Google Optimization tools for machining workshop task Planning "Python Edition"

In the previous article, "Using. NET core and Google Optimization tools to achieve workshop task planning", this time the official Google document Python implementation of the version of the full source to meet the love of Python friends.

 from __future__ Importprint_function#Import Python wrapper for Or-tools constraint solver. fromOrtools.constraint_solverImportPYWRAPCPdefMain ():#Create the solver.Solver = PYWRAPCP. Solver ('Jobshop') Machines_count= 3Jobs_count= 3All_machines=Range (0, machines_count) all_jobs=Range (0, Jobs_count)#Define data.machines = [[0, 1, 2], [0,2, 1],              [1, 2]] Processing_times= [[3, 2, 2],                      [2, 1, 4],                      [4, 3]]  #computes horizon.Horizon =0 forIinchAll_jobs:horizon+=sum (processing_times[i])#creates jobs.All_tasks = {}   forIinchAll_jobs: forJinchRange (0, Len (machines[i)): all_tasks[(I, J)]=Solver.                                                          Fixeddurationintervalvar (0, Horizon,                                                          PROCESSING_TIMES[I][J], False, 'job_%i_%i'%(i, j))#creates sequence variables and add disjunctive constraints.All_sequences =[] All_machines_jobs= []   forIinchAll_machines:machines_jobs= []     forJinchAll_jobs: forKinchRange (0, Len (machines[j)):ifMachines[j][k] = =I:machines_jobs.append (All_tasks[(J, K)]) DISJ= Solver. Disjunctiveconstraint (Machines_jobs,'Machine %i'%i) all_sequences.append (DISJ. Sequencevar ()) Solver. ADD (DISJ)#Add conjunctive contraints.   forIinchAll_jobs: forJinchRange (0, Len (machines[i))-1): Solver. ADD (all_tasks[(i, J+ 1)]. Startsafterend (all_tasks[(i, J)]))#Set the objective.Obj_var = Solver. Max ([All_tasks[(i, Len (Machines[i])-1)]. ENDEXPR () forIinchAll_jobs]) Objective_monitor= Solver. Minimize (Obj_var, 1)  #Create search phases.Sequence_phase = Solver. Phase ([All_sequences[i] forIinchAll_machines], solver. Sequence_default) Vars_phase=Solver. Phase ([Obj_var], Solver. Choose_first_unbound, Solver. Assign_min_value) Main_phase=Solver.compose ([Sequence_phase, vars_phase])#Create the solution collector.Collector =Solver. Lastsolutioncollector ()#ADD The interesting variables to the solutioncollector.Collector. ADD (all_sequences) collector. Addobjective (Obj_var) forIinchall_machines:sequence=All_sequences[i]; Sequence_count=sequence.    Size ();  forJinchRange (0, sequence_count): t=sequence. Interval (j) collector. ADD (t.startexpr (). Var ()) collector. ADD (t.endexpr (). Var ())#Solve the problem.Disp_col_width = 10ifSolver. Solve (Main_phase, [Objective_monitor, Collector]):Print("\noptimal Schedule Length:", collector. Objectivevalue (0),"\ n") Sol_line=""Sol_line_tasks=""    Print("Optimal Schedule","\ n")     forIinchAll_machines:seq=All_sequences[i] Sol_line+=" Machine"+ STR (i) +": "Sol_line_tasks+=" Machine"+ STR (i) +": "sequence=Collector. Forwardsequence (0, seq) seq_size=len (Sequence) forJinchRange (0, seq_size): t=seq.         Interval (Sequence[j]); #Add spaces to output to align columns.Sol_line_tasks + = T.name () +" "* (Disp_col_width-Len (T.name ())) forJinchRange (0, seq_size): t=seq.        Interval (Sequence[j]); Sol_tmp="["+ str (collector. Value (0, t.startexpr (). Var ())) +","sol_tmp+ = str (collector. Value (0, t.endexpr (). Var ())) +"] "        #Add spaces to output to align columns.Sol_line + = sol_tmp +" "* (Disp_col_width-Len (sol_tmp)) Sol_line+="\ n"Sol_line_tasks+="\ n"    Print(sol_line_tasks)Print("Time intervals for tasks\n")    Print(Sol_line)if __name__=='__main__': Main ()

Google Optimization tools for machining workshop task Planning "Python Edition"