# Local Dev Sandbox for the solver # Useful for testing some concepts and functionality # and offers a much faster feedback loop than the usual end-to-end process in Local Dev # # How to use: # 1. run `compose exec meazure-solver bash` # 2. run `python` # 3. import this file in the python repl by `from services.solver_sandbox import SolverSandbox` # 4. run any of the methds below e.g. `SolverSandbox.yas_elastic()` import logging from pulp import LpProblem, LpVariable, LpInteger, LpMinimize, LpMaximize, LpAffineExpression, LpConstraint, LpStatus, lpSum from services.loft_service import LoftService class SolverSandbox: def loft_service(body): LoftService(body).process() def yosh_loop(): Items = [1,2,3,4,5] tif = { 1: 0.2, 2: 0.5, 3: 0.3, 4: 0.8, 5: 0.1 } iif = { 1: 0.09, 2: 0.2, 3: 0.113, 4: 0.3, 5: 0.1 } drift = 0.0 drift_limit = 0.2 iif_target = 0.5 tif_target = 0.9 item_vars = LpVariable.dicts("Item", Items, cat="Binary") while drift <= drift_limit: prob = LpProblem("tif_tcc_test", LpMinimize) prob += lpSum([(tif[i] + iif[i]) * item_vars[i] for i in Items]), "TifTccSum" prob += lpSum([item_vars[i] for i in Items]) == 3, "TotalItems" prob += lpSum([tif[i] * item_vars[i] for i in Items]) >= tif_target - (tif_target * drift), 'TifMin' prob += lpSum([tif[i] * item_vars[i] for i in Items]) <= tif_target + (tif_target * drift), 'TifMax' prob += lpSum([iif[i] * item_vars[i] for i in Items]) >= iif_target - (iif_target * drift), 'TccMin' prob += lpSum([iif[i] * item_vars[i] for i in Items]) <= iif_target + (iif_target * drift), 'TccMax' prob.solve() print(prob) if LpStatus[prob.status] == "Infeasible": print('attempt infeasible') for v in prob.variables(): print(v.name, "=", v.varValue) drift += 0.02 else: print(f"solution found with drift of {drift}!") for v in prob.variables(): print(v.name, "=", v.varValue) break def yas_elastic(tif_target = 140.0): # 140 is the optimal Items = [1,2,3,4,5] # For TIF target tif = { 1: 10, 2: 20, 3: 40, 4: 60, 5: 80 } iif = { 1: 10, 2: 20, 3: 30, 4: 50, 5: 70 } # --- items = LpVariable.dicts('Item', Items, cat='Binary') drift = 0 max_drift = 10 # 10% elasticity while drift <= max_drift: drift_percent = drift / 100 problem = LpProblem('TIF_TCC', LpMinimize) # objective function problem += lpSum([(tif[i] + iif[i]) * items[i] for i in Items]) # Constraint 1 problem += lpSum([items[i] for i in Items]) == 3, 'TotalItems' print(f"Calculating TIF target of {tif_target} with drift of {drift}%") # Our own "Elastic Constraints" problem += lpSum( [(tif[i] + iif[i]) * items[i] for i in Items] ) >= tif_target - (tif_target * drift_percent), 'TifIifMin' problem += lpSum( [(tif[i] + iif[i]) * items[i] for i in Items] ) <= tif_target + (tif_target * drift_percent), 'TifIifMax' problem.solve() if LpStatus[problem.status] == 'Infeasible': print(f"attempt infeasible for drift of {drift}") for v in problem.variables(): print(v.name, "=", v.varValue) print(problem.objective.value()) print(problem.constraints) print(problem.objective) drift += 1 else: print(f"solution found with drift of {drift}!") for v in problem.variables(): print(v.name, "=", v.varValue) print(problem.constraints) print(problem.objective) break # Implementation of the Whiskas Cat problem, with elastic constraints # https://www.coin-or.org/PuLP/CaseStudies/a_blending_problem.html # https://stackoverflow.com/questions/27278691/how-can-an-elastic-subproblem-in-pulp-be-used-as-a-constraint?noredirect=1&lq=1 def whiskas(): # Creates a list of the Ingredients Ingredients = ['CHICKEN', 'BEEF', 'MUTTON', 'RICE', 'WHEAT', 'GEL'] # A dictionary of the costs of each of the Ingredients is created costs = {'CHICKEN': 0.013, 'BEEF': 0.008, 'MUTTON': 0.010, 'RICE': 0.002, 'WHEAT': 0.005, 'GEL': 0.001} # A dictionary of the protein percent in each of the Ingredients is created proteinPercent = {'CHICKEN': 0.100, 'BEEF': 0.200, 'MUTTON': 0.150, 'RICE': 0.000, 'WHEAT': 0.040, 'GEL': 0.000} # A dictionary of the fat percent in each of the Ingredients is created fatPercent = {'CHICKEN': 0.080, 'BEEF': 0.100, 'MUTTON': 0.110, 'RICE': 0.010, 'WHEAT': 0.010, 'GEL': 0.000} # A dictionary of the fibre percent in each of the Ingredients is created fibrePercent = {'CHICKEN': 0.001, 'BEEF': 0.005, 'MUTTON': 0.003, 'RICE': 0.100, 'WHEAT': 0.150, 'GEL': 0.000} # A dictionary of the salt percent in each of the Ingredients is created saltPercent = {'CHICKEN': 0.002, 'BEEF': 0.005, 'MUTTON': 0.007, 'RICE': 0.002, 'WHEAT': 0.008, 'GEL': 0.000} logging.info('Running Test...') # create problem problem = LpProblem("The Whiskas Problem", LpMinimize) # A dictionary called 'ingredient_vars' is created to contain the referenced Variables ingredient_vars = LpVariable.dicts("Ingr", Ingredients, 0) # set objective problem += lpSum([costs[i]*ingredient_vars[i] for i in Ingredients]), "Total Cost of Ingredients per can" # The five constraints are added to 'prob' problem += lpSum([ingredient_vars[i] for i in Ingredients]) == 100, "PercentagesSum" problem += lpSum([proteinPercent[i] * ingredient_vars[i] for i in Ingredients]) >= 8.0, "ProteinRequirement" problem += lpSum([fatPercent[i] * ingredient_vars[i] for i in Ingredients]) >= 6.0, "FatRequirement" problem += lpSum([fibrePercent[i] * ingredient_vars[i] for i in Ingredients]) <= 2.0, "FibreRequirement" problem += lpSum([saltPercent[i] * ingredient_vars[i] for i in Ingredients]) <= 0.4, "SaltRequirement" # ELASTICIZE # c6_LHS_A = LpAffineExpression([ingredient_vars]) c6_LHS = LpAffineExpression([(ingredient_vars['GEL'],1), (ingredient_vars['BEEF'],1)]) c6= LpConstraint(e=c6_LHS, sense=-1, name='GelBeefTotal', rhs=30) c6_elastic = c6.makeElasticSubProblem(penalty = 100, proportionFreeBound = .10) problem.extend(c6_elastic) print(problem) # solve problem problem.solve() # The status of the solution is printed to the screen print("Status:", LpStatus[problem.status]) # Each of the variables is printed with it's resolved optimum value for v in problem.variables(): print(v.name, "=", v.varValue) # The optimised objective function value is printed to the screen print("Total Cost of Ingredients per can = ", problem.objective.value())