如何使用docplex（python）对优化问题中的约束进行建模？

我需要解决一个类似于背包问题的优化问题。我在这篇文章中详细介绍了优化问题： 我实际上需要使用python而不是OPL，因此我安装了docplex和clpex包，以便使用cplex优化框架

下面是我想使用docplex转换为python的OPL代码

{string} categories=...;
{string} groups[categories]=...;

{string} allGroups=union (c in categories) groups[c];

{string} products[allGroups]=...;
{string} allProducts=union (g in allGroups) products[g];

float prices[allProducts]=...;

int Uc[categories]=...;
float Ug[allGroups]=...;

float budget=...;

dvar boolean z[allProducts]; // product out or in ?

dexpr int xg[g in allGroups]=(1<=sum(p in products[g]) z[p]); 

dexpr int xc[c in categories]=(1<=sum(g in groups[c]) xg[g]);

maximize 
sum(c in categories) Uc[c]*xc[c]+
sum(c in categories) sum(g in groups[c]) Uc[c]*Ug[g]*xg[g];
subject to
{
ctBudget:
    sum(p in allProducts) z[p]*prices[p]<=budget;
}

{string} solution={p | p in allProducts : z[p]==1};

execute
{
writeln("solution = ",solution);
}

{string}类别=。。。；
{string}组[类别]=。。。；
{string}allGroups=联合（类别中的c）组[c]；
{string}乘积[allGroups]=。。。；
{string}allProducts=union（所有组中的g）products[g]；
浮动价格[所有产品]=。。。；
int Uc[类别]=。。。；
浮动Ug[所有组]=。。。；
浮动预算=。。。；
dvar布尔z[所有产品]；//产品出口还是进口？
dexpr int xg[g in allGroups]=（1如果我正确理解了您的数据模型（我不确定您的示例中的数据是否一致（类别组和组产品没有相同的“组”值集合）），则决策变量和表达式的定义如下：

z = mdl.binary_var_dict(allProducts, name='z([%s])')
xg = {g: 1 <= 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}

z=mdl.binary\u var\u dict（所有产品，name='z（[%s]））
xg={g:1非常感谢您的回答和宝贵的建议。我已经更新了我的问题以添加新变量，现在我的代码工作正常。我只有这个警告“警告：没有索引为0的对象”同样的警告一直持续到索引8。你知道我为什么会有这个问题吗？我在谷歌上搜索了这个问题，但找不到任何答案。提前谢谢你。问候。
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":4, "Product21":1,"Product22":3,"Product23":2,"Product31":4,"Product32":2,
                    "Product41":1,"Product42":3, "Product51": 1,"Product61":2,"Product62":1}




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

Ug = {"Meat": 0.8, "Milk": 0.2, "Pasta": 0.1, "Bread": 1, "Oil": 0.01, "Butter": 0.6}
budget=3;


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='z([%s])')

    xg = {g: 1 <= 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)
    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]))
    mdl.solve()



    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()

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

z = mdl.binary_var_dict(allProducts, name='z([%s])')
xg = {g: 1 <= 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}