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68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
from mip import Model, xsum, minimize, BINARY
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class CostOptimiser:
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"""
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This class is used to minimise cost, given a constrained minimum gain
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"""
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def __init__(self, components, min_gain):
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self.components = components
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self.min_gain = min_gain
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self.m = None
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self.variables = []
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self.solution = []
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self.solution_cost = None
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self.solution_gain = None
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def setup(self):
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# Initialize Model
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self.m = Model("knapsack")
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# Create variables
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self.variables = [
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[self.m.add_var(var_type=BINARY, name=str(component["id"])) for component in group] for group in
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self.components
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]
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# Set objective
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# This objective is to minimize
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# cost_ig * x_ig, where cost_ig represents the cost for ith part in group g
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# and x_ig is the binary decision variable for the ith part in group g
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self.m.objective = minimize(
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xsum(
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component['cost'] * var for group, group_vars in zip(self.components, self.variables) for component, var
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in
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zip(group, group_vars)
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)
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)
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# Add constraints
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# This constrain ensures that sum of gain_ig * x_ig >= min_gain, where gain_ig represents the gain for the ith
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# component
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# in group g, and x_ig is the binary decision variable for the ith component in group g
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self.m += xsum(
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item['gain'] * var for group, group_vars in zip(self.components, self.variables) for item, var in
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zip(group, group_vars)
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) >= self.min_gain
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# At most one item from each group
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# This constraint ensures that at most one item from each group is selected
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# This is expressed by summing up the decision variables for each group and ensuring that the sum is <= 1
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for group_vars in self.variables:
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self.m += xsum(var for var in group_vars) <= 1
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def solve(self):
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# Solve the problem
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self.m.optimize()
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self.solution = [
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item for group, group_vars in zip(self.components, self.variables) for item, var in zip(group, group_vars)
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if
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var.x >= 0.99
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]
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# Get the selected items
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self.solution_cost = self.m.objective.x
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self.solution_gain = sum([component['gain'] for component in self.solution])
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