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https://github.com/Hestia-Homes/Model.git
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Added optimisation example
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Khalim Conn-Kowlessar
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commit
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1 changed files with 113 additions and 11 deletions
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@ -1,4 +1,5 @@
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from mip import Model, xsum, maximize, BINARY
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from pprint import pprint
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# Example parts
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wall = [
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@ -28,10 +29,8 @@ roof = [
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# The third sum (and the second constraint) ensures that at most one part from each group is selected
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# The last constraint ensures that the decision variables are binary
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C = 4000
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# group all the parts
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groups = [wall, floor, roof]
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components = [wall, floor, roof]
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class GainOptimiser:
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@ -39,8 +38,9 @@ class GainOptimiser:
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This class is used maximise gain, given a constrained cost
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"""
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def __init__(self, components):
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def __init__(self, components, max_cost):
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self.components = components
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self.max_cost = max_cost
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self.m = None
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self.variables = []
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self.solution = []
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@ -54,7 +54,8 @@ class GainOptimiser:
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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 groups
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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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@ -63,7 +64,8 @@ class GainOptimiser:
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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 = maximize(
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xsum(
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component['gain'] * var for group, group_vars in zip(groups, self.variables) for component, var in
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component['gain'] * 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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@ -73,9 +75,9 @@ class GainOptimiser:
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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['cost'] * var for group, group_vars in zip(groups, self.variables) for item, var in
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item['cost'] * 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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) <= C
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) <= self.max_cost
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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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@ -88,11 +90,111 @@ class GainOptimiser:
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self.m.optimize()
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self.solution = [
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item for group, group_vars in zip(groups, self.variables) for item, var in zip(group, group_vars) if
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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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solution_gain = self.m.objective.x
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solution_cost = sum([component['cost'] for component in self.solution])
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self.solution_gain = self.m.objective.x
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self.solution_cost = sum([component['cost'] for component in self.solution])
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opt = GainOptimiser(components, max_cost=4000)
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# Setup the knackpack problem
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# This sets the objective & contraints
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opt.setup()
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# Solve the problem
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opt.solve()
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pprint(opt.solution)
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print("total cost:", opt.solution_cost)
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print("total gain:", opt.solution_gain)
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# A bigger problem:
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wall = [
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{"id": 1, "cost": 2000, "gain": 5, "type": "wall"},
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{"id": 2, "cost": 2300, "gain": 6, "type": "wall"},
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{"id": 3, "cost": 2200, "gain": 5.5, "type": "wall"},
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{"id": 4, "cost": 2500, "gain": 6.2, "type": "wall"},
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{"id": 5, "cost": 2100, "gain": 5.1, "type": "wall"},
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{"id": 6, "cost": 2400, "gain": 6.1, "type": "wall"},
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{"id": 7, "cost": 2000, "gain": 5.2, "type": "wall"}
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]
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floor = [
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{"id": 1, "cost": 1500, "gain": 3, "type": "floor"},
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{"id": 2, "cost": 1600, "gain": 3.1, "type": "floor"},
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{"id": 3, "cost": 1550, "gain": 3.2, "type": "floor"},
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{"id": 4, "cost": 1650, "gain": 3.3, "type": "floor"},
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{"id": 5, "cost": 1500, "gain": 3.4, "type": "floor"},
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{"id": 6, "cost": 1550, "gain": 3.5, "type": "floor"},
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{"id": 7, "cost": 1600, "gain": 3.6, "type": "floor"}
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]
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roof = [
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{"id": 1, "cost": 1000, "gain": 2, "type": "roof"},
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{"id": 2, "cost": 1100, "gain": 2.3, "type": "roof"},
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{"id": 3, "cost": 1200, "gain": 2.6, "type": "roof"},
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{"id": 4, "cost": 1300, "gain": 2.9, "type": "roof"},
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{"id": 5, "cost": 1100, "gain": 2.5, "type": "roof"},
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{"id": 6, "cost": 1200, "gain": 2.7, "type": "roof"},
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{"id": 7, "cost": 1300, "gain": 2.8, "type": "roof"}
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]
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heating = [
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{"id": 1, "cost": 3000, "gain": 7, "type": "heating"},
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{"id": 2, "cost": 3200, "gain": 7.2, "type": "heating"},
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{"id": 3, "cost": 3100, "gain": 7.1, "type": "heating"},
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{"id": 4, "cost": 3300, "gain": 7.3, "type": "heating"},
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{"id": 5, "cost": 3000, "gain": 7.4, "type": "heating"}
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]
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hot_water = [
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{"id": 1, "cost": 2500, "gain": 6.5, "type": "hot water"},
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{"id": 2, "cost": 2600, "gain": 6.6, "type": "hot water"},
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{"id": 3, "cost": 2500, "gain": 6.7, "type": "hot water"},
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{"id": 4, "cost": 2700, "gain": 6.8, "type": "hot water"},
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{"id": 5, "cost": 2500, "gain": 6.9, "type": "hot water"}
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]
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solar = [
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{"id": 1, "cost": 5000, "gain": 10, "type": "solar"},
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{"id": 2, "cost": 5500, "gain": 11, "type": "solar"},
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{"id": 3, "cost": 5300, "gain": 10.5, "type": "solar"},
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{"id": 4, "cost": 5200, "gain": 10.2, "type": "solar"},
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{"id": 5, "cost": 5400, "gain": 10.8, "type": "solar"}
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]
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heat_pumps = [
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{"id": 1, "cost": 4000, "gain": 9, "type": "heat pumps"},
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{"id": 2, "cost": 4200, "gain": 9.2, "type": "heat pumps"},
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{"id": 3, "cost": 4100, "gain": 9.1, "type": "heat pumps"},
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{"id": 4, "cost": 4300, "gain": 9.3, "type": "heat pumps"},
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{"id": 5, "cost": 4000, "gain": 9.4, "type": "heat pumps"}
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]
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components2 = [
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wall,
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floor,
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roof,
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heating,
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hot_water,
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solar,
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heat_pumps
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]
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opt2 = GainOptimiser(components2, max_cost=22000)
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# Setup
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opt2.setup()
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# Solve the problem
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opt2.solve()
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pprint(opt2.solution)
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print("total cost:", opt2.solution_cost)
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print("total gain:", opt2.solution_gain)
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