source/create_dataset.py

import numpy as np
import os
import json
import itertools
import math
from loguru import logger
import tqdm
import time
import namer


def generate_random_pfsp_instance(nb_jobs, nb_machines, time_min, time_max, seed=97):
    #TODO: add the possibility to simply load an instance from a file
    """
    Generates a random instance of the Permutation Flow Shop Problem (PFSP).
    Parameters:
    - nb_jobs: Number of jobs (n).
    - nb_machines: Number of machines (m).j,
    - time_min: Minimum processing time for any job on any machine.
    - time_max: Maximum processing time for any job on any machine.
    Returns:
    - A 2D list (matrix) of size (nb_jobs x nb_machines) where each entry is a random processing time between time_min and time_max.
    """
    if seed is not None: np.random.seed(seed)
    
    # create a pfsp_instance using the uniform distribution
    pfsp_instance = np.random.uniform(low=time_min, high=time_max, size=(nb_jobs, nb_machines)).astype(np.float32)
    return pfsp_instance


def fit_palmer(pfsp_instance: np.ndarray):
    """
    Implements Palmer's heuristic for the flowshop scheduling problem. Returns a schedule and its corresponding makespan.
    For now I am using an old code that performs palmer by interfacing with it, but it should be refactored to be cleaner and more efficient.
    Parameters:
    - pfsp_instance: A 2D numpy array where pfsp_instance[i][j] is the processing time of job i on machine j.
    Returns:
    - A tuple (schedule, makespan) where:
        - schedule: A list of job indices representing the order of jobs (e.g., [0, 2, 1]).
        - makespan: The total completion time for the given schedule.
    """

    # =====================================================================================
    class Palmer:
        def __init__(self, jobs_list: list):
            self.jobs_list = jobs_list
            self.nb_jobs = len(jobs_list)
            self.nb_machines = len(jobs_list[0])
            self.seq_star = None
            self.make_span_star = None

        # utility function that returns the gantt cumule based on a job execution times and a previous gantt cumule
        def cumulate(self, job: list, previous_cumul=None):
            res = [0] * len(job)

            if previous_cumul == None:
                res[0] = job[0]
                for i in range(1, len(job)):
                    res[i] = res[i - 1] + job[i]
            else:
                res[0] = previous_cumul[0] + job[0]
                for i in range(1, len(job)):
                    res[i] = max(res[i - 1], previous_cumul[i]) + job[i]

            return res

        # utility function that computes the gantt cumule given only a job sequence (not used in the algorithm due to inneficiency
        # dynamic programming with cumulate is used instead ...)
        def cumulate_seq(self, seq: list):
            cumulated = None
            for i in seq:
                cumulated = self.cumulate(self.jobs_list[i], cumulated)

            return cumulated

        # launching the optimization
        def optim(self, debug=False):
            jobs_weights = []
            for i, job in zip(range(self.nb_jobs), self.jobs_list):
                weight = 0
                for j in range(self.nb_machines):
                    if debug == True:
                        print(
                            f">job {i} mach {j} first term: {(2*(j+1) - 1) - self.nb_machines}"
                        )
                        print(f">job {i} mach {j} second term: {job[j]}")
                        print(
                            "------------------------------------------------------------------"
                        )
                    weight += ((2 * (j + 1) - 1) - self.nb_machines) * job[j]
                if debug == True:
                    print(f"===>> job {i} weight: {weight}")
                jobs_weights.append((weight, i))

            self.seq_star = [tu[1] for tu in sorted(jobs_weights, reverse=True)]
            self.make_span_star = self.cumulate_seq(self.seq_star)[-1]

            return (self.seq_star, self.make_span_star)

    # =====================================================================================

    # Interfacing with the underlying old palmer code
    jobs_list = pfsp_instance.tolist()
    palmer_schedule, palmer_makespan = Palmer(jobs_list).optim()

    # Returning the schedule and makespan as numpy arrays of type int32
    return np.array(palmer_schedule, dtype=np.int32), np.float32(palmer_makespan)


def fit_cds(pfsp_instance: np.ndarray):
    """
    Implements CDS heuristic for the flowshop scheduling problem. Returns a schedule and its corresponding makespan.
    For now I am using an old code that performs cds by interfacing with it, but it should be refactored to be cleaner and more efficient.
    Parameters:
    - pfsp_instance: A 2D numpy array where pfsp_instance[i][j] is the processing time of job i on machine j.
    Returns:
    - A tuple (schedule, makespan) where:
        - schedule: A list of job indices representing the order of jobs (e.g., [0, 2, 1]).
        - makespan: The total completion time for the given schedule.
    """

    # =====================================================================================
    # Function to cumulate job processing times
    def cumulate(job, previous_cumul=None):
        res = [0] * len(job)
        if previous_cumul is None:
            res[0] = job[0]
            for i in range(1, len(job)):
                res[i] = res[i - 1] + job[i]
        else:
            res[0] = previous_cumul[0] + job[0]
            for i in range(1, len(job)):
                res[i] = max(res[i - 1], previous_cumul[i]) + job[i]
        return res

    # Function to cumulate processing times for a given sequence of jobs
    def cumulate_seq(seq, jobs_list):
        cumulated = None
        for i in seq:
            cumulated = cumulate(jobs_list[i], cumulated)
        return cumulated

    # Function to compute the makespan given a sequence of jobs and the job list
    def makespan(sequence, job_list):
        return cumulate_seq(sequence, job_list)[-1]

    # Function to perform the Johnson's algorithm for the flow shop problem
    def johnson_algorithm(matrix):
        n = matrix.shape[0]
        sequence = []
        machines = [[], []]

        # Preprocessing to determine the order of jobs
        for i in range(n):
            if matrix[i][0] < matrix[i][1]:  # if time(m1) < time(m2)
                machines[0].append((matrix[i][0], i))
            else:
                machines[1].append((matrix[i][1], i))

        # Sorting jobs for each machine
        machines[0] = sorted(
            machines[0], key=lambda x: x[0]
        )  # ascending sort for the first machine
        machines[1] = sorted(
            machines[1], key=lambda x: x[0], reverse=True
        )  # descending sort for the second machine

        # Merging the two sorted lists
        merged = machines[0] + machines[1]

        # Constructing the optimal sequence
        sequence = [index for _, index in merged]

        return sequence

    # Function that applies Johnson's algorithm and computes the makespan
    def johnson(job_matrix, data_matrix):
        sequence = johnson_algorithm(job_matrix)
        return sequence, makespan(sequence, data_matrix)

    # CDS heuristic
    def cds_heuristic(matrix):
        n = matrix.shape[0]
        m = matrix.shape[1]
        best_makespan = float("inf")
        best_sequences = []

        # Step 1: Generate matrices of all possible job lists
        for i in range(1, m):
            machine_subset_1 = matrix[:, :i].sum(axis=1)
            machine_subset_2 = matrix[:, -i:].sum(axis=1)
            job_matrix = np.column_stack((machine_subset_1, machine_subset_2))

            # Step 2: Apply Johnson's algorithm to the job matrix abd calculate the makespan
            sequence, makespan_value = johnson(job_matrix, matrix)

            # Step 3: Update the best makespan and corresponding sequences
            if makespan_value < best_makespan:
                best_makespan = makespan_value
                best_sequences = [sequence]
            elif makespan_value == best_makespan:
                best_sequences.append(sequence)

        return best_sequences[0], best_makespan

    # =====================================================================================

    # Interfacing with the underlying old cds code
    cds_schedule, cds_makespan = cds_heuristic(pfsp_instance)

    # Returning the schedule and makespan as numpy arrays of type int32
    return np.array(cds_schedule, dtype=np.int32), np.float32(cds_makespan)


def fit_neh(pfsp_instance: np.ndarray):
    """
    Implements NEH heuristic for the flowshop scheduling problem. Returns a schedule and its corresponding makespan.
    For now I am using an old code that performs neh by interfacing with it, but it should be refactored to be cleaner and more efficient.
    Parameters:
    - pfsp_instance: A 2D numpy array where pfsp_instance[i][j] is the processing time of job i on machine j.
    Returns:
    - A tuple (schedule, makespan) where:
        - schedule: A list of job indices representing the order of jobs (e.g., [0, 2, 1]).
        - makespan: The total completion time for the given schedule.
    """

    # =====================================================================================
    class Inst:
        def __init__(
            self,
            jobs: int,
            machines: int,
            seed: int,
            ub: int,
            lb: int,
            matrix: list[list[int]],
        ):
            self.jobs = jobs
            self.machines = machines
            self.seed = seed
            self.ub = ub
            self.lb = lb
            self.matrix = matrix

        def __repr__(self) -> str:
            return f"Inst(jobs={self.jobs}, machines={self.machines}, seed={self.seed}, ub={self.ub}, lb={self.lb}, matrix={self.matrix})"

    class NEH:
        def __init__(self, instance: Inst, debug: bool = False):
            self.instance = instance
            self.debug = debug

        def calculate_sj(self, job: int) -> int:
            sj = 0
            for machine in range(self.instance.machines):
                sj += self.instance.matrix[machine][job]
            return sj

        def sort_jobs(self, reverse: bool = False) -> list[int]:
            return sorted(
                range(self.instance.jobs),
                key=lambda job: self.calculate_sj(job),
                reverse=reverse,
            )

        def emulate(self, jobs: list[int]) -> list[int]:
            machines_exec = [0] * self.instance.machines
            for job in jobs:
                for current_machine in range(self.instance.machines):
                    # Add jobs execution time to current machine
                    machines_exec[current_machine] += self.instance.matrix[
                        current_machine
                    ][job]

                    # Sync other machines if they are behind current time
                    for machine in range(current_machine + 1, self.instance.machines):
                        machines_exec[machine] = max(
                            machines_exec[current_machine], machines_exec[machine]
                        )

            return machines_exec

        def calculate_cmax(self, jobs: list[int]) -> int:
            return self.emulate(jobs)[-1]

        def get_best_order(self, orders: list[list[int]]) -> tuple[int, list[int]]:
            min_cmax = float("inf")
            min_order = None
            for order in orders:
                cmax = self.calculate_cmax(order)
                if cmax < min_cmax:
                    min_cmax = cmax
                    min_order = order

            return min_cmax, min_order

        def get_best_position(
            self, order: list[int], job: int
        ) -> tuple[int, list[int]]:
            possible_orders: list[list[int]] = []
            for pos in range(len(order) + 1):
                possible_orders.append(order[:pos] + [job] + order[pos:])

            return self.get_best_order(possible_orders)

        def __call__(self) -> tuple[int, list[int]]:
            if self.instance.jobs < 2:
                raise ValueError("Number of jobs must be greater than 2")

            sorted_jobs = self.sort_jobs()
            current_cmax, current_order = self.get_best_order(
                [sorted_jobs[:2], sorted_jobs[:2][::-1]]
            )

            if self.debug:
                print(current_cmax, current_order)

            if self.instance.jobs == 2:
                return current_cmax, current_order

            for job in sorted_jobs[2:]:
                current_cmax, current_order = self.get_best_position(current_order, job)
                if self.debug:
                    print(current_cmax, current_order)

            return current_cmax, current_order

    # =====================================================================================

    # Interfacing with the underlying old neh code
    neh_instance_jobs = pfsp_instance.shape[0]
    neh_instance_machines = pfsp_instance.shape[1]
    neh_instance_matrix = pfsp_instance.T.tolist()
    neh_instance = Inst(
        neh_instance_jobs,
        neh_instance_machines,
        seed=0,
        ub=0,
        lb=0,
        matrix=neh_instance_matrix,
    )
    neh_makespan, neh_schedule = NEH(neh_instance)()

    # Returning the schedule and makespan as numpy arrays of type int32
    return np.array(neh_schedule, dtype=np.int32), np.float32(neh_makespan)


def evaluate_makespan(pfsp_instance, schedule):
    """
    Evaluates the makespan (completion time) of a given schedule for a given pfsp_instance.
    Parameters:
    - pfsp_instance: A list of lists, where pfsp_instance[i][j] is the processing time of job i on machine j.
    - schedule: A list/tuple indicating the order of jobs (e.g., [0, 2, 1]).
    Returns:
    - The makespan (total completion time) for the given schedule.
    """

    def cumulate(job: list, previous_cumul=None):
        # Calculate the cumulative completion times for a job

        res = [0] * len(job)
        if previous_cumul == None:
            res[0] = job[0]
            for i in range(1, len(job)):
                res[i] = res[i - 1] + job[i]
        else:
            res[0] = previous_cumul[0] + job[0]
            for i in range(1, len(job)):
                res[i] = max(res[i - 1], previous_cumul[i]) + job[i]
        return res

    def cumulate_seq(pfsp_instance: list, schedule: list):
        # Calculates the cumulative time for a sequence of jobs on machines.
        cumulates = [0] * len(pfsp_instance)
        cumulated = None
        for j, i in enumerate(schedule):
            cumulated = cumulate(pfsp_instance[i], cumulated)
            cumulates[j] = cumulated[-1]
        return cumulates

    cumulates = cumulate_seq(pfsp_instance, schedule)
    return cumulates


def create_dataset(
    testing,
    pfsp_instance,
    nb_samples,
    init_type,
    output_dir,
    seed,
    normalize_makespans,
    nb_jobs,
    nb_machines,
    time_min,
    time_max,
    autoname_output_dir,
):

    if autoname_output_dir:
        output_dir = os.path.join(output_dir, time.strftime("%Y_%m_%d_%H_%M_%S") + "_" + namer.generate(separator="_", category="sports"))
    
    # prepare loging
    logger.add(os.path.join(output_dir, "create_dataset.log"))

    if os.path.exists(pfsp_instance):
        # TODO: add logic to load pfsp_instance from some file
        pass
    else:
        # create pfsp_instance
        logger.info(f"Creating pfsp_instance with {nb_jobs} jobs and {nb_machines} machines")
        pfsp_instance = generate_random_pfsp_instance(nb_jobs, nb_machines, time_min, time_max, seed=seed)

    # create the output folder
    os.makedirs(output_dir, exist_ok=True)

    # check if experiment termination flag file exists
    if not testing:
        if os.path.exists(os.path.join(output_dir, ".terminated_create_dataset")):
            print("Dataset creation already done. Exiting...")
            return None

    
    # log parameters
    logger.info(f"nb_samples: {nb_samples}")
    logger.info(f"init_type: {init_type}")
    logger.info(f"output_dir: {output_dir}")
    logger.info(f"seed: {seed}")

    # compute the makespan upperbound if needed
    if normalize_makespans:
        makespans_upperbound = np.sum(pfsp_instance) # The upperbound of the makespan is the sum of all processing times (worst case: all jobs are processed sequentially with no parallelism)
        logger.info(f"Normalizing makespans by the sum of processing times with pfsp sum: {makespans_upperbound}")
    else:
        makespans_upperbound = None
        logger.info("Not normalizing makespans")

    if init_type == "exhaustive":
        nb_samples = math.factorial(pfsp_instance.shape[0])
        logger.info(f"Exhaustive init_type: Number of samples: {nb_samples}")
    
    if seed is not None: np.random.seed(seed)


    def perturb_schedule(schedule):
        perturbed_schedule = schedule[:]
        i, j = np.random.choice(perturbed_schedule.shape[0], size=2, replace=False)
        perturbed_schedule[[i,j]] = perturbed_schedule[[j,i]]
        return perturbed_schedule, evaluate_makespan(pfsp_instance, perturbed_schedule)
    # ======


    # create the folder if it doesn't exist
    os.makedirs(output_dir, exist_ok=True)

    # create the np memmap files for schedules and makespans
    nb_jobs = pfsp_instance.shape[0]
    schedules = np.lib.format.open_memmap(os.path.join(output_dir,"schedules.npy"), dtype=np.int32, mode='w+', shape=(nb_samples, nb_jobs))
    makespans = np.lib.format.open_memmap(os.path.join(output_dir,"makespans.npy"), dtype=np.float32, mode='w+', shape=(nb_samples, nb_jobs))
    
    # save the pfsp instance as a numpy file
    np.save(os.path.join(output_dir,"pfsp_instance.npy"), pfsp_instance)
    
    # create a metadata dictionary and save it as a json file
    metadata_dict = {
        "nb_samples": nb_samples,
        "nb_samples": nb_samples,
        "nb_jobs": nb_jobs,
        "nb_machines": pfsp_instance.shape[1],
        "init_type": init_type,
        "data_path": output_dir,
        "seed": seed,
        "date_time": time.strftime('%Y_%m_%d_%H_%M_%S')
    }

    with open(os.path.join(output_dir,"metadata.json"), "w") as f:
        json.dump(metadata_dict, f, indent=4)
    
    if init_type == "exhaustive":
        for i, schedule in tqdm.tqdm(enumerate(itertools.permutations(range(nb_jobs))), total=math.factorial(nb_jobs)):
            schedules[i] = schedule
            makespans[i] = evaluate_makespan(pfsp_instance, schedule)

    elif init_type == "cds":
        cds_schedule, _ = fit_cds(pfsp_instance)
        cds_makespan = evaluate_makespan(pfsp_instance, cds_schedule)
        schedules[0] = cds_schedule
        makespans[0] = cds_makespan
        for i in tqdm.tqdm(range(1, nb_samples), desc="Generating CDS samples"):
            schedules[i], makespans[i] = perturb_schedule(cds_schedule)
    
    elif init_type == "palmer":
        palmer_schedule, _ = fit_palmer(pfsp_instance)
        palmer_makespan = evaluate_makespan(pfsp_instance, palmer_schedule)
        schedules[0] = palmer_schedule
        makespans[0] = palmer_makespan
        for i in tqdm.tqdm(range(1, nb_samples), desc="Generating Palmer samples"):
            schedules[i], makespans[i] = perturb_schedule(palmer_schedule)

    elif init_type == "neh":
        neh_schedule, _ = fit_neh(pfsp_instance)
        neh_makespan = evaluate_makespan(pfsp_instance, neh_schedule)
        schedules[0] = neh_schedule
        makespans[0] = neh_makespan
        for i in tqdm.tqdm(range(1, nb_samples), desc="Generating NEH samples"):
            schedules[i], makespans[i] = perturb_schedule(neh_schedule)

    elif init_type == "heuristics":
        cds_schedule, _ = fit_cds(pfsp_instance)
        cds_makespan = evaluate_makespan(pfsp_instance, cds_schedule)
        schedules[0], makespans[0] = cds_schedule, cds_makespan
        cds_size = nb_samples // 3
        for i in tqdm.tqdm(range(1, cds_size), desc="Generating CDS heuristic samples"):
            schedules[i], makespans[i] = perturb_schedule(cds_schedule)
        i+=1
        palmer_schedule, _ = fit_palmer(pfsp_instance)
        palmer_makespan = evaluate_makespan(pfsp_instance, palmer_schedule)
        schedules[i], makespans[i] = palmer_schedule, palmer_makespan
        palmer_size = nb_samples // 3
        for i in tqdm.tqdm(range(i+1, i+palmer_size), desc="Generating Palmer heuristic samples"):
            schedules[i], makespans[i] = perturb_schedule(palmer_schedule)
        i+=1
        neh_schedule, _ = fit_neh(pfsp_instance)
        neh_makespan = evaluate_makespan(pfsp_instance, neh_schedule)
        schedules[i], makespans[i] = neh_schedule, neh_makespan
        neh_size = nb_samples - cds_size - palmer_size
        for i in tqdm.tqdm(range(i+1, i+neh_size), desc="Generating NEH heuristic samples"):
            schedules[i], makespans[i] = perturb_schedule(neh_schedule)

    elif init_type == "random":
        for i in tqdm.tqdm(range(nb_samples), desc="Generating Random samples"):
            schedule = np.random.permutation(pfsp_instance.shape[0])
            makespan = evaluate_makespan(pfsp_instance, schedule)
            schedules[i] = schedule
            makespans[i] = makespan

    else:
        raise ValueError("Invalid initialization type")
    
    # normalize the makespans if an upperbound is provided
    if makespans_upperbound is not None:
        makespans /= makespans_upperbound

    # flush
    schedules.flush()
    makespans.flush()

    # save min makespan
    min_makespan = np.min(makespans[:, -1])
    np.save(os.path.join(output_dir, "min_makespan.npy"), min_makespan)
    logger.info(f"Minimum makespan: {min_makespan}")

    # fit the heuristics and save their makespans
    neh_schedule, neh_makespan = fit_neh(pfsp_instance); neh_makespan/=makespans_upperbound
    cds_schedule, cds_makespan = fit_cds(pfsp_instance); cds_makespan/=makespans_upperbound
    palmer_schedule, palmer_makespan = fit_palmer(pfsp_instance); palmer_makespan/=makespans_upperbound
    np.save(os.path.join(output_dir, "neh_makespan.npy"), neh_makespan)
    np.save(os.path.join(output_dir, "cds_makespan.npy"), cds_makespan)
    np.save(os.path.join(output_dir, "palmer_makespan.npy"), palmer_makespan)

    # add termination flag file
    with open(os.path.join(output_dir, ".terminated_create_dataset"), "w") as f: pass

    # return
    return schedules, makespans
# ======


if __name__ == "__main__":
    
    # parse arguments and call create_dataset with the appropriate parameters
    import argparse
    parser = argparse.ArgumentParser(description="Create a dataset for the flowshop scheduling problem")
    parser.add_argument("--testing", type=bool, required=True)
    parser.add_argument("--nb_jobs", type=int, required=True, help="Number of jobs")
    parser.add_argument("--nb_machines", type=int, required=True, help="Number of machines")
    parser.add_argument("--time_min", type=int, required=True, help="Minimum processing time")
    parser.add_argument("--time_max", type=int, required=True, help="Maximum processing time")
    parser.add_argument("--nb_samples", type=int, required=True, help="Number of base samples to generate")
    parser.add_argument("--init_type", type=str, required=True, choices=["exhaustive", "cds", "palmer", "neh", "heuristics", "random"], help="Initialization type for the base samples")
    parser.add_argument("--output_dir", type=str, required=True, help="Path to the output directory where dataset artifacts will be saved")
    parser.add_argument("--autoname_output_dir", type=bool, required=True, help="Whether to autoname the output directory")
    parser.add_argument("--seed", type=int, required=True, help="Random seed for reproducibility (set to None for no seeding)")
    parser.add_argument("--normalize_makespans", type=bool, required=True, help="Whether to normalize makespans by the sum of processing times")
    parser.add_argument("--pfsp_instance", type=str, required=True, help="Path to the pfsp instance or None if to be generated")
    args = parser.parse_args()

    # create the dataset
    create_dataset(
        testing=args.testing,
        nb_samples=args.nb_samples,
        init_type=args.init_type,
        output_dir=args.output_dir,
        seed=args.seed,
        normalize_makespans=args.normalize_makespans,
        pfsp_instance=args.pfsp_instance,
        nb_jobs=args.nb_jobs,
        nb_machines=args.nb_machines,
        time_min=args.time_min,
        time_max=args.time_max,
        autoname_output_dir=args.autoname_output_dir,
    )
# ======