Purpose is to know how it looks solving linear programs using libraries and solvers.
Reference : https://pythonhosted.org/PuLP/CaseStudies/a_blending_problem.html
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| \* Minimize food cost *\ | |
| Minimize | |
| Total_Cost: 0.008 Beef_percentage + 0.013 chicken_percentag | |
| Subject To | |
| Fat_Constraint: 0.1 Beef_percentage + 0.08 chicken_percentag >= 6 | |
| Fiber_Constrint: 0.005 Beef_percentage + 0.001 chicken_percentag <= 2 | |
| Protine_Constraint: 0.2 Beef_percentage + 0.1 chicken_percentag >= 8 | |
| Salt_constraint: 0.005 Beef_percentage + 0.002 chicken_percentag <= 0.4 | |
| Total_Sum: Beef_percentage + chicken_percentag = 100 | |
| End |
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| """ | |
| THis is taken from https://pythonhosted.org/PuLP/CaseStudies/a_blending_problem.html | |
| """ | |
| from pulp import * | |
| problem = LpProblem("Minimize food cost", LpMinimize) | |
| x1 = LpVariable("chicken_percentag", lowBound=0) | |
| x2 = LpVariable("Beef_percentage", lowBound=0, upBound=None) | |
| problem += 0.013*x1 + 0.008*x2, "Total Cost" | |
| problem += x1 + x2 == 100, "Total Sum" | |
| problem += 0.1*x1 + 0.2*x2 >=8, "Protine Constraint" | |
| problem += 0.08*x1 + 0.1*x2 >=6, "Fat Constraint" | |
| problem += 0.001*x1 + 0.005*x2 <=2, "Fiber Constrint" | |
| problem += 0.002*x1 + 0.005*x2 <=0.4, "Salt constraint" | |
| problem.writeLP("minimize_food_cost.lp") | |
| problem.solve() | |
| print "Solution found was :", LpStatus[problem.status] | |
| print "Here is the proportional of chick and beef that should be used" | |
| for v in problem.variables(): | |
| print "\t",v.name, " : ", v.varValue | |
| print "Cost will be : ", value(problem.objective) |
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| Solution found was : Optimal | |
| Here is the proportional of chick and beef that should be used | |
| Beef_percentage : 66.666667 | |
| chicken_percentag : 33.333333 | |
| Cost will be : 0.966666665 |
Data Stories 