SearcharxivSearch

arXiv subjects

Issarapong Khuankrue

Publications and source records attributed to Issarapong Khuankrue.

2 recordsLinked to original sources

Modeling the Material-Inventory Transportation Problem Using Multi-Objective Optimization

In the era of industry 4.0, procurement in supply chain management is the key to developing information management systems. It directly affects production planning failure. In this case, it is the process to prepare and confirming the material inventory is in the ordinal stages and be able to produce the products in any production line. In terms of industrial informatics, it can provide information management approaches for leveraging data sharing between factories. The multiobjective optimization will be enabled by integrating material inventory, production planning and monitoring, and transportation planning collaboration. The material-inventory transportation problem is the virtual factory situation when production plan failure occurs. It becomes the cost to transport material between each factory and the distribution to clients. In this study, the question of the material-inventory transportation problem is: How can we transport other materials from one factory into another factory? This study proposed a model to find out about the adjustment of material inventory through transportation. The objective of this model is to minimize the whole production cost and total transportation cost.

eess.SY

Data-Driven Model for Failure Analysis of Internet of Things Devices: A Preliminary Study

This paper proposes the preliminary study of the data-driven failure analysis model for the internet of things (IoT) devices. This model focus on the impact of data transferring both get and receiving data in class C of Low Power Wide Area Network (LoRaWAN). To set up the network, the authors develop the combination of four several technology parts, including 1) the End Device Gateway Network server of LoRa IoT, 2) an Application server for storing the data in the database, 3) the Dashboard to show and got the command by the user, and 4) the failure analysis model based on Bayesian belief networks which calculate the probability values that collect the data transferring both uplink and downlink on the network connection in this study. In the testing phase, the authors input the separated data into the data-driven failure analysis model to analyze the time and latency of the connection by the concern of the impact and risk of the failure of the overall system. The model will show the probability value of failure. The authors hope to use the results to clarify whether a tested IoT device is suitable for use or not.

cs.NI