为什么IBM optimizer studio OPL给出的结果与docplex不同？

Question

这是一个优化问题，我正试图用我使用的opl代码来解决（稍微有点扭曲）。

opl代码为我提供了两种解决方案，即：{Product12，Product31}

当我使用docplex将此代码翻译为python语言时，我使用以下代码：

from docplex.mp.model import Model
from docplex.util.environment import get_environment

# ----------------------------------------------------------------------------
# Initialize the problem data
# ----------------------------------------------------------------------------

Categories_groups = {"Carbs": ["Meat","Milk"],"Protein":["Pasta","Bread"], "Fat": ["Oil","Butter"]}

Groups_Products = {"Meat":["Product11","Product12"], "Milk": ["Product21","Product22","Product23"], "Pasta": ["Product31","Product32"],
                   "Bread":["Product41","Product42"], "Oil":["Product51"],"Butter":["Product61","Product62"]}
Products_Prices ={"Product11":1,"Product12":1, "Product21":3,"Product22":3,"Product23":2,"Product31":1,"Product32":2,
                    "Product41":1,"Product42":3, "Product51": 1,"Product61":2,"Product62":1}

Uc={"Protein":1,"Carbs": 0, "Fat": 0}

Ug = {"Meat": 0.8, "Milk": 0.2, "Pasta": 0.1, "Bread": 1, "Oil": 0.01, "Butter": 0.6}


budget=2

def build_userbasket_model(**kwargs):


    allcategories = Categories_groups.keys()

    allgroups = Groups_Products.keys()

    allproducts = Products_Prices.keys()

    # Model
    mdl = Model(name='userbasket', **kwargs)
    z = mdl.binary_var_dict(allproducts, name='%s')

    xg = {g:  mdl.sum(z[p] for p in Groups_Products[g]) for g in allgroups}

    xc = {c: 1 <= mdl.sum(xg[g] for g in Categories_groups[c]) for c in allcategories}


    mdl.add_constraint(mdl.sum(Products_Prices[p] * z[p] for p in allproducts) <= budget)

    for g in allgroups:
        mdl.add_constraint(xg[g]==1 )


    for c in allcategories:
        mdl.add_constraint(Uc[c] == xc[c])



    mdl.maximize(mdl.sum(Uc[c] * xc[c] for c in allcategories) + mdl.sum(
        xg[g] * Uc[c] * Ug[g]  for c in allcategories for g in Categories_groups[c]  ))


    return mdl

if __name__ == '__main__':
    """DOcplexcloud credentials can be specified with url and api_key in the code block below.

    Alternatively, Context.make_default_context() searches the PYTHONPATH for
    the following files:

        * cplex_config.py
        * cplex_config_<hostname>.py
        * docloud_config.py (must only contain context.solver.docloud configuration)

    These files contain the credentials and other properties. For example,
    something similar to::

       context.solver.docloud.url = "https://docloud.service.com/job_manager/rest/v1"
       context.solver.docloud.key = "example api_key"
    """
    url = None
    key = None

    mdl = build_userbasket_model()

    # will use IBM Decision Optimization on cloud.
    if not mdl.solve(url=url, key=key):
        print("*** Problem has no solution")
    else:
        mdl.float_precision = 3
        print("* model solved as function:")

        mdl.print_solution()

        '''
        Solution displayed using the line of code above

        solution = mdl.solution



        for index, dvar in enumerate(solution.iter_variables()):
            print index, dvar.to_string(), solution[dvar], solution.get_var_value(dvar)



        # Save the CPLEX solution as "solution.json" program output
        with get_environment().get_output_stream("solution.json") as fp:
            mdl.solution.export(fp, "json")

我明白了：

***问题没有解决方案

我不明白为什么我有不同的结果，有人能帮我吗？

先谢谢你。

当做

Answer 1

如果一个问题提供了一个可行的解决方案，而另一个问题被认为是不可行的，那么很可能您没有解决同一个问题。您可以做以下几件事：

>

将模型从OPL和Python代码导出为LP文件，并比较这两个模型。

使用冲突细化器了解为何Python模型被认为不可行。从这里，您可以知道Python模型的问题在哪里。