49 lines
2.3 KiB
Python
49 lines
2.3 KiB
Python
import pulp
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def optimize_feed(requirements, ingredients, result_fields, optimization_type='min'):
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"""
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优化饲料配方，计算最低/最高目标值的原料比例
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:param requirements: 猪营养需求字典，如 {'蛋白_下限': 16, '能量_下限': 12, '纤维_上限': 8}
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:param ingredients: 原料数据字典，如 {'构树叶': {'蛋白': 18, '能量': 10, '纤维': 5, '价格': 500}}
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:param result_fields: 结果字段列表，如 ['比例', '成本']
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:param optimization_type: 'min'（最小化，如成本）或 'max'（最大化，如消化率）
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:return: 字典，含结果字段和值，如 {'构树叶': {'比例': 0.2, '成本': 100}, '总成本': 1475}
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"""
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# 创建模型
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sense = pulp.LpMinimize if optimization_type == 'min' else pulp.LpMaximize
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model = pulp.LpProblem("Feed_Optimization", sense)
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# 决策变量：原料比例（0-1）
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x = pulp.LpVariable.dicts("比例", ingredients, lowBound=0, upBound=1)
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# 目标函数：优化指定字段（如价格）
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target_field = next(f for f in ingredients[list(ingredients.keys())[0]] if '价格' in f or '成本' in f) # 默认优化价格
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model += pulp.lpSum([x[i] * ingredients[i][target_field] for i in ingredients]), "目标"
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# 约束1：总比例=100%
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model += pulp.lpSum([x[i] for i in ingredients]) == 1, "总比例"
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# 约束2：营养需求（下限/上限）
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for req, value in requirements.items():
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if '下限' in req:
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nutrient = req.replace('_下限', '')
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model += pulp.lpSum([x[i] * ingredients[i][nutrient] for i in ingredients]) >= value, req
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elif '上限' in req:
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nutrient = req.replace('_上限', '')
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model += pulp.lpSum([x[i] * ingredients[i][nutrient] for i in ingredients]) <= value, req
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# 求解
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model.solve()
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# 构建结果
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if pulp.LpStatus[model.status] != 'Optimal':
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return {"错误": "无解！检查原料数据或需求（比如纤维全超上限？）"}
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result = {}
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total_target = pulp.value(model.objective)
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result['总' + target_field] = round(total_target, 2)
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for i in ingredients:
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result[i] = {field: round(pulp.value(x[i]) * 100, 2) if field == '比例' else round(
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pulp.value(x[i]) * ingredients[i][target_field], 2) for field in result_fields}
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return result
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