Optimization 为什么IBM optimizer studio OPL给出的结果与docplex不同?
下面是我正在尝试解决的问题(稍微有点扭曲,使用我使用的代码) opl代码为我提供了两种解决方案,即:{Product12,Product31} 当我使用docplex将其转换为python语言时,使用以下代码:Optimization 为什么IBM optimizer studio OPL给出的结果与docplex不同?,optimization,mathematical-optimization,cplex,docplex,docplexcloud,Optimization,Mathematical Optimization,Cplex,Docplex,Docplexcloud,下面是我正在尝试解决的问题(稍微有点扭曲,使用我使用的代码) opl代码为我提供了两种解决方案,即:{Product12,Product31} 当我使用docplex将其转换为python语言时,使用以下代码: from docplex.mp.model import Model from docplex.util.environment import get_environment # -----------------------------------------------------
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")
来自docplex.mp.model导入模型
从docplex.util.environment导入获取\u环境
# ----------------------------------------------------------------------------
#初始化问题数据
# ----------------------------------------------------------------------------
类别组={“碳水化合物”:[“肉”、“牛奶”],“蛋白质”:[“面食”、“面包”],“脂肪”:[“油”、“黄油”]}
组产品={“肉类”:[“产品11”、“产品12”],“牛奶”:[“产品21”、“产品22”、“产品23”],“面食”:[“产品31”、“产品32”],
“面包”:[“产品41”、“产品42”]、“油”:[“产品51”]、“黄油”:[“产品61”、“产品62”]}
产品价格={“产品11”:1,“产品12”:1,“产品21”:3,“产品22”:3,“产品23”:2,“产品31”:1,“产品32”:2,
“产品41”:1,“产品42”:3,“产品51”:1,“产品61”:2,“产品62”:1}
Uc={“蛋白质”:1,“碳水化合物”:0,“脂肪”:0}
Ug={“肉”:0.8,“牛奶”:0.2,“面食”:0.1,“面包”:1,“油”:0.01,“黄油”:0.6}
预算=2
def build_USERBACKET_模型(**kwargs):
allcategories=Categories\u groups.keys()
allgroups=Groups\u Products.keys()
allproducts=产品价格.keys()
#模型
mdl=Model(name='userbasket',**kwargs)
z=mdl.binary\u var\u dict(所有产品,名称=“%s”)
xg={g:mdl.sum(z[p]表示群中的p_乘积[g])表示群中的g}
xc={c: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,那么很可能你没有解决同一个问题。
您可以做以下几件事:
仔细检查Python代码是否正确
从OPL和Python代码中将模型导出为LP文件,并比较这两个模型
使用冲突提炼器了解为什么Python模型被认为不可行。从中,您可以知道Python模型的问题在哪里
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:
仔细检查Python代码是否正确
从OPL和Python代码中将模型导出为LP文件,并比较这两个模型
使用冲突提炼器了解为什么Python模型被认为不可行。从中,您可以知道Python模型的问题在哪里