Hexanol777/SequencingSolver

A python package for solving various sequencing problems by Nima Mahmoodian.

SequencingSolver

A Python package for solving various sequencing problems by Nima Mahmoodian.

How to install the library?

To run the library, you need Numpy and Matplotlib to be installed. Then in the command line, run the following command:

Linux/Mac:

python3 -m pip install SequencingSolver

Windows:

For Windows, use the following command in the command prompt:

py -m pip install SequencingSolver

How to use the library?

in version 1.0.2 of the library, there are currently six optimization algorithms: WSPT, WDSPT, EDD, Hodgson, LCL, and minimizeSumCjStDeadline. for using any solver, all you need to do is to pass the correct form of data structure (which I call jobsData) to the corresponding solver.

Let's take a quick look at each.

WSPT Solver

Solves a scheduling problem using the Weighted Shortest Processing Time (WSPT) algorithm and creates a Gantt chart.

Args: jobsData (dict): A dictionary containing job data, where keys are job identifiers, and values are dictionaries with the following format:

                    {
                        "processingtime": int,  # The time required to complete the job.
                        "weight": int            # The weight of the job.
                    }

it returns the optimal sequence and the corresponding Gantt chart.

Example usage:

    jobsData = {
        "Job1": {"processingtime": 5, "weight": 10},
        "Job2": {"processingtime": 4, "weight": 8},
        "Job3": {"processingtime": 6, "weight": 12}
    }
    wsptSolver(jobsData)

WDSPT Solver

Solve a scheduling problem using the Weighted Discounted Shortest Processing Time (WDSPT) algorithm and create a Gantt chart.

Args: jobsData (dict): A dictionary containing job data, where keys are job identifiers, and values are dictionaries

                    with the following format:
                    {
                        "processingtime": int,  # The time required to complete the job.
                        "weight": int            # The weight of the job.
                    }
    r (float): A discount factor for adjusting the criteria calculation.

it returns the optimal sequence and the corresponding Gantt chart.

Example usage:

 jobsData = {
     "Job1": {"processingtime": 5, "weight": 10},
     "Job2": {"processingtime": 4, "weight": 8},
     "Job3": {"processingtime": 6, "weight": 12}
 }
 wdsptSolver(jobsData, r=0.1)

EDD Solver

Solve the Early Due Date (EDD) scheduling problem and create a Gantt chart for the optimal sequence of jobs.

Args:

jobsData (dict): A dictionary containing job data, where keys are job identifiers, and values are dictionaries with the following format:

                    {
                        "processingtime": int,  # The time required to complete the job.
                        "duedate": int           # The due date for the job.
                    }

Example usage:

    jobsData = {
        "Job1": {"processingtime": 5, "duedate": 10},
        "Job2": {"processingtime": 4, "duedate": 8},
        "Job3": {"processingtime": 6, "duedate": 12}
    }

Hodgson Solver

Solve a scheduling problem using the Hodgson algorithm and generate alternative job sequences.

Args:

    jobsData (dict): A dictionary containing job data, where keys are job identifiers, and values are dictionaries
                    with the following format:
                    {
                        "processingtime": int,  # The time required to complete the job.
                        "duedate": int           # The due date for the job.
                    }

**Example usage:**

    jobsData = {
        "Job1": {"processingtime": 5, "duedate": 10},
        "Job2": {"processingtime": 4, "duedate": 8},
        "Job3": {"processingtime": 6, "duedate": 12}
    }
    hodgsonSolver(jobsData)

LCL Solver

Solve a scheduling problem using the Lowest Cost Last (LCL) algorithm and create a Gantt chart for the optimal sequence.

Args:

    jobsData (dict): A dictionary containing job data, where keys are job identifiers, and values are dictionaries
                    with the following format:
                    {
                        "processingtime": int,  # The time required to complete the job.
                        "successors": set,      # A set of job identifiers that depend on this job.
                        "hFunction": function  # A function for computing the h value.
                    }

Important note You must always pass jobsComplementarySet, jobsData, and j to the h calculator function. No matter whether it uses them or not.

Example usage:

    jobsData = {
        "Job1": {"processingtime": 5, "successors": {"Job2"}, "hFunction": some_function},
        "Job2": {"processingtime": 4, "successors": set(), "hFunction": some_function},
        "Job3": {"processingtime": 6, "successors": {"Job1", "Job2"}, "hFunction": some_function}
    }
    lclSolver(jobsData)

minimizeSumCjStDeadline

Solve a scheduling problem to minimize the sum of completion times (Cj) while meeting job deadlines.

Args:

    jobsData (dict): A dictionary containing job data, where keys are job identifiers, and values are dictionaries
                    with the following format:
                    {
                        "processingtime": int,  # The time required to complete the job.
                        "deadline": int           # The job's deadline.
                    }

This function applies an algorithm to schedule jobs to minimize the sum of completion times (Cj) while ensuring that job deadlines are met. It computes the optimal job sequence and generates a Gantt chart to visualize the schedule. The function will print the optimal job sequence and create a Gantt chart to represent the job schedule.

Example usage:

    jobsData = {
        "Job1": {"processingtime": 5, "deadline": 10},
        "Job2": {"processingtime": 4, "deadline": 8},
        "Job3": {"processingtime": 6, "deadline": 12}
    }
    minimizeSumCjstDeadline(jobsData)