from opt.smm.basis import * from opt.utils import * from opt.smm.solver import * class Aggregation(BaseOpt): def __init__(self, config, part_data, step_data, feeder_data=pd.DataFrame(columns=['slot', 'part'])): super().__init__(config, part_data, step_data, feeder_data) self.feeder_assigner = FeederAssignOpt(config, part_data, step_data) def optimize(self, hinter=True): # === phase 0: data preparation === M = 1000 # a sufficient large number a, b = 1, 6 # coefficient part_list, nozzle_list = defaultdict(int), defaultdict(int) cpidx_2_part, nzidx_2_nozzle = {}, {} for _, data in self.step_data.iterrows(): part = data.part if part not in cpidx_2_part.values(): cpidx_2_part[len(cpidx_2_part)] = part part_list[part] += 1 idx = self.part_data[self.part_data['part'] == part].index.tolist()[0] nozzle = self.part_data.loc[idx]['nz'] if nozzle not in nzidx_2_nozzle.values(): nzidx_2_nozzle[len(nzidx_2_nozzle)] = nozzle nozzle_list[nozzle] += 1 I, J = len(part_list.keys()), len(nozzle_list.keys()) # the maximum number of part types and nozzle types L = I + 1 # the maximum number of batch level K = self.config.head_num # the maximum number of heads HC = [[M for _ in range(J)] for _ in range(I)] # represent the nozzle-part compatibility for i in range(I): for _, item in enumerate(cpidx_2_part.items()): index, part = item cp_idx = self.part_data[self.part_data['part'] == part].index.tolist()[0] nozzle = self.part_data.loc[cp_idx]['nz'] for j in range(J): if nzidx_2_nozzle[j] == nozzle: HC[index][j] = 0 # === phase 1: mathematical model solver === mdl = Model('SMT') # mdl.setParam('OutputFlag', hinter) # === Decision Variables === # the largest workload of all placement heads WL = mdl.addVar(vtype=GRB.INTEGER, lb=0, ub=len(self.step_data), name='WL') # the number of parts of type i that are placed by nozzle type j on placement head k X = mdl.addVars(I, J, K, vtype=GRB.INTEGER, ub=max(part_list.values()), name='X') # the total number of nozzle changes on placement head k N = mdl.addVars(K, vtype=GRB.INTEGER, name='N') # whether batch Xijk is placed on level l Z = mdl.addVars(I, J, L, K, vtype=GRB.BINARY, name='Z') # Dlk := 2 if a change of nozzles in the level l + 1 on placement head k # Dlk := 1 if there are no batches placed on levels higher than l # Dlk := 0 otherwise D = mdl.addVars(L, K, vtype=GRB.BINARY, name='D') D_plus = mdl.addVars(L, J, K, vtype=GRB.INTEGER, name='D_plus') D_minus = mdl.addVars(L, J, K, vtype=GRB.INTEGER, name='D_minus') # == Objective function === mdl.setObjective(a * WL + b * quicksum(N[k] for k in range(K)), GRB.MINIMIZE) # === Constraint === mdl.addConstrs( quicksum(X[i, j, k] for j in range(J) for k in range(K)) == part_list[cpidx_2_part[i]] for i in range(I)) mdl.addConstrs(quicksum(X[i, j, k] for i in range(I) for j in range(J)) <= WL for k in range(K)) mdl.addConstrs( X[i, j, k] <= M * quicksum(Z[i, j, l, k] for l in range(L)) for i in range(I) for j in range(J) for k in range(K)) mdl.addConstrs( quicksum(Z[i, j, l, k] for l in range(L)) <= 1 for i in range(I) for j in range(J) for k in range(K)) mdl.addConstrs( quicksum(Z[i, j, l, k] for l in range(L)) <= X[i, j, k] for i in range(I) for j in range(J) for k in range(K)) mdl.addConstrs(quicksum(Z[i, j, l, k] for j in range(J) for i in range(I)) >= quicksum( Z[i, j, l + 1, k] for j in range(J) for i in range(I)) for k in range(K) for l in range(L - 1)) mdl.addConstrs( quicksum(Z[i, j, l, k] for i in range(I) for j in range(J)) <= 1 for k in range(K) for l in range(L)) mdl.addConstrs(D_plus[l, j, k] - D_minus[l, j, k] == quicksum(Z[i, j, l, k] for i in range(I)) - quicksum( Z[i, j, l + 1, k] for i in range(I)) for l in range(L - 1) for j in range(J) for k in range(K)) mdl.addConstrs( D[l, k] == quicksum((D_plus[l, j, k] + D_minus[l, j, k]) for j in range(J)) for k in range(K) for l in range(L)) # mdl.addConstrs(2 * N[k] == quicksum(D[l, k] for l in range(L)) - 1 for k in range(K)) # mdl.addConstrs( # 0 >= quicksum(HC[i][j] * Z[i, j, l, k] for i in range(I) for j in range(J)) for l in range(L) for k in # range(K)) # === Main Process === mdl.TimeLimit = 100 mdl.optimize() if mdl.Status == GRB.OPTIMAL or mdl.Status == GRB.TIME_LIMIT: print('total cost = {}'.format(mdl.objval)) # convert cp model solution to standard output model_cycle_result, model_part_result = [], [] for l in range(L): model_part_result.append([None for _ in range(K)]) model_cycle_result.append([0 for _ in range(K)]) for k in range(K): for i in range(I): for j in range(J): if abs(Z[i, j, l, k].x - 1) <= 1e-3: model_part_result[-1][k] = cpidx_2_part[i] model_cycle_result[-1][k] = round(X[i, j, k].x) # remove redundant term if sum(model_cycle_result[-1]) == 0: model_part_result.pop() model_cycle_result.pop() head_part_index = [0 for _ in range(self.config.head_num)] while True: head_cycle = [] for head, index in enumerate(head_part_index): head_cycle.append(model_cycle_result[index][head]) if len([cycle for cycle in head_cycle if cycle > 0]) == 0: break self.result.part.append([None for _ in range(self.config.head_num)]) min_cycle = min([cycle for cycle in head_cycle if cycle > 0]) for head, index in enumerate(head_part_index): if model_cycle_result[index][head] != 0: self.result.part[-1][head] = model_part_result[index][head] else: continue model_cycle_result[index][head] -= min_cycle if model_cycle_result[index][head] == 0 and index + 1 < len(model_cycle_result): head_part_index[head] += 1 self.result.cycle.append(min_cycle) part_2_index = {} for index, data in self.part_data.iterrows(): part_2_index[data['part']] = index for cycle in range(len(self.result.part)): for head in range(self.config.head_num): part = self.result.part[cycle][head] self.result.part[cycle][head] = -1 if part is None else part_2_index[part] self.result.slot = self.feeder_assigner.do(self.result.part, self.result.cycle) # === phase 2: heuristic method === self.result.point, self.result.sequence = self.path_planner.greedy_level_placing(self.result.part, self.result.cycle, self.result.slot) else: warnings.warn('No solution found!', UserWarning)