Demo Abstract: Collaborative Real-Time Scheduling (CRTS) Algorithm for AGV Transportation System within a CPS Architecture Himansu Shaw University of Houston Houston, USA hshaw@cougarnet.uh.edu Albert M. K. Cheng University of Houston Houston, USA cheng@cs.uh.edu ABSTRACT The use of Autonomous Guided Vehicles (AGVs) is important in smart factories to enhance manufacturing operations. However, cur- rent AGV scheduling algorithms are inadequate and not connected to real-time data on manufacturing needs, leading to underutilized AGVs and missed delivery deadlines. The lack of algorithms that ensure timely delivery of materials results in idle time of AGVs and material shortages. We present a collaborative real-time scheduling (CRTS) algorithm for AGVs in smart factories. The algorithm not only ensures timely delivery of materials to processing units but also predicts the minimum number of AGVs required. The algorithm is designed to operate within a cyber-physical system architecture, where AGVs and processing units exchange data via a wireless net- work. The simulation results on the Node-Red platform show that the algorithm is efficient and adequate to meet real-time delivery requirements, with an average AGV utilization of over 92%. ACM Reference Format: Himansu Shaw and Albert M. K. Cheng. 2023. Demo Abstract: Collaborative Real-Time Scheduling (CRTS) Algorithm for AGV Transportation System within a CPS Architecture. In International Conference on Internet-of-Things Design and Implementation (IoTDI ’23), May 09–12, 2023, San Antonio, TX, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3576842. 3589177 1 INTRODUCTION While fleet manager-based AGV systems have been used in mod- ern industries for 50 years, these systems only prioritize collision- free path planning [1] and localization [2]. They do not consider real-time operational data to improve scheduling or optimize AGV utilization. The paper [3] proposes a real-time scheduling method for battery assembly using a single processing unit and an AGV transporting one type of part. A smart AGV manager server re- ceives battery count and travel-related information to assign de- livery tasks using a dynamic threshold approach. However, actual manufacturing scenarios employ a fleet of AGVs for transporting various parts to different locations.We have expanded this approach Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. IoTDI ’23, May 09–12, 2023, San Antonio, TX, USA © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 979-8-4007-0037-8/23/05. . . $15.00 https://doi.org/10.1145/3576842.3589177 by developing a Collaborative Real-Time Scheduling (CRTS) algo- rithm incorporating multiple AGVs, processing units, and parts without a centralized server for information processing. 2 PROBLEM STATEMENT 2.1 Shopfloor Transportation System We simulated a simplified manufacturing shop floor using a matrix design with 4 processing units (PUs) and 5 part stores. The AGVs are identical, and there is a designated AGV home position where idle AGVs relocate. The layout of the shop floor is shown in Figure 1. Each PU is processing a specific type of parts [A-E]. Each part is processed at different consumption rate. CR i,j is the consumption rate (number of parts per minute) of part j by PU i (PU ID i=1 for the PU 1 , i=2 for PU 2 and so on. part ID j=1 for part type A, j=2 for part type B and so on.). Each of the parts (A-E) are physically different in terms of size and weight so the payload capacity of the AGV for each part is different. For example a heavy part the AGV can carry fewer number of parts per delivery and for lighter part it can carry more part per delivery. We can define the payload capacity of AGV by the term PC j (payload capacity of AGV for part j . At any point of manufacturing operation, all the AGVs can be at any location of the shop-floor. So delivery time by each AGV of part j to PU i is also different. We can define the delivery time by the term DT k,i,j delivery time by AGV k of part j to PU i (AGV ID k=1 for AGV number 1, k=2 for AGV number 2 and so on.). Available parts in the buffer area of any PU can be defined by the term C i,j = available part j at any time in the buffer area of the PU i . Figure 1: Shopfloor facility layout 2.2 Real-time Scheduling Problem of AGV The primary challenge we are tackling is to ensure that parts are delivered to each production unit (PU) on time. Specifically, we aim to deliver each part j via an AGV to any PU i just before C i,j =0, in order to avoid PUs idling without the necessary parts, while also preventing AGVs from waiting until the buffer area is empty 494