examples/bpp.mod
author Alpar Juttner <alpar@cs.elte.hu>
Mon, 06 Dec 2010 13:09:21 +0100
changeset 1 c445c931472f
permissions -rw-r--r--
Import glpk-4.45

- Generated files and doc/notes are removed
     1 /* BPP, Bin Packing Problem */
     2 
     3 /* Written in GNU MathProg by Andrew Makhorin <mao@gnu.org> */
     4 
     5 /* Given a set of items I = {1,...,m} with weight w[i] > 0, the Bin
     6    Packing Problem (BPP) is to pack the items into bins of capacity c
     7    in such a way that the number of bins used is minimal. */
     8 
     9 param m, integer, > 0;
    10 /* number of items */
    11 
    12 set I := 1..m;
    13 /* set of items */
    14 
    15 param w{i in 1..m}, > 0;
    16 /* w[i] is weight of item i */
    17 
    18 param c, > 0;
    19 /* bin capacity */
    20 
    21 /* We need to estimate an upper bound of the number of bins sufficient
    22    to contain all items. The number of items m can be used, however, it
    23    is not a good idea. To obtain a more suitable estimation an easy
    24    heuristic is used: we put items into a bin while it is possible, and
    25    if the bin is full, we use another bin. The number of bins used in
    26    this way gives us a more appropriate estimation. */
    27 
    28 param z{i in I, j in 1..m} :=
    29 /* z[i,j] = 1 if item i is in bin j, otherwise z[i,j] = 0 */
    30 
    31    if i = 1 and j = 1 then 1
    32    /* put item 1 into bin 1 */
    33 
    34    else if exists{jj in 1..j-1} z[i,jj] then 0
    35    /* if item i is already in some bin, do not put it into bin j */
    36 
    37    else if sum{ii in 1..i-1} w[ii] * z[ii,j] + w[i] > c then 0
    38    /* if item i does not fit into bin j, do not put it into bin j */
    39 
    40    else 1;
    41    /* otherwise put item i into bin j */
    42 
    43 check{i in I}: sum{j in 1..m} z[i,j] = 1;
    44 /* each item must be exactly in one bin */
    45 
    46 check{j in 1..m}: sum{i in I} w[i] * z[i,j] <= c;
    47 /* no bin must be overflowed */
    48 
    49 param n := sum{j in 1..m} if exists{i in I} z[i,j] then 1;
    50 /* determine the number of bins used by the heuristic; obviously it is
    51    an upper bound of the optimal solution */
    52 
    53 display n;
    54 
    55 set J := 1..n;
    56 /* set of bins */
    57 
    58 var x{i in I, j in J}, binary;
    59 /* x[i,j] = 1 means item i is in bin j */
    60 
    61 var used{j in J}, binary;
    62 /* used[j] = 1 means bin j contains at least one item */
    63 
    64 s.t. one{i in I}: sum{j in J} x[i,j] = 1;
    65 /* each item must be exactly in one bin */
    66 
    67 s.t. lim{j in J}: sum{i in I} w[i] * x[i,j] <= c * used[j];
    68 /* if bin j is used, it must not be overflowed */
    69 
    70 minimize obj: sum{j in J} used[j];
    71 /* objective is to minimize the number of bins used */
    72 
    73 data;
    74 
    75 /* The optimal solution is 3 bins */
    76 
    77 param m := 6;
    78 
    79 param w := 1 50, 2 60, 3 30, 4 70, 5 50, 6 40;
    80 
    81 param c := 100;
    82 
    83 end;