There are many variations of this problem, such as 2D packing, linear packing, packing by weight, packing by cost, and so on. They have many applications, such as filling up containers, loading trucks with weight capacity constraints, creating file backups in media and technology mapping in Field-programmable gate array semiconductor chip design.

The bin packing problem can also be seen as a special case of the cutting stock problem. When the number of bins is restricted to 1 and each item is characterised by both a volume and a value, the problem of maximising the value of items that can fit in the bin is known as the knapsack problem.

Here is source code of the C++ Program to Implement the Bin Packing Algorithm. The C++ program is successfully compiled and run on a Linux system. The program output is also shown below.

`#include<iostream>`

using namespace std;

void binPacking(int *a, int size, int n)

`{`

int binCount = 1;

int s = size;

for (int i = 0; i < n; i++)

`{`

if (s - *(a + i) > 0)

`{`

s -= *(a + i);

continue;

`}`

`else`

`{`

binCount++;

s = size;

i--;

`}`

`}`

cout << "Number of bins required: " << binCount;

`}`

int main(int argc, char **argv)

`{`

cout << "BIN - PACKING Algorithm\n";

cout << "Enter the number of items in Set: ";

int n;

cin >> n;

cout << "Enter " << n << " items:";

int a[n];

for (int i = 0; i < n; i++)

cin >> a[i];

cout << "Enter the bin size: ";

int size;

cin >> size;

binPacking(a, size, n);

`}`

Output:

$ g++ BinPacking.cpp $ a.out BIN - PACKING Algorithm Enter the number of items in Set: 5 Enter 5 items:12 23 34 45 56 Enter the bin size: 70 Number of bins required: 3

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