The Decomposition Algorithm

This formulation clearly has no decomposable structure. However, you can use a common modeling technique known as Lagrangian decomposition to bring the model into block-angular form. Lagrangian decomposition works by first partitioning the constraints into blocks. Then, each original variable is split into multiple copies of itself, one copy for each block in which the variable has a nonzero coefficient in the constraint matrix. Constraints are added to enforce the equality of each copy of the original variable. Then, you can write the original constraints in block-angular form by using the duplicate variables.

To apply Lagrangian decomposition to the resource allocation problem, define a set B of blocks and let define the set of start times for a given block b, such that . Given this partition of start times, let define the set of blocks in which task is scheduled to be running. Now, for each task , define duplicate variables for each . Let define the minimum block index for each class of variable that represents task i. You can now model the problem in block-angular form as follows:

In this formulation, the LinkDupVarsCon constraints ensure that the duplicate variables are equal to the original variables. Now, the five-task example has been transformed from a staircase structure to a block-angular structure:

To see how to apply Lagrangian decomposition in PROC OPTMODEL, consider the data set TaskData from Caprara, Furini, and Malaguti (2010), which consists of 2,916 tasks:

data TaskData;
   input profit weight start end;
   datalines;
99 92 1 9 
56 30 1 3 
39 73 1 20 
86 76 1 9 
...
24 94 768 769 
95 40 768 769 
66 17 768 769 
18 48 768 769 
97 23 768 769 
;

The following PROC OPTMODEL statements read in the data and solve the original staircase formulation by calling the MILP solver directly:

%macro SetupData(task_data=, capacity=);
   set TASKS;
   num capacity=&capacity;
   num profit{TASKS}, weight{TASKS}, start{TASKS}, end{TASKS};

   read data &task_data into TASKS=[_n_] profit weight start end;
   /* the set of start times */

   set STARTS = setof{i in TASKS} start[i];
   /* the set of tasks i that are active at a given start time s */
   set TASKS_START{s in STARTS}
      = {i in TASKS: start[i] <= s < end[i]};
%mend SetupData;

%macro ResourceAllocation_Direct(task_data=, capacity=);
   proc optmodel;
      %SetupData(task_data=&task_data,capacity=&capacity);

      /* select task i to come online from period [start to end) */
      var x{TASKS} binary;

      /* maximize the total profit of running tasks */
      max TotalProfit = sum{i in TASKS} profit[i] * x[i];

      /* enforce that the shared resource capacity is not exceeded */
      con CapacityCon{s in STARTS}:
         sum{i in TASKS_START[s]} weight[i] * x[i] <= capacity;

      solve with milp / maxtime=200 logfreq=10000;
   quit;
%mend ResourceAllocation_Direct;

%ResourceAllocation_Direct(task_data=TaskData, capacity=100);

The problem summary and solution summary are displayed in Output 15.7.1.

The iteration log, which contains the problem statistics, the progress of the solution, and the best solution found, is shown in Output 15.7.2.

To transform this data into block-angular form, first sort the task data to help reduce the number of duplicate variables that are needed in the reformulation as follows:

proc sort data=TaskData;
   by start end;
run;

Then, create the partition of constraints into blocks of size block_size as follows:

%macro ResourceAllocation_Decomp(task_data=, capacity=, block_size=);
   proc optmodel;
      %SetupData(task_data=&task_data,capacity=&capacity);
      /* partition into blocks of size blocks_size */
      num block_size = &block_size;
      num num_blocks = ceil( card(TASKS) / block_size );
      set BLOCKS     = 1..num_blocks;

      /* the set of starts s for which task i is active */
      set STARTS_TASK{i in TASKS} = {s in STARTS: start[i] <= s < end[i]};

      /* partition the start times into blocks of size block_size */
      set STARTS_BLOCK{BLOCKS} init {};
      num block_id    init 1;
      num block_count init 0;
      for{s in STARTS} do;
         STARTS_BLOCK[block_id] = STARTS_BLOCK[block_id] union {s};
         block_count = block_count + 1;
         if(mod(block_count, block_size) = 0) then
             block_id = block_id + 1;
      end;

Then, use the following PROC OPTMODEL statements to define the block-angular formulation and solve the problem by using the decomposition algorithm, the PRESOLVER=BASIC option, and block_size=20. Because this reformulation is equivalent to the original staircase formulation, disabling some of the advanced presolver techniques ensures that the model maintains block-angularity.

      /* blocks in which task i is online */
      set BLOCKS_TASK{i in TASKS} =
         {b in BLOCKS: card(STARTS_BLOCK[b] inter STARTS_TASK[i]) > 0};

      /* minimum block id in which task i is online */
      num min_block{i in TASKS} = min{b in BLOCKS_TASK[i]} b;

      /* select task i to come online from period [start to end)
         in each block */
      var x{i in TASKS, b in BLOCKS_TASK[i]} binary;

      /* maximize the total profit of running tasks */
      max TotalProfit = sum{i in TASKS} profit[i] * x[i,min_block[i]];

      /* enforce that task selection is consistent across blocks */
      con LinkDupVarsCon{i in TASKS, b in BLOCKS_TASK[i] diff {min_block[i]}}:
         x[i,b] = x[i,min_block[i]];

      /* enforce that the shared resource capacity is not exceeded */
      con CapacityCon{b in BLOCKS, s in STARTS_BLOCK[b]}:
         sum{i in TASKS_START[s]} weight[i] * x[i,b] <= capacity;

      /* define blocks for decomposition algorithm */
      for{b in BLOCKS, s in STARTS_BLOCK[b]} CapacityCon[b,s].block = b;

      solve with milp / presolver=basic decomp;
   quit;
%mend ResourceAllocation_Decomp;

%ResourceAllocation_Decomp(task_data=TaskData, capacity=100, block_size=20);

The problem summary and solution summary are displayed in Output 15.7.3. Compared to the original formulation, the numbers of variables and constraints are increased by the number of duplicate variables.

The iteration log, which contains the problem statistics, the progress of the solution, and the optimal objective value, is shown in Output 15.7.4.

The reformulation of this resource allocation problem provides a nice example of the potential trade-offs in modeling a problem for use with the decomposition algorithm. As seen in Example 15.2, the strength of the bound is an important factor in the overall performance of the algorithm, but it is not always correlated to the magnitude of the subproblem coverage. In the current example, the block size determines the number of blocks. Moreover, it determines the number of linking variables that are needed in the reformulation. At one extreme, if the block size is set to be , then the number of blocks is 1, and the number of copies of original variables is 0. Using one block would be equivalent to the original staircase formulation and would not yield a model conducive to decomposition. As the number of blocks is increased, the number of linking variables increases (the size of the master problem), the strength of the decomposition bound decreases, and the difficulty of solving the subproblems decreases. In addition, as the number of blocks and their relative difficulty change, the efficient utilization of your machine’s parallel architecture can be affected.

The previous section used a block size of 20. The following statement calls the decomposition algorithm and uses a block size of 80:

%ResourceAllocation_Decomp(task_data=TaskData, capacity=100, block_size=80);

The solution summary is displayed in Output 15.7.5.

The iteration log, which contains the problem statistics, the progress of the solution, and the optimal objective value, is shown in Output 15.7.6.

This version of the model provides a stronger initial bound and solves to optimality in the root node.