使用 OR 工具的设施位置答案

【问题标题】：facility location with OR-tools使用 OR 工具的设施位置
【发布时间】：2021-05-30 11:47:34
【问题描述】：

我正在尝试使用 OR 工具编写设施位置 MIP 解决方案。我从这里翻译了一个 Scip 解决方案：

https://scipbook.readthedocs.io/en/latest/flp.html 但我得到一个只有零的表意味着没有解决方案.. 问题的框架或/和我在这里使用 OR 工具的方式应该有效吗？

    def or_tools_scip_mine(facilities, customers, time_limit=None):

    import numpy
    import datetime

    if time_limit is None:
        time_limit = 1000 * 60  # 1 minute


    solver = pywraplp.Solver.CreateSolver('SCIP')


    customer_count = range(len(customers))
    facility_count = range(len(facilities))
    x =[[] for _ in range(len(customers))]
    y = []
    facility_capacities=[facilities[i][2] for i in facility_count]
    facility_setup_costs = [facilities[i][1] for i in facility_count]
    demands=[customers[i][1] for i in customer_count]
    c=dist_matrix(facilities,customers)

    for j in facility_count:
        y.append(solver.BoolVar("y(%s)" % j))
        for i in customer_count:
            x[i].append(solver.BoolVar("x(%s,%s)" % (i, j)))

    for i in customer_count:
        solver.Add(solver.Sum(x[i][j] for j in facility_count) <= demands[i])#, "Demand(%s)" % i
    for j in facility_count:
        solver.Add(solver.Sum(x[i][j] for i in customer_count) <= facility_capacities[j] * y[j])#, "Capacity(%s)" % j)
    for j in facility_count:
        for i in customer_count:
            solver.Add(x[i][j] <= demands[i] * y[j])
    a=solver.Sum((facility_setup_costs[j] * y[j] for j in facility_count))
    b=solver.Sum((c[i, j] * x[i][j] for i in customer_count for j in facility_count))
    func_=solver.Sum([a,b])
    solver.Minimize(func_)

    solver.set_time_limit(time_limit)
    result_status = solver.Solve()
    print(result_status)
    val = solver.Objective().Value()

    x_val = [[] for _ in range(len(customers))]  
    solution = []
    for j in range(len(facilities)):
        for i in range(len(customers)):
            x_val[i].append(int(x[i][j].solution_value()))
    x_val = numpy.array(x_val)
    for j in range(len(customers)):
        solution.append(numpy.where(x_val[:, j] == 1)[0][0])

    return val, solution

the  Error:
solution.append(numpy.where(x_val[:, j] == 1)[0][0])
IndexError: index 0 is out of bounds for axis 0 with size 0

【问题讨论】：

标签： python optimization or-tools scip discrete-optimization

【解决方案1】：

在代码中的 CreateSolver 行之后添加solver.EnableOutput()。这将使您更深入地了解正在发生的事情。

以下是帮助求解器的参数和状态。如果您需要更多详情点击此文档link

    print('Number of variables = %d' % solver.NumVariables())
    print('Number of constraints = %d' % solver.NumConstraints())                
    print('The Optimal Objective value =',solver.Objective().Value())
    print('Total Iterations:',solver.iterations())
    print('Total Nodes:',solver.nodes())
    print('Total number of Variables:',solver.NumVariables())
    print(pywraplp.Solver.FEASIBLE)
    print(pywraplp.Solver.MODEL_INVALID)
    print(pywraplp.Solver.OPTIMAL)
    print(pywraplp.Solver.INFEASIBLE)
    print(pywraplp.Solver.UNBOUNDED)

【讨论】：