Aprende Automating Project Schedules | Project Management and Optimization

Project scheduling is a crucial aspect of civil engineering project management. Schedules help you plan, coordinate, and track the progress of tasks, ensuring that resources are used efficiently and deadlines are met. Common tools used for scheduling include Gantt charts, which provide a visual timeline of tasks, and dependency lists, which specify which tasks must be completed before others can begin. In large projects, manually creating and updating these schedules can be time-consuming and error-prone. Automating these processes with Python allows you to quickly generate schedules, analyze task sequences, and adapt to project changes.


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# Define project tasks with durations and dependencies
tasks = {
    "Site Preparation": {
        "duration": 5,
        "dependencies": []
    },
    "Foundation": {
        "duration": 10,
        "dependencies": ["Site Preparation"]
    },
    "Framing": {
        "duration": 7,
        "dependencies": ["Foundation"]
    },
    "Roofing": {
        "duration": 4,
        "dependencies": ["Framing"]
    },
    "Finishing": {
        "duration": 6,
        "dependencies": ["Roofing"]
    }
}

This dictionary-based structure makes it easy to automate schedule calculations. Each task is represented with its name as a key, and the value is another dictionary containing the task's duration and a list of dependencies. By storing dependencies this way, you can programmatically determine the order in which tasks must be performed and calculate when each task can start and finish. This approach also sets the stage for visualizing the schedule as a Gantt chart or analyzing the impact of delays.


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def calculate_earliest_times(tasks):
    earliest_start = {}
    earliest_finish = {}

    def get_earliest_start(task):
        deps = tasks[task]["dependencies"]
        if not deps:
            return 0
        return max(earliest_finish[dep] for dep in deps)

    for task in tasks:
        start = get_earliest_start(task)
        finish = start + tasks[task]["duration"]
        earliest_start[task] = start
        earliest_finish[task] = finish

    return earliest_start, earliest_finish

# Calculate and display earliest start and finish times for each task
earliest_start, earliest_finish = calculate_earliest_times(tasks)
for task in tasks:
    print(f"{task}: Start at day {earliest_start[task]}, Finish at day {earliest_finish[task]}")

1. What is the main advantage of automating project schedules with Python?

2. How are task dependencies represented in the code sample?

3. Fill in the blank: The ______ time of a task is the earliest it can begin, considering dependencies.

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Sección 3. Capítulo 1

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Suggested prompts:

Can you explain how the dependencies affect the schedule?

How would I add a new task with dependencies to this structure?

Can you show how to visualize this schedule as a Gantt chart?

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