239 citations · 256 across the 9 of their papers we have counts for
5 papers · 1 filter
In-Context Guidance: Learning Inter-Task Synergies via Numerical Foundational Models for Few-Shot Multitask Optimization
Tingyang Wei, Haofeng Wu, Jiao Liu +3
Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation under limited evaluation budg…
Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modeling
Tingyang Wei, Jiao Liu, Abhishek Gupta +3
Many real-world applications require solving families of expensive multi-objective optimization problems~(EMOPs) under varying operational conditions. This can be formulated as par…
Identifying the Key Attributes in an Unlabeled Event Log for Automated Process Discovery
Kentaroh Toyoda, Rachel Gan Kai Ying, Allan NengSheng Zhang +1
Process mining discovers and analyzes a process model from historical event logs. The prior art methods use the key attributes of case-id, activity, and timestamp hidden in an even…
Learning to Solve Multiple-TSP with Time Window and Rejections via Deep Reinforcement Learning
Rongkai Zhang, Cong Zhang, Zhiguang Cao +5
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 wi…
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Cong Zhang, Wen Song, Zhiguang Cao +3
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myria…