POMO

the proposed Light Encoder and Heavy Decoder (LEHD) method is a neural solver for Vehicle Routing Problems that integrates a primary global policy, which learns from the complete VRP instance, with an auxiliary local policy that learns from transferable local topological features to boost generalization performance.

POMOInitialization

Bases: Initialization

Methods: POMOInitialization

The inputs consist of a tensor containing (x, y) coordinates of the instance. POMO then construct a routing solution step by step. After POMOInitializaiton, it outputs a solution solved by the model.

Usage

python train.py setting=pomo_settings mode=train problem={problem}
 

Task

Supported Tasks: TSP, CVRP.

Required Data Generator:

• TSPGenerator

• CVRPGenerator

Policy

• class POMOPolicy

class POMOPolicy(nn.Module):
    def __init__(
        self,
        phase: str = 'train',
        env_name: str = 'tsp',
        layer_num: int = 6,
        node_dim: int = 2,
        embed_dim: int = 128,
        num_heads: int = 8,
        qkv_dim: int = 16,
        feedforward_hidden: int = 512,
        normalization: str = None,
        multihead_bias: bool = False,
        first_mode: str = 'random',
    ):

• Parameters of AM

  • general parameter same as AM

  • multihead_bias: whether use bias in multi head attention.

• Backbone: POMO utilize the Transformer to predict next node.