Dynamic Reactive Assignment of Tasks in Real-Time Automated Guided Vehicle Environments with Potential Interruptions

An efficient management of production plants has to consider several external and internal factors, such as potential interruptions of the ongoing processes. Automated guided vehicles (AGVs) are becoming a widespread technology that offers many advantages. These AGVs can perform complex tasks in an...

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Bibliographic Details
Authors: Martin, Xabier A.|||0000-0003-4182-0120, Hatami, Sara|||0000-0002-8000-4989, Calvet, Laura|||0000-0001-8425-1381, Peyman, Mohammad|||0000-0003-4734-2414, Juan, Ángel A.|||0000-0003-1392-1776
Format: article
Publication Date:2023
Country:España
Institution:Universitat Autònoma de Barcelona
Repository:Dipòsit Digital de Documents de la UAB
Language:English
OAI Identifier:oai:ddd.uab.cat:287469
Online Access:https://ddd.uab.cat/record/287469
https://dx.doi.org/urn:doi:10.3390/app13063708
Access Level:Open access
Keyword:Automated guided vehicles
Dynamic task assignment
Internal logistics
Heuristic optimization
Disruption management
Description
Summary:An efficient management of production plants has to consider several external and internal factors, such as potential interruptions of the ongoing processes. Automated guided vehicles (AGVs) are becoming a widespread technology that offers many advantages. These AGVs can perform complex tasks in an autonomous way. However, an inefficient schedule of the tasks assigned to an AGV can suffer from unwanted interruptions and idle times, which in turn will affect the total time required by the AGV to complete its assigned tasks. In order to avoid these issues, this paper proposes a heuristic-based approach that: (i) makes use of a delay matrix to estimate circuit delays for different daily times; (ii) employs these estimates to define an initial itinerary of tasks for an AGV; and (iii) dynamically adjusts the initial agenda as new information on actual delays is obtained by the system. The objective is to minimize the total time required for the AGV to complete all the assigned tasks, taking into account situations that generate unexpected disruptions along the circuits that the AGV follows. In order to test and validate the proposed approach, a series of computational experiments utilizing real-life data are carried out. These experiments allow us to measure the improvement gap with respect to the former policy used by the system managers.