Gurddy MCP Server

""" Scheduling Problem using Gurddy CSP Solver Schedule tasks with constraints on time slots, resources, and dependencies. """ import gurddy def solve_course_scheduling(): """Solve a university course scheduling problem.""" model = gurddy.Model("CourseScheduling", "CSP") # Courses to schedule courses = ['Math101', 'Physics101', 'Chemistry101', 'Biology101', 'English101'] # Time slots (0=Mon9am, 1=Mon10am, 2=Mon11am, 3=Tue9am, etc.) # 5 days × 4 time slots = 20 total slots time_slots = list(range(20)) # Variables: each course gets assigned to a time slot course_vars = {} for course in courses: course_vars[course] = model.addVar(course, domain=time_slots) # Constraint 1: No two courses at the same time model.addConstraint(gurddy.AllDifferentConstraint(list(course_vars.values()))) # Constraint 2: Some courses cannot be on the same day # Math and Physics should not be on the same day (too intensive) def not_same_day(slot1, slot2): day1 = slot1 // 4 # 4 slots per day day2 = slot2 // 4 return day1 != day2 model.addConstraint(gurddy.FunctionConstraint( not_same_day, (course_vars['Math101'], course_vars['Physics101']) )) # Constraint 3: Chemistry should be in the morning (slots 0,1 of each day) def is_morning_slot(slot): return (slot % 4) < 2 # first 2 slots of each day # Convert to binary constraint by checking if Chemistry is in morning def chemistry_morning_constraint(chem_slot, dummy_slot): return is_morning_slot(chem_slot) # Use a dummy variable for the constraint dummy_var = model.addVar("dummy", domain=[0]) model.addConstraint(gurddy.FunctionConstraint( chemistry_morning_constraint, (course_vars['Chemistry101'], dummy_var) )) solution = model.solve() return solution, courses, time_slots def solve_meeting_scheduling(): """Solve a meeting room scheduling problem.""" model = gurddy.Model("MeetingScheduling", "CSP") # Meetings to schedule meetings = ['TeamA', 'TeamB', 'TeamC', 'BoardMeeting', 'ClientCall'] # Time slots: 8 slots per day (9am-5pm), 2 days time_slots = list(range(16)) # Variables meeting_vars = {} for meeting in meetings: meeting_vars[meeting] = model.addVar(meeting, domain=time_slots) # Constraint 1: No overlapping meetings model.addConstraint(gurddy.AllDifferentConstraint(list(meeting_vars.values()))) # Constraint 2: Board meeting must be in the afternoon (slots 4-7 each day) def is_afternoon(slot): return (slot % 8) >= 4 def board_afternoon_constraint(board_slot, dummy_slot): return is_afternoon(board_slot) dummy_var = model.addVar("dummy", domain=[0]) model.addConstraint(gurddy.FunctionConstraint( board_afternoon_constraint, (meeting_vars['BoardMeeting'], dummy_var) )) # Constraint 3: TeamA and TeamB should not be on the same day def different_days(slot1, slot2): day1 = slot1 // 8 day2 = slot2 // 8 return day1 != day2 model.addConstraint(gurddy.FunctionConstraint( different_days, (meeting_vars['TeamA'], meeting_vars['TeamB']) )) solution = model.solve() return solution, meetings, time_slots def print_schedule(solution, items, time_slots, schedule_type="Schedule"): """Print the scheduling solution.""" if not solution: print(f"No solution found for {schedule_type}!") return print(f"\n{schedule_type} Solution:") print("=" * 50) # Create schedule grid if len(time_slots) == 20: # Course scheduling (5 days × 4 slots) days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'] slots_per_day = 4 slot_names = ['9:00 AM', '10:00 AM', '11:00 AM', '12:00 PM'] else: # Meeting scheduling (2 days × 8 slots) days = ['Day 1', 'Day 2'] slots_per_day = 8 slot_names = ['9:00 AM', '10:00 AM', '11:00 AM', '12:00 PM', '1:00 PM', '2:00 PM', '3:00 PM', '4:00 PM'] # Create reverse mapping: slot -> item slot_to_item = {} for item in items: if item in solution: # Skip dummy variables slot = solution[item] slot_to_item[slot] = item # Print schedule for day_idx, day in enumerate(days): print(f"\n{day}:") print("-" * 20) for slot_idx in range(slots_per_day): absolute_slot = day_idx * slots_per_day + slot_idx time = slot_names[slot_idx] item = slot_to_item.get(absolute_slot, "Free") print(f"{time:>10}: {item}") # Print summary print(f"\nScheduled items:") for item in sorted(items): if item in solution: slot = solution[item] day_idx = slot // slots_per_day slot_idx = slot % slots_per_day day_name = days[day_idx] time_name = slot_names[slot_idx] print(f"{item:>15}: {day_name} {time_name}") def solve_resource_scheduling(): """Solve a resource allocation scheduling problem.""" model = gurddy.Model("ResourceScheduling", "CSP") # Tasks that need resources tasks = ['TaskA', 'TaskB', 'TaskC', 'TaskD'] # Resources available (0=Resource1, 1=Resource2, 2=Resource3) resources = list(range(3)) # Time slots (0-7 for 8 time periods) time_slots = list(range(8)) # Variables: each task gets assigned a resource and time slot # We'll encode this as: value = resource * 8 + time_slot # So domain is [0-23] representing all resource-time combinations task_vars = {} domain = [] for resource in resources: for time_slot in time_slots: domain.append(resource * 8 + time_slot) for task in tasks: task_vars[task] = model.addVar(task, domain=domain) # Constraint 1: No two tasks can use the same resource at the same time def no_resource_conflict(assignment1, assignment2): return assignment1 != assignment2 model.addConstraint(gurddy.AllDifferentConstraint(list(task_vars.values()))) # Constraint 2: TaskA must use Resource1 (resource 0) def task_a_resource1(assignment, dummy): resource = assignment // 8 return resource == 0 dummy_var = model.addVar("dummy", domain=[0]) model.addConstraint(gurddy.FunctionConstraint( task_a_resource1, (task_vars['TaskA'], dummy_var) )) solution = model.solve() return solution, tasks, resources, time_slots def print_resource_schedule(solution, tasks, resources, time_slots): """Print the resource scheduling solution.""" if not solution: print("No solution found for Resource Scheduling!") return print("\nResource Scheduling Solution:") print("=" * 50) # Decode assignments assignments = {} for task in tasks: if task in solution: encoded = solution[task] resource = encoded // 8 time_slot = encoded % 8 assignments[task] = (resource, time_slot) # Print by task print("Task Assignments:") for task in sorted(tasks): if task in assignments: resource, time_slot = assignments[task] print(f"{task:>8}: Resource{resource+1} at Time{time_slot+1}") # Print resource utilization print(f"\nResource Utilization:") for resource in resources: print(f"\nResource{resource+1}:") for time_slot in time_slots: assigned_task = None for task, (r, t) in assignments.items(): if r == resource and t == time_slot: assigned_task = task break status = assigned_task if assigned_task else "Free" print(f" Time{time_slot+1}: {status}") if __name__ == "__main__": # Solve course scheduling print("Solving Course Scheduling Problem...") solution, courses, time_slots = solve_course_scheduling() print_schedule(solution, courses, time_slots, "Course Schedule") # Solve meeting scheduling print("\n" + "="*60) print("Solving Meeting Scheduling Problem...") solution, meetings, time_slots = solve_meeting_scheduling() print_schedule(solution, meetings, time_slots, "Meeting Schedule") # Solve resource scheduling print("\n" + "="*60) print("Solving Resource Scheduling Problem...") solution, tasks, resources, time_slots = solve_resource_scheduling() print_resource_schedule(solution, tasks, resources, time_slots)