Lära Resource Allocation and Scheduling | Optimizing Operations with Python

Resource allocation and scheduling are core challenges in operations management. You are often faced with the task of assigning limited resources—such as workers, machines, or time slots—to a set of tasks or shifts. The goal is to maximize efficiency, ensure coverage, and meet operational goals, all while considering constraints like worker availability, labor laws, and fairness. These challenges become more complex as the size of your operation grows, making manual scheduling time-consuming and error-prone.


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# Assign workers to shifts based on their availability

# List of workers and their available shifts
workers = {
    "Alice": ["morning", "afternoon"],
    "Bob": ["afternoon", "night"],
    "Charlie": ["morning", "night"],
    "Dana": ["afternoon"],
}

# List of shifts to fill
shifts = ["morning", "afternoon", "night"]

# Dictionary to hold shift assignments
assignments = {shift: None for shift in shifts}

# Simple assignment: assign the first available worker to each shift
for shift in shifts:
    for worker, available_shifts in workers.items():
        if shift in available_shifts and worker not in assignments.values():
            assignments[shift] = worker
            break

print("Shift assignments:")
for shift, worker in assignments.items():
    print(f"{shift.capitalize()}: {worker if worker else 'Unfilled'}")

When designing scheduling algorithms, you must balance fairness and efficiency. Fairness ensures that no worker is consistently given unpopular or difficult shifts, while efficiency aims to make the best use of available resources, reducing costs and maximizing output. Achieving both can require thoughtful algorithm design, especially when preferences and operational constraints must be respected. Python allows you to automate and test different approaches, enabling you to find solutions that best fit your organization's needs.


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# Optimize shift assignments to minimize idle time

# Each worker has a maximum number of shifts they can take
worker_limits = {
    "Alice": 2,
    "Bob": 2,
    "Charlie": 1,
    "Dana": 1,
}

# Track how many shifts each worker has been assigned
assigned_counts = {worker: 0 for worker in workers}

# Assign shifts to minimize idle (unfilled) shifts and balance workload
optimized_assignments = {shift: None for shift in shifts}

for shift in shifts:
    # Find available workers who have not reached their limit
    candidates = [
        worker for worker, available_shifts in workers.items()
        if shift in available_shifts and assigned_counts[worker] < worker_limits[worker]
    ]
    # Assign the worker with the fewest current assignments
    if candidates:
        selected = min(candidates, key=lambda w: assigned_counts[w])
        optimized_assignments[shift] = selected
        assigned_counts[selected] += 1

print("Optimized shift assignments:")
for shift, worker in optimized_assignments.items():
    print(f"{shift.capitalize()}: {worker if worker else 'Unfilled'}")

Var allt tydligt?

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Avsnitt 3. Kapitel 3

Fråga AI

Fråga vad du vill eller prova någon av de föreslagna frågorna för att starta vårt samtal

Suggested prompts:

Can you explain how the optimized assignment improves fairness compared to the simple assignment?

What other constraints or preferences can be added to the scheduling algorithm?

How would you handle situations where there are more shifts than available workers?

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              1234567891011121314151617181920212223242526
            
# Assign workers to shifts based on their availability

# List of workers and their available shifts
workers = {
    "Alice": ["morning", "afternoon"],
    "Bob": ["afternoon", "night"],
    "Charlie": ["morning", "night"],
    "Dana": ["afternoon"],
}

# List of shifts to fill
shifts = ["morning", "afternoon", "night"]

# Dictionary to hold shift assignments
assignments = {shift: None for shift in shifts}

# Simple assignment: assign the first available worker to each shift
for shift in shifts:
    for worker, available_shifts in workers.items():
        if shift in available_shifts and worker not in assignments.values():
            assignments[shift] = worker
            break

print("Shift assignments:")
for shift, worker in assignments.items():
    print(f"{shift.capitalize()}: {worker if worker else 'Unfilled'}")


              12345678910111213141516171819202122232425262728293031
            
# Optimize shift assignments to minimize idle time

# Each worker has a maximum number of shifts they can take
worker_limits = {
    "Alice": 2,
    "Bob": 2,
    "Charlie": 1,
    "Dana": 1,
}

# Track how many shifts each worker has been assigned
assigned_counts = {worker: 0 for worker in workers}

# Assign shifts to minimize idle (unfilled) shifts and balance workload
optimized_assignments = {shift: None for shift in shifts}

for shift in shifts:
    # Find available workers who have not reached their limit
    candidates = [
        worker for worker, available_shifts in workers.items()
        if shift in available_shifts and assigned_counts[worker] < worker_limits[worker]
    ]
    # Assign the worker with the fewest current assignments
    if candidates:
        selected = min(candidates, key=lambda w: assigned_counts[w])
        optimized_assignments[shift] = selected
        assigned_counts[selected] += 1

print("Optimized shift assignments:")
for shift, worker in optimized_assignments.items():
    print(f"{shift.capitalize()}: {worker if worker else 'Unfilled'}")

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 3

Resource Allocation and Scheduling

1. What factors should be considered when allocating resources?

2. How can Python help automate shift scheduling?

3. What is the benefit of minimizing idle time in operations?

Resource Allocation and Scheduling

1. What factors should be considered when allocating resources?

2. How can Python help automate shift scheduling?

3. What is the benefit of minimizing idle time in operations?