About RobPratt

RobPratt

The following documentation example shows how to approximately solve the problem: https://documentation.sas.com/doc/en/pgmsascdc/v_053/casmopt/casmopt_optmodel_examples05.htm The following article shows how to exactly solve the problem: https://communities.sas.com/t5/SAS-Communities-Library/Implementing-Column-Generation-using-SAS-Optimization/ta-p/593049 Are you able to share your data?

RobPratt

You might be interested in PROC OPTBINNING: SAS Help Center: OPTBINNING Procedure

RobPratt

Always glad to help!

RobPratt

Here's a more compact declaration that should help you reduce the include lists: set FORCED_PLANTS = {'1435750', '1441177', '1441192', '1441198'}; set KSET_i {FORCED_PLANTS}; KSET_i {'1435750'} = {'710','711','717','718','750','751','752','753','754','755','756','757','758','759','760','761','762','763','764','766','767','768','769','795','796'}; KSET_i {'1441177'} = {'678','679','739','790','791','792','793','794','797','881','882','884'}; KSET_i {'1441192'} = {'770','772','773','774','775','776','777','778'}; KSET_i {'1441198'} = {'765','779','780','781','782','783','784','785','786','787','788','789'}; con force_zip3_exe {i in FORCED_PLANTS, k in ZIP3:<i,k> in ARCS2}: Assign2[i,k] = (if k in KSET_i[i] then 1 else 0); Now you can include force_zip3_exe instead of including four separate constraint families.

RobPratt

Hi Dan, The part after INCLUDE should be a list of variables, objectives, and constraints rather than a problem name. I recommend the following: problem sol_m_tx from final_zip3_assign include force_zip3_dallas_exe force_zip3_amarillo_exe force_zip3_nhouston_exe force_zip3_sanantonio_exe; Please let me know if that doesn't resolve your issue. Thanks, Rob

RobPratt

Can you please share your data set?

RobPratt

For the code I suggested, the following suffices: num FTR {PROD_IDs}; read data SECOND_FTR_PRODS into [PROD_ID] FTR;

RobPratt

con Constraint1 {<PROD_ID, slot, day> in PROD_SLOT_DAY, <PROD_ID2, slot2, (day)> in PROD_SLOT_DAY: FTR[PROD_ID] = FTR[PROD_ID2] and slot2 = slot+1}: PROD_ASSIGN[PROD_ID, slot, day] + PROD_ASSIGN[PROD_ID2, slot2, day] <= 1; con Constraint2 {<PROD_ID, slot, day> in PROD_SLOT_DAY, <PROD_ID2, (slot), day2> in PROD_SLOT_DAY: FTR[PROD_ID] = FTR[PROD_ID2] and dayToNum[day2] = dayToNum[day]+1}: PROD_ASSIGN[PROD_ID, slot, day] + PROD_ASSIGN[PROD_ID2, slot, day2] <= 1;

RobPratt

I am not able to replicate the behavior you are seeing. Please attach the full log from a fresh session.

RobPratt

What does the following yield? proc print data=casuser.vrpdata(where=(ModelWeekNumber=3)); run;

RobPratt

From your log: ModelWeekNumber=3 NOTE: There were 1 rows read from table 'VRPDATA' in caslib 'CASUSER(SRHY-Exp)'. I recommend checking your vrpdata table, which seems to have only one observation with ModelWeekNumber=3.

RobPratt

The simplest way is to use the runOptmodel action with the groupBy parameter. The idea is to write the OPTMODEL code as if you are solving for only one BY group and then make the following modifications. Call PROC CAS instead of PROC OPTMODEL: /*proc optmodel;*/ proc cas noqueue; source pgm; put _BY_LINE_; Use NodeID instead of _N_ as the key column (always a good idea to avoid _N_ with CAS because the data order is unpredictable): read data vrpdata into NODES =[NodeID] ModelNode ModelLat ModelLong Volume Mode; After the end of your OPTMODEL code, call the runOptmodel action: /*quit;*/ endsource; action optimization.runOptmodel / code=pgm groupBy='ModelWeekNumber'; quit; The resulting solution_data CAS table will automatically include any BY variables, so you can use a BY statement in the PROC SGPLOT call to plot one BY group at a time: proc sgplot data=casuser.solution_data noautolegend; by ModelWeekNumber; scatter x=x1 y=y1 / datalabel=i; vector x=x2 y=y2 / xorigin=x1 yorigin=y1 group=k noarrowheads; xaxis display=none; yaxis display=none; run; It is worth noting that you can optionally get a summary table with one row per BY group as follows: action optimization.runOptmodel / code=pgm groupBy='ModelWeekNumber' statusOut='statusOut'; The resulting statusOut table contains information about the solver call for each BY group.

RobPratt

Glad to help. Yes, your understanding is correct (assuming N=6). You can also use the EXPAND statement to help verify what is happening. For example: expand DesiredConstraint1; Here are the first several lines of the resulting output: Constraint DesiredConstraint1[AAA,9,DAY_1,11,DAY_2]: PROD_ASSIGN[AAA,9,DAY_1] + PROD_ASSIGN[AAA,11,DAY_2] <= 1 Constraint DesiredConstraint1[AAA,9,DAY_1,11,DAY_3]: PROD_ASSIGN[AAA,9,DAY_1] + PROD_ASSIGN[AAA,11,DAY_3] <= 1 Constraint DesiredConstraint1[AAA,9,DAY_1,12,DAY_2]: PROD_ASSIGN[AAA,9,DAY_1] + PROD_ASSIGN[AAA,12,DAY_2] <= 1 Constraint DesiredConstraint1[AAA,9,DAY_1,12,DAY_3]: PROD_ASSIGN[AAA,9,DAY_1] + PROD_ASSIGN[AAA,12,DAY_3] <= 1 Constraint DesiredConstraint1[AAA,9,DAY_1,9,DAY_3]: PROD_ASSIGN[AAA,9,DAY_1] + PROD_ASSIGN[AAA,9,DAY_3] <= 1 Constraint DesiredConstraint1[AAA,9,DAY_1,9,DAY_2]: PROD_ASSIGN[AAA,9,DAY_1] + PROD_ASSIGN[AAA,9,DAY_2] <= 1 Constraint DesiredConstraint1[AAA,9,DAY_1,10,DAY_3]: PROD_ASSIGN[AAA,9,DAY_1] + PROD_ASSIGN[AAA,10,DAY_3] <= 1 Constraint DesiredConstraint1[AAA,9,DAY_1,10,DAY_2]: PROD_ASSIGN[AAA,9,DAY_1] + PROD_ASSIGN[AAA,10,DAY_2] <= 1

RobPratt

create data want from [item] x UseItem;

RobPratt

Here's one way to enforce your first desired constraint: num dayToNum {DayNumbers}; num count init 0; for {d in DayNumbers} do; count = count + 1; dayToNum[d] = count; end; print dayToNum; num N = 2; con DesiredConstraint1 {<PROD_ID, slot, day> in PROD_SLOT_DAY, <(PROD_ID), slot2, day2> in PROD_SLOT_DAY: dayToNum[day2] in dayToNum[day]+1..dayToNum[day]+N}: PROD_ASSIGN[PROD_ID, slot, day] + PROD_ASSIGN[PROD_ID, slot2, day2] <= 1; But notice that this conflicts with your fixed assignments of AAA to days 6, 7, and 8, so the resulting problem is infeasible. For your second desired constraint, you can use multiple objectives as follows: max NumPreferences = sum {<PROD_ID, slot, day> in PREF_PROD_SHOWCD inter PROD_SLOT_DAY} PROD_ASSIGN[PROD_ID, slot, day]; solve obj NumPreferences; con ObjectiveCut: NumPreferences >= NumPreferences.sol; solve obj TotalPreferenceWeight with milp / primalin; The idea is to first maximize the number of preferences that can be satisfied, then impose a constraint that enforces that maximum, and then maximize your original objective function. I have used the PRIMALIN option to warm start the second solve with the solution from the first solve.

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