System Intelligence & Optimization LabKO

AI-driven decision-making for manufacturing and logistics systems 제조·물류 시스템을 위한 AI 기반 의사결정

SIO Lab develops optimization models and learning-based algorithms for manufacturing and logistics systems. We bring together operations research (OR) and machine learning (ML) to build operations planning and scheduling solutions and validate them on real industrial problems — semiconductor fabs, steelmaking plants, shipyards, warehouses, container terminals, and smart factories. Our research is driven by industry problems, spanning algorithm design through field proof-of-concept.

School of Industrial and Systems Engineering · Gyeongsang National University

From Industry Problem to Solution

Recent News

Held the lab's first group meeting and dinner with Nahye Kim, Sungwoo Kim, Jitae Kim, and Inhee Han.
Our paper "Monte Carlo tree search for the multi-crane scheduling problem with temporal constraints" has been accepted to Flexible Services and Manufacturing Journal (FSMJ).
Our paper "Towards a unified model for flexible job shop scheduling problems" has been accepted to NeurIPS 2026.
Our paper "A unified learning framework for the block relocation problem: A new performance baseline" has been accepted to European Journal of Operational Research (EJOR).
All news →

SIO Lab에서 함께할 학부연구생, 석사·박사과정생을 모집합니다.

We are recruiting undergraduate, M.S., and Ph.D. students.

wjshin@gnu.ac.kr