Search Results for author:
Found 27 papers, 6 papers with code
Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem.
UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model
Recently, applying neural networks to address combinatorial optimization problems (COPs) has attracted considerable research attention.
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems.
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
We evaluated MO-DCMAC using two utility functions, which use probability of collapse and cost as input.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
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.
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
We develop a discrete-event simulation model, which we use to train and evaluate the proposed DRL approach.
Parcel loss prediction in last-mile delivery: deep and non-deep approaches with insights from Explainable AI
Within the domain of e-commerce retail, an important objective is the reduction of parcel loss during the last-mile delivery phase.
Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
We introduce an open-source GitHub repository containing comprehensive benchmarks for a wide range of machine scheduling problems, including Job Shop Scheduling (JSP), Flow Shop Scheduling (FSP), Flexible Job Shop Scheduling (FJSP), FJSP with Assembly constraints (FAJSP), FJSP with Sequence-Dependent Setup Times (FJSP-SDST), and the online FJSP (with online job arrivals).
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
In order to evaluate the impact of interpretations on perceived trust, explanation satisfaction attributes are rated by MEs through a survey.
Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networks
In this paper, we revisit the algorithm selection problem for TSP, and propose a novel Graph Neural Network (GNN), called GINES.
Digital Twin Applications in Urban Logistics: An Overview
In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics networks.
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search.
Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems
The DRL policy is trained to adaptively set the values that dictate the intensity and probability of mutation for solutions during optimization.
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers.
Automated Reinforcement Learning: An Overview
Automated RL provides a framework in which different components of RL including MDP modeling, algorithm selection and hyper-parameter optimization are modeled and defined automatically.
Policies for the Dynamic Traveling Maintainer Problem with Alerts
On the other hand, because the available resources to serve a network of geographically dispersed assets are typically limited.
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning
In this paper, we present a Deep Reinforcement Learning (DRL) approach for deciding how and when orders should be batched and picked in a warehouse to minimize the number of tardy orders.
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning
This paper proposes a Deep Reinforcement Learning (DRL) approach for solving knapsack problem.
Algorithms for slate bandits with non-separable reward functions
In this paper, we study a slate bandit problem where the function that determines the slate-level reward is non-separable: the optimal value of the function cannot be determined by learning the optimal action for each slot.
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution.
Term Based Semantic Clusters for Very Short Text Classification
These clusters are ranked using a semantic similarity function which in turn defines a semantic feature space that can be used for text classification.
Remaining Useful Lifetime Prediction via Deep Domain Adaptation
We propose a Domain Adversarial Neural Network (DANN) approach to learn domain-invariant features that can be used to predict the RUL in the target domain.
Machine Learning based Simulation Optimisation for Trailer Management
In many situations, simulation models are developed to handle complex real-world business optimisation problems.
Column generation based math-heuristic for classification trees
To speed up the heuristic, we use a restricted instance data by considering a subset of decision splits, sampled from the solutions of the well-known CART algorithm.
Fair task allocation in transportation
Task allocation problems have traditionally focused on cost optimization.
Learning optimization models in the presence of unknown relations
Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver.
Solving Weighted Voting Game Design Problems Optimally: Representations, Synthesis, and Enumeration
We study the inverse power index problem for weighted voting games: the problem of finding a weighted voting game in which the power of the players is as close as possible to a certain target distribution.
