arXiv subjects

Yongkuk Jeong

Publications and source records attributed to Yongkuk Jeong.

3 recordsLinked to original sources

arXiv2026

Autonomous mobile robot operations logistics: a dataset of jobs, dispatch events and robot states

Autonomous mobile robots (AMRs) increasingly perform material transport in production logistics, where their operation is governed by job generation, dispatching and robot control. We present MoRoOp, a dataset of AMR operations recorded in a laboratory kit preparation and supply scenario over nine eight-hour shifts. During each shift, an AMR executed stochastically generated kit supply, empty-box refill and charging jobs. The dataset links job specifications, the operations constituting each job, dispatch events documenting operation state transitions and outcomes, and robot-state observations comprising position, orientation, velocity, per-wheel state of charge and diagnostics. It contains 1,382 jobs, 4,815 operations, 19,352 dispatch events and 140,386 robot-state observations together with the kit specifications used during job generation. The dataset was recorded in an operating laboratory environment, and technically valid observations of delays, obstructed navigation and unsuccessful operations were retained. Both raw and cleaned robot-state tables are provided. Documented reuse directions include the evaluation of AI agents on operational decision records, disturbance detection, operation prediction, data-driven simulation and event-log analysis.

cs.RO↗

arXiv2026

Toward Self-Organizing Production Logistics: A Multi-Agent Approach

Production logistics is increasingly exposed to variability, dynamic interdependencies, and operational disturbances that challenge conventional centralized planning and control approaches. Following a Design Science Research Methodology, this paper establishes a conceptual foundation for the design, implementation, and evaluation of Self-Organizing Production Logistics (SOPL) systems. First, key technological and systemic drivers motivating SOPL are identified, including autonomous logistics resources, advances in distributed AI-based decision-making, and the transition toward circular production systems, which further amplify operational uncertainty and complexity. Based on these drivers, system-level objectives and design requirements for SOPL are derived. Building on these requirements, the paper proposes an initial multi-agent architecture that integrates embodied and non-embodied agents, event-driven coordination, semantic knowledge structures, and digital twins. In addition, a three-phase demonstration roadmap is presented, progressing from an initial laboratory demonstrator toward increasingly distributed and adaptive SOPL systems. The Phase I demonstrator provides an experimental environment for investigating disturbance handling, human involvement, and supervisory coordination within an order-driven kitting and supply scenario.

eess.SY↗

arXiv2022

An optimization model with stochastic variables for flexible production logistics planning

Production logistics has an important role as a chain that connects the components of the production system. The most important goal of production logistics plans is to keep the flow of the production system well. However, compared to the production system, the level of planning, management, and digitalization of the production logistics system is not high enough, so it is difficult to respond flexibly when unexpected situations occur in the production logistics system. Optimization and heuristic algorithms have been proposed to solve this problem, but due to their inflexible nature, they can only achieve the desired solution in a limited environment. In this paper, the relationship between the production and production logistics system is analyzed and stochastic variables are introduced by modifying the pickup and delivery problem with time windows (PDPTW) optimization model to establish a flexible production logistics plan. This model, taking into account stochastic variables, gives the scheduler a new perspective, allowing them to have new insights based on the mathematical model. However, since the optimization model is still insufficient to respond to the dynamic environment, future research will cover how to derive meaningful results even in a dynamic environment such as a machine learning model.

math.OC↗