The problem I want to solve is the same as this text

For example, I have 3 works and 4 tasks. The costs matrix is shown below.

costs = [
        [90, 80, 75, 70],
        [35, 85, 55, 65],
        [125, 95, 90, 95],
    ]

I need to assign each worker with one task. But I don’t need to minimiz the total cost. Instead, I want to minimiz their extreme range (means that I want to make their working hours as close as possible ).

For example, if work0->task0 work1->task1 work2->task2, then their range is 5, the smallest. This is what I want to get.

I have tried to ues the MIP solver and CP-SAT solver. But there will always be mistakes.
The code below shows how MIP solver deal this problem.

from ortools.linear_solver import pywraplp

def main():
    # Data
    costs = [
        [90, 80, 75, 70],
        [35, 85, 55, 65],
        [125, 95, 90, 95],
    ]
    num_workers = len(costs)
    num_tasks = len(costs[0])

    # Solver
    # Create the mip solver with the SCIP backend.
    solver = pywraplp.Solver.CreateSolver("SCIP")

    if not solver:
        return

    # Variables
    # x[i, j] is an array of 0-1 variables, which will be 1
    # if worker i is assigned to task j.
    x = {}
    for i in range(num_workers):
        for j in range(num_tasks):
            x[i, j] = solver.IntVar(0, 1, "")

    # Constraints
    # Each worker is assigned to exactly one 1 task.
    for i in range(num_workers):
        solver.Add(solver.Sum([x[i, j] for j in range(num_tasks)]) == 1)

    # Each task is assigned to at most one worker.
    for j in range(num_tasks):
        solver.Add(solver.Sum([x[i, j] for i in range(num_workers)]) <= 1)

    # Objective
    objective_terms = []
    for i in range(num_workers):
        for j in range(num_tasks):
            objective_terms.append(costs[i][j] * x[i, j])

    objective_cost = max(objective_terms)-min(objective_terms)
    solver.Minimize(objective_cost)

    # Solve
    print(f"Solving with {solver.SolverVersion()}")
    status = solver.Solve()

    # Print solution.
    if status == pywraplp.Solver.OPTIMAL or status == pywraplp.Solver.FEASIBLE:
        print(f"Total cost = {solver.Objective().Value()}n")
        for i in range(num_workers):
            for j in range(num_tasks):
                # Test if x[i,j] is 1 (with tolerance for floating point arithmetic).
                if x[i, j].solution_value() > 0.5:
                    print(f"Worker {i} assigned to task {j}." + f" Cost: {costs[i][j]}")
    else:
        print("No solution found.")


if __name__ == "__main__":
    main()

Running this, the following is thrown:

Operators "<" and ">" not supported with the linear solver

The key code is this:

objective_cost = max(objective_terms)-min(objective_terms)
    solver.Minimize(objective_cost)

I know why it’s wrong. The elements’ data type in the list objective_terms are not int. But I don’t kown to modify it. Alos,I can’t think of any other way to deal this.

Is this a problem type I can use or-tools to solve?

Many thanks for your kind and warm help.