Search Results for author:
Found 29 papers, 21 papers with code
Cross-Problem Learning for Solving Vehicle Routing Problems
Accordingly, we propose to pre-train the backbone Transformer for TSP, and then apply it in the process of fine-tuning the Transformer models for each target VRP variant.
Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
As a proof-of-principle study, we apply this framework to a group of Differential Evolution algorithms.
LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation
Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer.
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Existing learning-based methods for solving job shop scheduling problem (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs).
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design process.
GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance.
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods.
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
As a Neural Combinatorial Optimization method, DeepACO performs better than or on par with problem-specific methods on canonical routing problems.
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules.
Neural Airport Ground Handling
Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation.
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling
Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH).
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics.
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i. e., uniform).
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections.
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV
Moreover, we also show via an ablation study that our ITS can help achieve a balance between the performance and training efficiency.
Efficient Neural Neighborhood Search for Pickup and Delivery Problems
We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs).
Learning to Solve Routing Problems via Distributionally Robust Optimization
Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability.
Learning Large Neighborhood Search Policy for Integer Programming
We then design a neural network to learn policies for each variable in parallel, trained by a customized actor-critic algorithm.
NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem
We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem.
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem
To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning
In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i. e., the pickup node must precede the pairing delivery node.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs
Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity.
Generative Adversarial Training for Neural Combinatorial Optimization Models
Recent studies show that deep neural networks can be trained to learn good heuristics for various Combinatorial Optimization Problems (COPs).
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems
We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems.
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems
In this paper, we propose a deep reinforcement learning based approach to automatically discover new variable ordering heuristics that are better adapted for a given class of CSP instances.
Learning Improvement Heuristics for Solving Routing Problems
In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems.
