def solve_hidato(puzzle, index): """Solve the given hidato table.""" # Create the model. model = cp_model.CpModel() r = len(puzzle) c = len(puzzle[0]) if not visualization.RunFromIPython(): print('') print('----- Solving problem %i -----' % index) print('') print(('Initial game (%i x %i)' % (r, c))) print_matrix(puzzle) # # declare variables # positions = [ model.NewIntVar(0, r * c - 1, 'p[%i]' % i) for i in range(r * c) ] # # constraints # model.AddAllDifferent(positions) # # Fill in the clues # for i in range(r): for j in range(c): if puzzle[i][j] > 0: model.Add(positions[puzzle[i][j] - 1] == i * c + j) # Consecutive numbers much touch each other in the grid. # We use an allowed assignment constraint to model it. close_tuples = build_pairs(r, c) for k in range(0, r * c - 1): model.AddAllowedAssignments([positions[k], positions[k + 1]], close_tuples) # # solution and search # solver = cp_model.CpSolver() status = solver.Solve(model) if status == cp_model.OPTIMAL: if visualization.RunFromIPython(): output = visualization.SvgWrapper(10, r, 40.0) for i, var in enumerate(positions): val = solver.Value(var) x = val % c y = val // c color = 'white' if puzzle[y][x] == 0 else 'lightgreen' output.AddRectangle(x, r - y - 1, 1, 1, color, 'black', str(i + 1)) output.AddTitle('Puzzle %i solved in %f s' % (index, solver.WallTime())) output.Display() else: print_solution( [solver.Value(x) for x in positions], r, c, ) print('Statistics') print(' - conflicts : %i' % solver.NumConflicts()) print(' - branches : %i' % solver.NumBranches()) print(' - wall time : %f s' % solver.WallTime())
def main(): # Creates the solver. model = cp_model.CpModel() machines_count = 6 jobs_count = 6 all_machines = range(0, machines_count) all_jobs = range(0, jobs_count) durations = [[1, 3, 6, 7, 3, 6], [8, 5, 10, 10, 10, 4], [5, 4, 8, 9, 1, 7], [5, 5, 5, 3, 8, 9], [9, 3, 5, 4, 3, 1], [3, 3, 9, 10, 4, 1]] machines = [[2, 0, 1, 3, 5, 4], [1, 2, 4, 5, 0, 3], [2, 3, 5, 0, 1, 4], [1, 0, 2, 3, 4, 5], [2, 1, 4, 5, 0, 3], [1, 3, 5, 0, 4, 2]] # Computes horizon dynamically. horizon = sum([sum(durations[i]) for i in all_jobs]) # Creates jobs. all_tasks = {} for i in all_jobs: for j in all_machines: start = model.NewIntVar(0, horizon, 'start_%i_%i' % (i, j)) duration = durations[i][j] end = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j)) interval = model.NewIntervalVar(start, duration, end, 'interval_%i_%i' % (i, j)) all_tasks[(i, j)] = (start, end, interval) # Create disjuctive constraints. machine_to_jobs = {} for i in all_machines: machines_jobs = [] for j in all_jobs: for k in all_machines: if machines[j][k] == i: machines_jobs.append(all_tasks[(j, k)][2]) machine_to_jobs[i] = machines_jobs model.AddNoOverlap(machines_jobs) # Precedences inside a job. for i in all_jobs: for j in range(0, machines_count - 1): model.Add(all_tasks[(i, j + 1)][0] >= all_tasks[(i, j)][1]) # Makespan objective. obj_var = model.NewIntVar(0, horizon, 'makespan') model.AddMaxEquality( obj_var, [all_tasks[(i, machines_count - 1)][1] for i in all_jobs]) model.Minimize(obj_var) # Solve model. solver = cp_model.CpSolver() response = solver.Solve(model) # Output solution. if visualization.RunFromIPython(): starts = [[solver.Value(all_tasks[(i, j)][0]) for j in all_machines] for i in all_jobs] visualization.DisplayJobshop(starts, durations, machines, 'FT06') else: print('Optimal makespan: %i' % solver.ObjectiveValue())
def main(): """Solves the gate scheduling problem.""" model = cp_model.CpModel() jobs = [[3, 3], [2, 5], [1, 3], [3, 7], [7, 3], [2, 2], [2, 2], [5, 5], [10, 2], [4, 3], [2, 6], [1, 2], [6, 8], [4, 5], [3, 7]] max_length = 10 horizon = sum(t[0] for t in jobs) num_jobs = len(jobs) all_jobs = range(num_jobs) intervals = [] intervals0 = [] intervals1 = [] performed = [] starts = [] ends = [] demands = [] for i in all_jobs: # Create main interval. start = model.NewIntVar(0, horizon, 'start_%i' % i) duration = jobs[i][0] end = model.NewIntVar(0, horizon, 'end_%i' % i) interval = model.NewIntervalVar(start, duration, end, 'interval_%i' % i) starts.append(start) intervals.append(interval) ends.append(end) demands.append(jobs[i][1]) performed_on_m0 = model.NewBoolVar('perform_%i_on_m0' % i) performed.append(performed_on_m0) # Create an optional copy of interval to be executed on machine 0. start0 = model.NewIntVar(0, horizon, 'start_%i_on_m0' % i) end0 = model.NewIntVar(0, horizon, 'end_%i_on_m0' % i) interval0 = model.NewOptionalIntervalVar(start0, duration, end0, performed_on_m0, 'interval_%i_on_m0' % i) intervals0.append(interval0) # Create an optional copy of interval to be executed on machine 1. start1 = model.NewIntVar(0, horizon, 'start_%i_on_m1' % i) end1 = model.NewIntVar(0, horizon, 'end_%i_on_m1' % i) interval1 = model.NewOptionalIntervalVar(start1, duration, end1, performed_on_m0.Not(), 'interval_%i_on_m1' % i) intervals1.append(interval1) # We only propagate the constraint if the tasks is performed on the machine. model.Add(start0 == start).OnlyEnforceIf(performed_on_m0) model.Add(start1 == start).OnlyEnforceIf(performed_on_m0.Not()) # Max Length constraint (modeled as a cumulative) model.AddCumulative(intervals, demands, max_length) # Choose which machine to perform the jobs on. model.AddNoOverlap(intervals0) model.AddNoOverlap(intervals1) # Objective variable. makespan = model.NewIntVar(0, horizon, 'makespan') model.AddMaxEquality(makespan, ends) model.Minimize(makespan) # Symmetry breaking. model.Add(performed[0] == 0) # Solve model. solver = cp_model.CpSolver() solver.Solve(model) # Output solution. if visualization.RunFromIPython(): output = visualization.SvgWrapper(solver.ObjectiveValue(), max_length, 40.0) output.AddTitle('Makespan = %i' % solver.ObjectiveValue()) color_manager = visualization.ColorManager() color_manager.SeedRandomColor(0) for i in all_jobs: performed_machine = 1 - solver.Value(performed[i]) start = solver.Value(starts[i]) d_x = jobs[i][0] d_y = jobs[i][1] s_y = performed_machine * (max_length - d_y) output.AddRectangle(start, s_y, d_x, d_y, color_manager.RandomColor(), 'black', 'j%i' % i) output.AddXScale() output.AddYScale() output.Display() else: print('Solution') print(' - makespan = %i' % solver.ObjectiveValue()) for i in all_jobs: performed_machine = 1 - solver.Value(performed[i]) start = solver.Value(starts[i]) print(' - Job %i starts at %i on machine %i' % (i, start, performed_machine)) print('Statistics') print(' - conflicts : %i' % solver.NumConflicts()) print(' - branches : %i' % solver.NumBranches()) print(' - wall time : %f ms' % solver.WallTime())
def jobshop_ft06(): """Solves the ft06 jobshop.""" # Creates the solver. model = cp_model.CpModel() machines_count = 6 jobs_count = 6 all_machines = range(0, machines_count) all_jobs = range(0, jobs_count) durations = [[1, 3, 6, 7, 3, 6], [8, 5, 10, 10, 10, 4], [5, 4, 8, 9, 1, 7], [5, 5, 5, 3, 8, 9], [9, 3, 5, 4, 3, 1], [3, 3, 9, 10, 4, 1]] machines = [[2, 0, 1, 3, 5, 4], [1, 2, 4, 5, 0, 3], [2, 3, 5, 0, 1, 4], [1, 0, 2, 3, 4, 5], [2, 1, 4, 5, 0, 3], [1, 3, 5, 0, 4, 2]] # Computes horizon dynamically. horizon = sum([sum(durations[i]) for i in all_jobs]) task_type = collections.namedtuple('task_type', 'start end interval') # Creates jobs. all_tasks = {} for i in all_jobs: for j in all_machines: start_var = model.NewIntVar(0, horizon, 'start_%i_%i' % (i, j)) duration = durations[i][j] end_var = model.NewIntVar(0, horizon, 'end_%i_%i' % (i, j)) interval_var = model.NewIntervalVar(start_var, duration, end_var, 'interval_%i_%i' % (i, j)) all_tasks[(i, j)] = task_type(start=start_var, end=end_var, interval=interval_var) # Create disjuctive constraints. machine_to_jobs = {} for i in all_machines: machines_jobs = [] for j in all_jobs: for k in all_machines: if machines[j][k] == i: machines_jobs.append(all_tasks[(j, k)].interval) machine_to_jobs[i] = machines_jobs model.AddNoOverlap(machines_jobs) # Precedences inside a job. for i in all_jobs: for j in range(0, machines_count - 1): model.Add(all_tasks[(i, j + 1)].start >= all_tasks[(i, j)].end) # Makespan objective. obj_var = model.NewIntVar(0, horizon, 'makespan') model.AddMaxEquality( obj_var, [all_tasks[(i, machines_count - 1)].end for i in all_jobs]) model.Minimize(obj_var) # Solve model. solver = cp_model.CpSolver() solver.parameters.log_search_progress = True status = solver.Solve(model) # Output solution. if status == cp_model.OPTIMAL: if visualization.RunFromIPython(): starts = [[ solver.Value(all_tasks[(i, j)][0]) for j in all_machines ] for i in all_jobs] visualization.DisplayJobshop(starts, durations, machines, 'FT06') else: print('Optimal makespan: %i' % solver.ObjectiveValue())
model.AddNoOverlap(intervals1) # Objective variable. makespan = model.NewIntVar(0, horizon, 'makespan') model.AddMaxEquality(makespan, ends) model.Minimize(makespan) # Symmetry breaking. model.Add(performed[0] == 0) # Solve model. solver = cp_model.CpSolver() solver.Solve(model) # Output solution. if visualization.RunFromIPython(): output = visualization.SvgWrapper(solver.ObjectiveValue(), max_length, 40.0) output.AddTitle('Makespan = %i' % solver.ObjectiveValue()) color_manager = visualization.ColorManager() color_manager.SeedRandomColor(0) for i in all_jobs: performed_machine = 1 - solver.Value(performed[i]) start = solver.Value(starts[i]) dx = jobs[i][0] dy = jobs[i][1] sy = performed_machine * (max_length - dy) output.AddRectangle(start, sy, dx, dy, color_manager.RandomColor(), 'black', 'j%i' % i)