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