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from greedrl.feature import * |
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from greedrl.variable import * |
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from greedrl.function import * |
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from greedrl import Problem |
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features = [local_category('task_group'), |
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global_category('task_priority', 2), |
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variable_feature('distance_this_to_task'), |
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variable_feature('distance_task_to_end')] |
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variables = [task_demand_now('task_demand_now', feature='task_demand'), |
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task_demand_now('task_demand_this', feature='task_demand', only_this=True), |
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feature_variable('task_weight'), |
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feature_variable('task_group'), |
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feature_variable('task_priority'), |
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feature_variable('task_due_time2', feature='task_due_time'), |
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task_variable('task_due_time'), |
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task_variable('task_service_time'), |
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task_variable('task_due_time_penalty'), |
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worker_variable('worker_basic_cost'), |
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worker_variable('worker_distance_cost'), |
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worker_variable('worker_due_time'), |
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worker_variable('worker_weight_limit'), |
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worker_used_resource('worker_used_weight', task_require='task_weight'), |
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worker_used_resource('worker_used_time', 'distance_matrix', 'task_service_time', 'task_ready_time', |
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'worker_ready_time'), |
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edge_variable('distance_last_to_this', feature='distance_matrix', last_to_this=True), |
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edge_variable('distance_this_to_task', feature='distance_matrix', this_to_task=True), |
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edge_variable('distance_task_to_end', feature='distance_matrix', task_to_end=True)] |
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class Constraint: |
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def do_task(self): |
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return self.task_demand_this |
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def mask_worker_end(self): |
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return task_group_split(self.task_group, self.task_demand_now <= 0) |
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def mask_task(self): |
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mask = self.task_demand_now <= 0 |
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mask |= task_group_priority(self.task_group, self.task_priority, mask) |
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worker_used_time = self.worker_used_time[:, None] + self.distance_this_to_task |
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mask |= (worker_used_time > self.task_due_time2) & (self.task_priority == 0) |
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worker_weight_limit = self.worker_weight_limit - self.worker_used_weight |
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mask |= self.task_demand_now * self.task_weight > worker_weight_limit[:, None] |
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return mask |
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def finished(self): |
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return torch.all(self.task_demand_now <= 0, 1) |
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class Objective: |
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def step_worker_start(self): |
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return self.worker_basic_cost |
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def step_worker_end(self): |
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feasible = self.worker_used_time <= self.worker_due_time |
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return self.distance_last_to_this * self.worker_distance_cost, feasible |
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def step_task(self): |
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worker_used_time = self.worker_used_time - self.task_service_time |
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feasible = worker_used_time <= self.task_due_time |
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feasible &= worker_used_time <= self.worker_due_time |
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cost = self.distance_last_to_this * self.worker_distance_cost |
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return torch.where(feasible, cost, cost + self.task_due_time_penalty), feasible |
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def make_problem(batch_count, batch_size=1, task_count=100): |
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assert batch_size == 1 |
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N = task_count // 2 |
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problem_list = [] |
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for i in range(batch_count): |
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problem = Problem() |
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problem.id = i |
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problem.worker_weight_limit = torch.tensor([50], dtype=torch.float32) |
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problem.worker_ready_time = torch.tensor([0], dtype=torch.float32) |
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problem.worker_due_time = torch.tensor([1000000], dtype=torch.float32) |
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problem.worker_basic_cost = torch.tensor([100], dtype=torch.float32) |
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problem.worker_distance_cost = torch.tensor([1], dtype=torch.float32) |
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task_demand = torch.randint(1, 10, (N,), dtype=torch.int32) |
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problem.task_demand = torch.cat([task_demand, task_demand], 0) |
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task_weight = torch.ones(N, dtype=torch.float32) |
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problem.task_weight = torch.cat([task_weight, task_weight * -1], 0) |
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task_group = torch.arange(N, dtype=torch.int32) |
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problem.task_group = torch.cat([task_group, task_group], 0) |
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task_priority = torch.zeros(N, dtype=torch.int32) |
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problem.task_priority = torch.cat([task_priority, task_priority + 1], 0) |
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task_ready_time = torch.zeros(N, dtype=torch.float32) |
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problem.task_ready_time = torch.cat([task_ready_time, task_ready_time], 0) |
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task_due_time = torch.randint(10000, 100000, (N,), dtype=torch.float32) |
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problem.task_due_time = torch.cat([task_due_time, task_due_time * 2], 0) |
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task_service_time = torch.zeros(N, dtype=torch.float32) |
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problem.task_service_time = torch.cat([task_service_time, task_service_time]) |
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task_due_time_penalty = torch.ones(N, dtype=torch.float32) |
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problem.task_due_time_penalty = torch.cat([task_due_time_penalty, task_due_time_penalty]) |
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loc = torch.rand(N + 1, 2, dtype=torch.float32) |
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distance_matrix = torch.norm(loc[:, None, :] - loc[None, :, :], dim=2) * 1000 |
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distance_matrix = distance_matrix.to(torch.float32) |
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index = torch.cat([torch.zeros(N + 1, dtype=torch.int64), torch.arange(N, dtype=torch.int64) + 1]) |
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index1 = index[:, None] |
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index2 = index[None, :] |
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problem.distance_matrix = distance_matrix[index1, index2] |
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problem.features = features |
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problem.variables = variables |
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problem.constraint = Constraint |
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problem.objective = Objective |
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problem_list.append(problem) |
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return problem_list |
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if __name__ == '__main__': |
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import sys |
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import os.path as osp |
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sys.path.append(osp.join(osp.dirname(__file__), '../')) |
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import runner |
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runner.run(make_problem) |
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