Parallel Machine Scheduling

You are an expert in deterministic machine scheduling on a single machine and on identical (P), uniform (Q), and unrelated (R) parallel machines. This skill covers the polynomial single-machine dispatching rules (SPT, WSPT, EDD, Moore-Hodgson) with their optimality arguments, list scheduling and LPT with worst-case bounds, exact MIP models for makespan and due-date objectives, and an LNS heuristic for large instances. Use the framework below to classify the problem in three-field notation, pick the cheapest method that actually solves it, and validate every schedule independently.

Initial Assessment

Establish the following before proposing any model or algorithm.

• Machine environment. One machine, identical machines (same speed), uniform machines (speed factors s_k, so p_jk = p_j / s_k), or unrelated machines (a full n x m matrix p_jk with no structure)? This is the single biggest complexity driver.
• Objective. Makespan Cmax, total completion sum C_j, weighted completion sum w_j C_j, maximum lateness Lmax, number of tardy jobs sum U_j, or (weighted) total tardiness sum (w_j) T_j? Several of these are solved by a sorting rule; do not build a MIP for those.
• Regularity check. Is the objective non-decreasing in every completion time? All objectives above are regular, which means an optimal schedule exists with no inserted idle time. Earliness or just-in-time costs break this and require idle-time variables.
• Beta-field complications. Release dates r_j? Preemption allowed? Sequence-dependent setup times? Precedence constraints? Machine eligibility (job j may only run on a subset of machines)? Each one changes the complexity class. Confirm their absence explicitly; users forget to mention them.
• Instance size. Get n (jobs) and m (machines). The disjunctive MIP for tardiness has O(n^2 m) big-M rows and stalls beyond roughly 20-30 jobs; assignment-only models scale much further.
• Data type and horizon. Integer or fractional processing times? Time-indexed formulations need integer data and a horizon T ~ sum p_j; estimate n * m * T variables before suggesting one.
• Due-date data. Are due dates and weights given, or must they be generated? If generated for experiments, control tightness and range (TF/RDD parameters, see the instance generator below).
• Exactness need. Is a provable optimum required, or a good schedule with a reported gap against a lower bound? LPT plus the trivial bounds often certifies near-optimality without a solver.
• Time budget and solver access. Seconds or hours? Gurobi license available, or should the model target an open-source solver / CP-SAT?
• Usage pattern. One offline instance, or a dispatching decision repeated every few minutes inside a production system? The latter favors O(n log n) rules over any solver.
• Deliverable. Machine assignment only (enough for Cmax), or a fully timed schedule with per-machine sequences and start times (required for any due-date objective and for Gantt charts)?

Problem Classes and Formulation

Three-field notation (Graham, Lawler, Lenstra & Rinnooy Kan 1979, "Optimization and approximation in deterministic sequencing and scheduling"): alpha | beta | gamma where alpha is the machine environment (1, P, Pm, Q, R), beta lists constraints (r_j, pmtn, prec, s_jk), and gamma is the objective. Examples: 1 || sum w_j C_j, P || Cmax, R || Cmax, P | r_j | Lmax.

Formal setting. Jobs j in J = {1, ..., n}, machines k in M = {1, ..., m}. Job j has processing time p_jk on machine k (identical: p_jk = p_j; uniform: p_jk = p_j / s_k), optionally a due date d_j and weight w_j > 0. A schedule assigns each job to one machine and sequences the jobs on each machine; C_j is the completion time of job j. Derived quantities: lateness L_j = C_j - d_j, tardiness T_j = max(L_j, 0), tardy indicator U_j = 1 if C_j > d_j.

Two structural facts drive every method choice below:

1. For regular objectives, a schedule is fully described by the pair (assignment, per-machine sequence) with jobs run back-to-back from time 0. No start-time decisions are needed beyond that.
2. For Cmax the per-machine sequence is irrelevant (only machine loads matter), so parallel-machine makespan is a pure assignment problem. For completion-time and due-date objectives the sequence matters; given an assignment, WSPT within each machine is optimal for sum w_j C_j, and EDD within each machine is optimal for Lmax.

Complexity landscape.

Preemptive lower bound. P | pmtn | Cmax has optimum max(max_j p_j, sum_j p_j / m) (McNaughton 1959). This value is also the standard lower bound for the non-preemptive P || Cmax.

The unrelated-machine makespan problem, the workhorse exact model of this skill, reads:

$$ \min ; C_{\max} \quad \text{s.t.} \quad \sum_{k=1}^{m} x_{jk} = 1 ;; \forall j \in J, \qquad \sum_{j=1}^{n} p_{jk}, x_{jk} \le C_{\max} ;; \forall k \in M, \qquad x_{jk} \in {0, 1}. $$

Method selection.

Single-Machine Sequencing Rules

The WSPT exchange argument (Smith 1956, "Various optimizers for single-stage production"). Take any sequence in which job i immediately precedes job j with p_i / w_i > p_j / w_j, and let t be the start time of the pair. The pair contributes w_i (t + p_i) + w_j (t + p_i + p_j). After swapping, it contributes w_j (t + p_j) + w_i (t + p_j + p_i). The difference (before minus after) is w_j p_i - w_i p_j > 0, and no other job's completion time changes. So any sequence not ordered by non-decreasing p_j / w_j can be strictly improved by an adjacent swap, which proves WSPT optimal for 1 || sum w_j C_j. The same adjacent-interchange template proves SPT optimal for sum C_j (set w_j = 1) and EDD optimal for Lmax (Jackson 1955). This argument style transfers: given a fixed machine assignment on parallel machines, it applies machine by machine.

import numpy as np


def spt_order(p: np.ndarray) -> np.ndarray:
    """SPT: non-decreasing p_j. Optimal for 1 || sum C_j (Smith 1956 with unit weights)."""
    return np.argsort(p, kind="stable")


def wspt_order(p: np.ndarray, w: np.ndarray) -> np.ndarray:
    """WSPT: non-decreasing p_j / w_j. Optimal for 1 || sum w_j C_j (Smith 1956)."""
    return np.argsort(p / w, kind="stable")


def edd_order(d: np.ndarray) -> np.ndarray:
    """EDD: non-decreasing d_j. Optimal for 1 || Lmax (Jackson 1955)."""
    return np.argsort(d, kind="stable")


def sequence_stats(order: np.ndarray, p: np.ndarray, d: np.ndarray, w: np.ndarray) -> dict:
    """Recompute all standard objectives of a single-machine sequence (no inserted idle time)."""
    completion_in_seq = np.cumsum(p[order])
    c = np.empty_like(completion_in_seq)
    c[order] = completion_in_seq
    lateness = c - d
    tardiness = np.maximum(lateness, 0.0)
    return {
        "total_completion": float(c.sum()),
        "weighted_completion": float((w * c).sum()),
        "lmax": float(lateness.max()),
        "total_tardiness": float(tardiness.sum()),
        "num_tardy": int((tardiness > 0).sum()),
    }


p = np.array([3.0, 1.0, 4.0, 2.0])
w = np.array([1.0, 1.0, 4.0, 1.0])
d = np.array([4.0, 2.0, 9.0, 12.0])
order = wspt_order(p, w)
print(order, sequence_stats(order, p, d, w)["weighted_completion"])
# Expected: order [1, 2, 3, 0] (ratios p/w = [3, 1, 1, 2]; the stable sort puts job 1
# before job 2) and weighted completion 1*1 + 4*5 + 1*7 + 1*10 = 38.0.

Moore-Hodgson for 1 || sum U_j (Moore 1968, "An n job, one machine sequencing algorithm for minimizing the number of late jobs"). Scan jobs in EDD order, keeping a running completion time. The moment the current job would finish late, remove the longest job scheduled so far — removing the longest job restores the most slack, and an exchange argument shows this greedy choice is never wrong. Removed jobs are appended at the end in any order; only they are tardy.

import heapq

import numpy as np


def moore_hodgson(p: np.ndarray, d: np.ndarray) -> tuple[list[int], list[int]]:
    """1 || sum U_j in O(n log n) (Moore 1968). Returns (on_time_sequence, late_jobs)."""
    order = np.argsort(d, kind="stable")
    heap: list[tuple[float, int]] = []          # max-heap on p_j via negated keys
    t = 0.0
    late: list[int] = []
    for j in order.tolist():
        heapq.heappush(heap, (-float(p[j]), j))
        t += float(p[j])
        if t > d[j]:
            neg_p, k = heapq.heappop(heap)      # drop the longest job scheduled so far
            t += neg_p
            late.append(k)
    late_set = set(late)
    on_time = [j for j in order.tolist() if j not in late_set]
    return on_time, late


p = np.array([4.0, 3.0, 2.0, 5.0])
d = np.array([4.0, 5.0, 6.0, 10.0])
print(moore_hodgson(p, d))
# Expected: ([1, 2, 3], [0]) -- dropping job 0 leaves jobs 1, 2, 3 all on time; exactly
# one tardy job, which is optimal for this instance.

For 1 || sum T_j, no sorting rule is optimal. The problem is NP-hard in the ordinary sense (Du & Leung 1990), and the classic exact approach is Lawler's (1977) decomposition: in EDD order, the longest job's final position can be restricted to a small candidate set, splitting the instance into independent subproblems — a pseudo-polynomial DP. For 1 || sum w_j T_j (strongly NP-hard), use branch-and-bound with Emmons (1969) dominance rules on small instances and local search beyond that.

Identical Machines: List Scheduling, LPT, and Bounds

List scheduling assigns the next job in a given list to the machine that becomes free first. Graham's (1966) bound: if job l determines the makespan, it started at Cmax - p_l when all machines were busy, so m (Cmax - p_l) <= sum_{j != l} p_j, giving Cmax <= sum_j p_j / m + (1 - 1/m) p_l <= (2 - 1/m) OPT. This holds for any list order — a useful guarantee for online arrival.

LPT (Longest Processing Time first) sorts the list by non-increasing p_j and achieves Cmax(LPT) <= (4/3 - 1/(3m)) OPT (Graham 1969). Proof idea: if the critical job satisfies p_l <= OPT / 3, the list-scheduling inequality already gives the bound; otherwise every machine holds at most two jobs, and LPT is optimal for such instances.

Lower bounds for P || Cmax: the longest job max_j p_j; the average load ceil(sum_j p_j / m) (equal to the preemptive optimum of McNaughton 1959 for integer data); and the pairing bound: among the m + 1 longest jobs, two must share a machine, so the sum of the two smallest of them is a valid bound. Report LB = max of the three next to any heuristic value.

PTAS note. P || Cmax admits a PTAS (Hochbaum & Shmoys 1987, dual approximation): binary search a target makespan T, round the "big" jobs (those above eps * T) to few size classes, enumerate machine configurations by DP, and fill small jobs greedily. Polynomial for fixed eps but rarely competitive in practice; use it as a theory reference, not as an implementation plan.

import heapq

import numpy as np


def lpt_schedule(p: np.ndarray, m: int) -> tuple[np.ndarray, float]:
    """LPT for P || Cmax: longest job first onto the least-loaded machine.

    Guarantee: Cmax(LPT) <= (4/3 - 1/(3m)) * OPT (Graham 1969). Runs in O(n log n).
    """
    assign = np.empty(p.shape[0], dtype=np.int64)
    loads: list[tuple[float, int]] = [(0.0, k) for k in range(m)]
    heapq.heapify(loads)
    for j in np.argsort(-p, kind="stable").tolist():
        load, k = heapq.heappop(loads)
        assign[j] = k
        heapq.heappush(loads, (load + float(p[j]), k))
    return assign, max(load for load, _ in loads)


def cmax_lower_bound(p: np.ndarray, m: int) -> float:
    """Max of: longest job, average machine load, and the (m+1)-longest-jobs pairing bound."""
    lb = max(float(p.max()), float(np.ceil(p.sum() / m)))
    if p.shape[0] > m:
        top = np.sort(p)[::-1][: m + 1]         # two of these m+1 jobs must share a machine
        lb = max(lb, float(top[-2] + top[-1]))
    return lb


p = np.array([3.0, 3.0, 2.0, 2.0, 2.0])
assign, cmax = lpt_schedule(p, m=2)
print(assign, cmax, cmax_lower_bound(p, m=2))
# Expected: Cmax(LPT) = 7.0 against lower bound 6.0 -- the classic tight instance for
# Graham's 4/3 - 1/(3m) = 7/6 ratio at m = 2 (OPT = 6 with {3,3} and {2,2,2}).

When LPT matches the lower bound, optimality is certified without any solver. When it does not (as above), decide between the exact MIP below and LNS based on instance size and time budget. On uniform machines (Q), the same template works with completion-time-aware insertion (put the next longest job where it would finish earliest); the worst-case ratio degrades but the heuristic stays strong in practice.

Exact MIP Models in gurobipy

Assignment model for R || Cmax

Sequencing is irrelevant for makespan, so the model needs only assignment variables x_jk and the makespan variable — the formulation displayed in the framework section. The LP relaxation is weak (it splits jobs fractionally), but modern solvers handle thousands of jobs. Each constraint family lives in its own builder function so it can be unit-tested and reused.

import gurobipy as gp
import numpy as np
from gurobipy import GRB


def add_assignment_constraints(model: gp.Model, x: gp.tupledict, data: dict) -> None:
    """Every job is assigned to exactly one machine."""
    for j in range(data["n"]):
        model.addConstr(x.sum(j, "*") == 1, name=f"assign[{j}]")


def add_makespan_constraints(model: gp.Model, x: gp.tupledict, cmax: gp.Var, data: dict) -> None:
    """The load of every machine is at most the makespan."""
    p = data["p"]
    for k in range(data["m"]):
        load = gp.quicksum(float(p[j, k]) * x[j, k] for j in range(data["n"]))
        model.addConstr(load <= cmax, name=f"load[{k}]")


def solve_r_cmax(data: dict, time_limit: float = 60.0) -> dict:
    """Exact MIP for R || Cmax. data holds n, m, and p as an (n, m) numpy array."""
    model = gp.Model("r_cmax")
    model.Params.OutputFlag = 0
    model.Params.TimeLimit = time_limit
    x = model.addVars(data["n"], data["m"], vtype=GRB.BINARY, name="x")
    cmax = model.addVar(lb=0.0, vtype=GRB.CONTINUOUS, name="cmax")
    add_assignment_constraints(model, x, data)
    add_makespan_constraints(model, x, cmax, data)
    model.setObjective(cmax, GRB.MINIMIZE)
    model.optimize()
    usable = model.Status == GRB.OPTIMAL or (model.Status == GRB.TIME_LIMIT and model.SolCount > 0)
    if not usable:
        return {"status": int(model.Status)}
    x_val = np.array([[x[j, k].X for k in range(data["m"])] for j in range(data["n"])])
    return {
        "status": int(model.Status),
        "cmax": float(cmax.X),
        "assign": x_val.argmax(axis=1),
        "gap": float(model.MIPGap),
    }


data = {"n": 4, "m": 2, "p": np.array([[2, 3], [4, 1], [3, 3], [2, 5]], dtype=float)}
print(solve_r_cmax(data))
# Expected: cmax = 4.0 -- e.g. jobs 0 and 3 on machine 0 (load 2+2) and jobs 1 and 2 on
# machine 1 (load 1+3). The bound sum_j min_k p_jk / m = 8/2 = 4 proves optimality.

Disjunctive model for R | d_j | sum w_j T_j

Due-date objectives need per-machine sequences, hence start times s_j, completion times C_j, tardiness T_j, and ordering binaries y_ij (i < j; y_ij = 1 means i precedes j when they share a machine). The big-M disjunction is only active when both jobs sit on the same machine:

min  sum_j w_j T_j
s.t. sum_k x_jk = 1                                      for all j         (assignment)
     C_j  = s_j + sum_k p_jk x_jk                        for all j         (completion)
     s_j >= C_i - M (1 - y_ij) - M (2 - x_ik - x_jk)     for all i<j, k    (i before j on k)
     s_i >= C_j - M y_ij       - M (2 - x_ik - x_jk)     for all i<j, k    (j before i on k)
     T_j >= C_j - d_j                                    for all j         (tardiness)
     s_j, T_j >= 0;  x_jk, y_ij in {0, 1}

A safe and explainable big-M is M = sum_j max_k p_jk: no active schedule for a regular objective finishes later than that. The model has m * n * (n - 1) big-M rows and a weak LP relaxation; expect it to be practical up to roughly 20-30 jobs. Beyond that, switch to CP-SAT interval models (the pattern is covered in job-shop-scheduling) or to the LNS below.

import gurobipy as gp
import numpy as np
from gurobipy import GRB


def add_assignment_constraints(model: gp.Model, x: gp.tupledict, data: dict) -> None:
    """Every job is assigned to exactly one machine (same builder as the makespan model)."""
    for j in range(data["n"]):
        model.addConstr(x.sum(j, "*") == 1, name=f"assign[{j}]")


def add_completion_constraints(model: gp.Model, x: gp.tupledict, s: gp.tupledict,
                               c: gp.tupledict, data: dict) -> None:
    """C_j = s_j + processing time of j on its chosen machine."""
    p = data["p"]
    for j in range(data["n"]):
        proc = gp.quicksum(float(p[j, k]) * x[j, k] for k in range(data["m"]))
        model.addConstr(c[j] == s[j] + proc, name=f"complete[{j}]")


def add_disjunctive_constraints(model: gp.Model, x: gp.tupledict, s: gp.tupledict,
                                c: gp.tupledict, y: gp.tupledict, data: dict) -> None:
    """Jobs sharing a machine must not overlap; y[i, j] = 1 means i precedes j."""
    n, m, big_m = data["n"], data["m"], data["big_m"]
    for i in range(n):
        for j in range(i + 1, n):
            for k in range(m):
                shared = 2 - x[i, k] - x[j, k]
                model.addConstr(s[j] >= c[i] - big_m * (1 - y[i, j]) - big_m * shared,
                                name=f"prec[{i},{j},{k}]")
                model.addConstr(s[i] >= c[j] - big_m * y[i, j] - big_m * shared,
                                name=f"prec[{j},{i},{k}]")


def add_tardiness_constraints(model: gp.Model, c: gp.tupledict, t: gp.tupledict,
                              data: dict) -> None:
    """T_j >= C_j - d_j; T_j >= 0 comes from the variable lower bound."""
    for j in range(data["n"]):
        model.addConstr(t[j] >= c[j] - float(data["d"][j]), name=f"tardy[{j}]")


def solve_weighted_tardiness(data: dict, time_limit: float = 120.0) -> dict:
    """Exact MIP for R | d_j | sum w_j T_j: machine assignment plus big-M sequencing."""
    model = gp.Model("r_sum_wt")
    model.Params.OutputFlag = 0
    model.Params.TimeLimit = time_limit
    n, m = data["n"], data["m"]
    data = {**data, "big_m": float(np.asarray(data["p"]).max(axis=1).sum())}
    x = model.addVars(n, m, vtype=GRB.BINARY, name="x")
    y = model.addVars([(i, j) for i in range(n) for j in range(i + 1, n)],
                      vtype=GRB.BINARY, name="y")
    s = model.addVars(n, lb=0.0, name="s")
    c = model.addVars(n, lb=0.0, name="c")
    t = model.addVars(n, lb=0.0, name="t")
    add_assignment_constraints(model, x, data)
    add_completion_constraints(model, x, s, c, data)
    add_disjunctive_constraints(model, x, s, c, y, data)
    add_tardiness_constraints(model, c, t, data)
    model.setObjective(gp.quicksum(float(data["w"][j]) * t[j] for j in range(n)), GRB.MINIMIZE)
    model.optimize()
    usable = model.Status == GRB.OPTIMAL or (model.Status == GRB.TIME_LIMIT and model.SolCount > 0)
    if not usable:
        return {"status": int(model.Status)}
    assign = [max(range(m), key=lambda k: x[j, k].X) for j in range(n)]
    return {
        "status": int(model.Status),
        "objective": float(model.ObjVal),
        "assign": assign,
        "start": [float(s[j].X) for j in range(n)],
        "gap": float(model.MIPGap),
    }


data = {
    "n": 3, "m": 2,
    "p": np.array([[3, 3], [2, 2], [2, 2]], dtype=float),
    "d": np.array([3.0, 2.0, 3.0]),
    "w": np.array([2.0, 1.0, 1.0]),
}
print(solve_weighted_tardiness(data))
# Expected: objective = 1.0 -- job 0 alone on one machine (on time), jobs 1 then 2 on the
# other machine (C_1 = 2 on time; C_2 = 4 is one unit late with weight 1).

Modeling notes: drop the y variables for pairs that can never share a machine (machine eligibility), and tighten the second big-M occurrence to pair-specific values M_ij = max_k p_ik + max upstream load when the generic horizon proves too loose. For identical machines, add symmetry breaking (see Advanced Techniques) before anything else.

LNS, Instance Generation, and Validation

LNS for R || Cmax

Large Neighborhood Search is the natural metaheuristic here: the destroy/repair pattern maps directly onto "free some jobs, reinsert them greedily," and for makespan no sequencing state needs repair. The implementation below keeps the loads vectorized (np.bincount with weights and broadcast ld + p[j] for the repair scan). It is a deliberately small fixed-operator LNS; for adaptive operator weights, regret-based insertion, and simulated-annealing acceptance, use the full machinery in the large-neighborhood-search skill.

import numpy as np


def lns_r_cmax(p: np.ndarray, iters: int = 2000, q: int = 4, seed: int = 0) -> tuple[np.ndarray, float]:
    """LNS for R || Cmax. p has shape (n, m) with p[j, k] = time of job j on machine k.

    Destroy: remove q jobs, half from the busiest machine, half uniformly at random.
    Repair: greedy minimum-resulting-load reinsertion in random order.
    Acceptance: accept ties or improvements (drifts across the flat makespan plateaus).
    See the large-neighborhood-search skill for the full adaptive (ALNS) version.
    """
    rng = np.random.default_rng(seed)
    n, m = p.shape
    jobs = np.arange(n)

    def loads(a: np.ndarray) -> np.ndarray:
        return np.bincount(a, weights=p[jobs, a], minlength=m)

    cur = p.argmin(axis=1).astype(np.int64)             # cheapest-machine start
    cur_val = float(loads(cur).max())
    best, best_val = cur.copy(), cur_val
    for _ in range(iters):
        a = cur.copy()
        ld = loads(a)
        busy = np.flatnonzero(a == int(ld.argmax()))
        k1 = min(q // 2, busy.size)
        rem1 = rng.choice(busy, size=k1, replace=False)
        rest = np.setdiff1d(jobs, rem1)
        rem2 = rng.choice(rest, size=min(q - k1, rest.size), replace=False)
        removed = np.concatenate([rem1, rem2]).astype(np.int64)
        ld = ld - np.bincount(a[removed], weights=p[removed, a[removed]], minlength=m)
        for j in rng.permutation(removed).tolist():
            k = int(np.argmin(ld + p[j]))               # vectorized repair scan over machines
            a[j] = k
            ld[k] += p[j, k]
        val = float(ld.max())
        if val <= cur_val:
            cur, cur_val = a, val
            if val < best_val:
                best, best_val = a.copy(), val
    return best, best_val


p = np.array([[2, 3], [4, 1], [3, 3], [2, 5]], dtype=float)
assign, cmax = lns_r_cmax(p, iters=200, q=2, seed=7)
print(assign, cmax)
# Expected: cmax = 4.0, matching the MIP optimum for the same instance in the exact-model
# section (the cheapest-machine start gives 7.0; LNS closes the gap within a few iterations).

Parameter guidance: set q to roughly 10-20% of n (minimum 2); larger q diversifies more per iteration but slows the repair. For due-date objectives, keep the same destroy logic but repair by best-position insertion into the per-machine EDD/WSPT sequences and evaluate incrementally; the acceptance rule then needs a real annealing or record-to-record criterion because tardiness landscapes have wide plateaus.

Instance generator

The hardness of unrelated-machine instances depends on the correlation structure of p_jk (machine-correlated and job-correlated classes are systematically harder for assignment-based bounds than uncorrelated ones). Due dates use the standard tardiness-factor / due-date-range scheme (TF/RDD, as in Potts & Van Wassenhove 1982): TF controls how tight due dates are on average, RDD how spread out they are.

import numpy as np


def gen_unrelated(n: int, m: int, corr: str = "uncorrelated", seed: int = 0) -> dict:
    """Unrelated-machine instance; corr in {'uncorrelated', 'machine', 'job'}."""
    rng = np.random.default_rng(seed)
    if corr == "uncorrelated":
        p = rng.integers(10, 101, size=(n, m))
    elif corr == "machine":
        speed = rng.integers(1, 11, size=m)
        p = rng.integers(10, 21, size=(n, m)) * speed       # slow/fast machines
    elif corr == "job":
        base = rng.integers(10, 101, size=(n, 1))
        p = base + rng.integers(0, 21, size=(n, m))         # intrinsically long/short jobs
    else:
        raise ValueError(f"unknown correlation class: {corr!r}")
    return {"n": n, "m": m, "p": p.astype(np.int64)}


def add_due_dates(data: dict, tf: float = 0.4, rdd: float = 0.8, seed: int = 1) -> dict:
    """Attach due dates and weights using the TF/RDD scheme (Potts & Van Wassenhove 1982).

    Due dates are uniform on [H(1 - TF - RDD/2), H(1 - TF + RDD/2)] where H estimates the
    average machine load. Larger TF = tighter dates; larger RDD = more spread.
    """
    rng = np.random.default_rng(seed)
    horizon = float(data["p"].mean(axis=1).sum()) / data["m"]
    lo = max(horizon * (1.0 - tf - rdd / 2.0), 0.0)
    hi = max(horizon * (1.0 - tf + rdd / 2.0), 1.0)
    d = rng.uniform(lo, hi, size=data["n"])
    w = rng.integers(1, 11, size=data["n"]).astype(np.int64)
    return {**data, "d": d, "w": w}


inst = add_due_dates(gen_unrelated(n=6, m=2, corr="uncorrelated", seed=42), tf=0.4, rdd=0.8, seed=42)
print(inst["p"].shape, inst["p"].min(), inst["p"].max(), inst["d"].round(1))
# Expected: p has shape (6, 2) with entries in [10, 100]; due dates fall inside
# [0.2 * H, 1.0 * H] where H is the average machine load. Same seeds reproduce the instance.

Hold the generator fixed (seeds recorded) across every experiment; tune algorithm parameters on one seed range and report results on a disjoint one.

Independent validator

Never trust the objective value an algorithm reports about itself. The validator recomputes feasibility and all objectives from the raw solution artifact, sharing no code with the model or the heuristic. Run it after every solver call and inside every experiment script.

import numpy as np


def validate_assignment(assign: np.ndarray, p: np.ndarray) -> tuple[list[str], float]:
    """Check an assignment-only schedule (R || Cmax) and recompute its makespan."""
    errors: list[str] = []
    n, m = p.shape
    a = np.asarray(assign)
    if a.shape != (n,):
        errors.append(f"assignment has shape {a.shape}, expected ({n},)")
        return errors, float("inf")
    if a.min() < 0 or a.max() >= m:
        errors.append("machine index out of range")
        return errors, float("inf")
    loads = np.bincount(a, weights=p[np.arange(n), a], minlength=m)
    return errors, float(loads.max())


def validate_sequences(seqs: list[list[int]], p: np.ndarray, d: np.ndarray,
                       w: np.ndarray) -> tuple[list[str], dict]:
    """Check a sequenced schedule (one job list per machine); recompute Cmax, Lmax, sum w_j T_j."""
    errors: list[str] = []
    n, m = p.shape
    if len(seqs) != m:
        errors.append(f"{len(seqs)} machine sequences given for {m} machines")
        return errors, {}
    flat = [j for seq in seqs for j in seq]
    if sorted(flat) != list(range(n)):
        errors.append("jobs are not partitioned exactly once across machines")
        return errors, {}
    c = np.zeros(n)
    for k, seq in enumerate(seqs):
        t = 0.0
        for j in seq:                                   # back-to-back: regular objectives
            t += float(p[j, k])
            c[j] = t
    tard = np.maximum(c - d, 0.0)
    objectives = {
        "cmax": float(c.max()),
        "lmax": float((c - d).max()),
        "total_weighted_tardiness": float((w * tard).sum()),
    }
    return errors, objectives


p = np.array([[3, 3], [2, 2], [2, 2]], dtype=float)
d = np.array([3.0, 2.0, 3.0])
w = np.array([2.0, 1.0, 1.0])
print(validate_sequences([[0], [1, 2]], p, d, w))
# Expected: ([], {'cmax': 4.0, 'lmax': 1.0, 'total_weighted_tardiness': 1.0}) -- the same
# value the disjunctive MIP reported, confirmed by code that shares nothing with the model.

A disagreement between the validator and the solver almost always means the model is wrong (big-M too small, a missing disjunction, or an objective that ignores idle time), not the validator. Debug the model first.

Advanced Techniques

PTAS for P || Cmax via dual approximation

Hochbaum & Shmoys (1987, "Using dual approximation algorithms for scheduling problems") binary search a target makespan T in [LB, 2 LB]. For each T they answer "can all jobs finish by (1 + eps) T?": jobs longer than eps * T are rounded down to the nearest multiple of eps^2 * T, leaving at most 1 / eps^2 distinct big-job sizes and at most 1 / eps big jobs per machine, so feasible machine configurations can be enumerated and counted by dynamic programming; small jobs are then poured greedily into the leftover capacity. The scheme is the canonical example of dual approximation (approximate the decision version, then search), and the same template extends to Q || Cmax. Use it to justify "near-optimal is cheap for identical machines" — but implement LPT + MIP, not the PTAS.

LP rounding for R || Cmax

Lenstra, Shmoys & Tardos (1990) solve a parametric LP in which x_jk is fixed to 0 whenever p_jk > T, then exploit the structure of extreme points: at most n + m variables are positive, so the bipartite support graph is a forest in which fractional jobs can be matched one-per-machine. Rounding along that matching yields a schedule of makespan at most 2T. They also prove no polynomial algorithm achieves a ratio below 3/2 unless P = NP. Practical takeaway: the parametric LP (or just the assignment LP) is a strong, cheap lower bound — report LNS gaps against it.

Time-indexed formulations for due-date objectives

With integer data and horizon T = max big_m, binaries x_jkt ("job j starts at time t on machine k") give the constraint set sum_{k,t} x_jkt = 1 per job plus, for each machine and time slot, "at most one job covers slot t." Any additively separable objective in completion times — weighted tardiness included — becomes linear. The LP relaxation dominates the big-M disjunctive relaxation (Dyer & Wolsey 1990; Sousa & Wolsey 1992) and often yields gaps an order of magnitude smaller; the price is O(n m T) variables, so scale processing times down first and consider solving the LP only as a bounding device, or pricing the columns out lazily.

Symmetry breaking on identical machines

Every solution on identical machines has m! mirror images obtained by permuting machine indices, which branch-and-bound explores separately unless told otherwise. Standard remedies: restrict job j to machines 0..min(j, m-1) (the first j+1 machines); or order machines by non-increasing load with named constraints load[k] >= load[k+1]; or fix the longest job on machine 0. Load-ordering interacts badly with machine eligibility constraints — prefer the job-index scheme when eligibility is present. On unrelated machines the symmetry vanishes by itself, which is one reason the R model often solves faster than a naive P model of equal size.

Warm starts from list schedules

The MIP never has to start cold: LPT (makespan) or per-machine WSPT/EDD after a greedy assignment (due-date objectives) produce a feasible incumbent in milliseconds. Set x[j,k].Start from the construction, derive the y_ij ordering binaries from the per-machine sequences, and the solver begins with an upper bound that prunes most of the weak big-M relaxation's tree. The same constructions seed the LNS (replace the cheapest-machine start) and the comparison baseline in experiments. Mechanics and pitfalls of MIP starts are covered in warm-starts-and-initial-solutions.

Practical Challenges

The disjunctive tardiness MIP stalls beyond ~20-30 jobs. This is expected: O(n^2 m) big-M rows with a near-vacuous LP relaxation. In order of preference: tighten big-M values per pair, add symmetry breaking, warm-start with a list schedule, switch to a CP-SAT interval model (see job-shop-scheduling for the pattern), or accept the LNS plus a time-indexed LP bound.

Identical machines blow up the branch-and-bound tree. Machine-permutation symmetry makes the solver re-prove the same subtree m! times. Add one symmetry-breaking family before touching any other parameter; on a P||Cmax model with m = 8 this alone can turn hours into seconds.

Due dates bolted onto an assignment-only model give wrong answers. For Cmax the per-machine sequence is irrelevant, so people reuse the assignment model for tardiness and compute completion times as machine loads. That assigns every job on a machine the same completion time. The validator's sequenced check exists precisely to catch this class of bug.

Release dates silently break the dispatching rules. SPT is optimal for 1 || sum C_j but 1 | r_j | sum C_j is strongly NP-hard, and EDD loses optimality for 1 | r_j | Lmax as well. Whenever r_j appears, re-derive the method; do not patch the rule with "skip unavailable jobs" and assume optimality survived.

Ties make runs irreproducible. Equal p_j / w_j ratios or equal due dates produce alternative optima, and an unstable sort picks among them by memory layout. Use stable sorts with a documented tie-break (the code above uses kind="stable" everywhere) and record seeds; otherwise two "identical" runs report different schedules and your regression tests flap.

Tardiness landscapes are flat. Once most jobs are on time, large neighborhoods of schedules share the same objective and a hill-climber stalls. Use a lexicographic surrogate (total weighted tardiness, then total completion time) for move evaluation, or an acceptance rule that tolerates sideways moves — the LNS above accepts ties for exactly this reason.

LPT's gap will not close on "few large jobs" instances. When two or three long jobs dominate, LPT's early commitments cannot be undone. Try MULTIFIT (Coffman, Garey & Johnson 1978), which binary-searches a bin capacity and packs by FFD, or run pairwise-swap local search on the LPT solution; if n is moderate, the assignment MIP settles the question outright.

The heuristic "beats" the exact optimum. A heuristic objective below the MIP optimum always means the two codes evaluate different problems — a too-small big-M cutting off true optima, a transposed p matrix, or due dates scaled differently. Run both solutions through the independent validator; whichever code disagrees with it is the broken one.

Tools & Libraries

Output Format

A complete answer to a parallel-machine scheduling task contains:

1. Problem classification. One line of three-field notation with the complexity status, e.g. "R | d_j | sum w_j T_j — strongly NP-hard; exact up to ~25 jobs, LNS beyond." If a polynomial rule applies, say so and skip the solver entirely.

2. Model summary (when a MIP is built):

   Item	Value
   Variables	x: n*m binary; y: n(n-1)/2 binary; s, c, t: n continuous
   Constraints	n assignment, n completion, mn(n-1) disjunctive, n tardiness
   Big-M	sum_j max_k p_jk, with per-pair tightening if applied
   Symmetry breaking	scheme used, or "none needed (unrelated machines)"

3. Solution-quality report. Best objective, lower bound and its source (LP value, preemptive bound, pairing bound), relative gap, runtime, solver status, and — for stochastic methods — the seed list with best/median/worst over seeds.
4. Schedule artifact. Per machine, the ordered job list with start and completion times (CSV columns: machine, position, job, start, completion, due, tardiness). Assignment-only output is acceptable solely for makespan objectives, and the answer must say why.
5. Validation line. Explicit confirmation that the independent validator reproduced the reported objective, e.g. "validator: feasible, total weighted tardiness 1431.0 = reported."
6. Convergence summary (heuristics): iterations run, iteration of last improvement, and initial-vs-final objective, so the reader can judge whether more time would help.

Questions to Ask

• One machine or several? If several: identical speeds, speed factors, or a full per-job per-machine time matrix?
• Which single objective — makespan, total (weighted) completion time, max lateness, number of tardy jobs, or (weighted) total tardiness? Or several, and in what priority?
• Are there release dates, sequence-dependent setup times, precedence constraints, or machine eligibility restrictions? Is preemption allowed?
• How large is the instance (n jobs, m machines), and is it solved once or repeatedly?
• Are processing times integer (or scalable to integers), and how large is the horizon?
• Is a provable optimum required, or is a certified gap against a lower bound acceptable?
• What is the time budget per solve, and is a Gurobi license available?
• What artifact does the downstream consumer need — machine assignment, a timed Gantt schedule, or just the objective value?

Related Skills

• milp-modeling-gurobi — when building, debugging, or extending the assignment and disjunctive MIPs: constraint-builder patterns, parameter setting, and status handling.
• large-neighborhood-search — when instances outgrow the exact models; full ALNS machinery (adaptive operator weights, regret insertion, acceptance criteria) for machine scheduling.
• job-shop-scheduling — when each job visits several machines in a routing; disjunctive models and the CP-SAT interval formulation that dominates there.
• warm-starts-and-initial-solutions — when feeding LPT/WSPT constructions into the MIP as starts or seeding the LNS population of starting points.