTXi3/kaL7/AHl/9BFFHi3/AJGi+/3l/wDQRRWJR5h8ef8Akov/AG6R/wA2rzCvT/jz/wAlF/7dI/5tXmFarYlhRRRTAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKkt/wDj5i/3x/Oo6kt/+PmL/fH86APprxb/AMjRff7y/wDoIoo8W/8AI0X3+8v/AKCKKxKPMPjz/wAlF/7dI/5tXmFen/Hn/kov/bpH/Nq8wrVbEsKKKKYBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABUlv/AMfMX++P51HUlv8A8fMX++P50AfTXi3/AJGi+/3l/wDQRRR4t/5Gi+/3l/8AQRRWJR5h8ef+Si/9ukf82rzCvT/jz/yUX/t0j/m1eYVqtiWFFFFMAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAqS3/wCPmL/fH86jqS3/AOPmL/fH86APprxb/wAjRff7y/8AoIoo8W/8jRff7y/+giisSjn/AImeALnxl4sbVLDXNEhhEKxbbi5IbIJz0UjHNcb/AMKU1b/oY/Dn/gW//wARRRVczCwf8KU1b/oY/Dn/AIFv/wDEUf8AClNW/wChj8Of+Bb/APxFFFHMxWD/AIUpq3/Qx+HP/At//iKP+FKat/0Mfhz/AMC3/wDiKKKOZhYP+FKat/0Mfhz/AMC3/wDiKP8AhSmrf9DH4c/8C3/+Iooo5mFg/wCFKat/0Mfhz/wLf/4ij/hSmrf9DH4c/wDAt/8A4iiijmYWD/hSmrf9DH4c/wDAt/8A4ij/AIUpq3/Qx+HP/At//iKKKOZhYP8AhSmrf9DH4c/8C3/+Io/4Upq3/Qx+HP8AwLf/AOIooo5mFg/4Upq3/Qx+HP8AwLf/AOIo/wCFKat/0Mfhz/wLf/4iiijmYWD/AIUpq3/Qx+HP/At//iKP+FKat/0Mfhz/AMC3/wDiKKKOZhYP+FKat/0Mfhz/AMC3/wDiKP8AhSmrf9DH4c/8C3/+Iooo5mFg/wCFKat/0Mfhz/wLf/4ij/hSmrf9DH4c/wDAt/8A4iiijmYWD/hSmrf9DH4c/wDAt/8A4ij/AIUpq3/Qx+HP/At//iKKKOZhYP8AhSmrf9DH4c/8C3/+Io/4Upq3/Qx+HP8AwLf/AOIooo5mFg/4Upq3/Qx+HP8AwLf/AOIo/wCFKat/0Mfhz/wLf/4iiijmYWD/AIUpq3/Qx+HP/At//iKP+FKat/0Mfhz/AMC3/wDiKKKOZhYP+FKat/0Mfhz/AMC3/wDiKP8AhSmrf9DH4c/8C3/+Iooo5mFg/wCFKat/0Mfhz/wLf/4ij/hSmrf9DH4c/wDAt/8A4iiijmYWD/hSmrf9DH4c/wDAt/8A4ij/AIUpq3/Qx+HP/At//iKKKOZhYP8AhSmrf9DH4c/8C3/+Io/4Upq3/Qx+HP8AwLf/AOIooo5mFg/4Upq3/Qx+HP8AwLf/AOIo/wCFKat/0Mfhz/wLf/4iiijmYWD/AIUpq3/Qx+HP/At//iKfF8FtVSZHPiLw5hWBP+lv/wDEUUUczCx6X4nuILjxHeSwzRyRsy7WVgQflFFFFSM//9k=)
После появляется посчитанная таблица (2), в которой, в графе tovar указана начальная матрица прибыли, а в графе Zij - minZijуказана матрица рисков. В нижней строчке под таблицей указан Max(Zij-minZij). На последней строчке показан ответ решения задачи.
Рисунок 2
![](data:image/jpeg;base64,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)
Заключение.
Внедрение компьютеров в данную область науки привело к ускорению и упрощению расчетов, что высоко цениться у работников, производивших ранее эти расчёты в ручную. Это не только экономия времени и сил, но и точность ответа, т.к. компьютер никогда не ошибается.
На рис. 2 представлен результат работы программы на TurboPascal. Данный результат совпал с аналитическим решениемзадачи.
Список литературы.
1. Андреев В.Н., Герасимов Ю.Ю. Принятие оптимальных решений: Теория и применение в лесном деле. Йоэнсуу: Из-во ун-та Йоэнсуу, 1999. 200 с.
2. Беллман Р., Калаба Р. Динамическое программирование и современная теория управления. М.: Наука, 1969. 120 с.
3. Вентцель Е.С. Элементы динамического программирования. М.: Наука, 1964. 176 с.
4. Вентцель Е.С. Исследование операций: задачи, принципы, методология. М.: Наука, 1988.
5. Юдин Д.Б. Задачи и методы стохастического программирования. М.: Сов. радио, 1979. 392 с.
6. Davis L.S., Johnson K.N. Forest management. New York: McGraw-Hill Book Company, 1987. 790 p.
7. Моисеев Н.Н., Математические методы системного анализа М. Наука 1981 487 с.