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

Abstract

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.

Optimization of AGV Sorting Systems in Pharmaceutical Distribution: A Two-Stage Package Assignment and Simulation Approach

“…Considering AGVs in dynamic environments, Martin et al [22] introduced a reactive heuristic approach to reduce the total time AGVs spend on a series of tasks. By using a delay matrix based on historical data, they could anticipate circuit delays, thus allowing for a more adaptive AGV task scheduling.…”

Simulation of Heuristics for Automated Guided Vehicle Task Sequencing with Resource Sharing and Dynamic Queues

“…They proposed a heuristic to reduce its complexity from O(N 3 ) to O(N 2 ) by utilizing auxiliary data structures. In another approach, Martin et al [30] proposed a reactive solution for a dynamic RTSP, where the AGV resets after each task, akin to this study's application. They focused on task sequencing, using simulation to manage resource constraints, workstation availability, and queue dynamics, thus leading to effective scheduling.…”

Scheduling of Multi-AGV Systems in Automated Electricity Meter Verification Workshops Based on an Improved Snake Optimization Algorithm

“…In order to compare the performance of different optimization algorithms for AGV charging route planning, this study analyzed the simulation results of the FIFO, MVO, WOA, DE, GA, PSO, SO, and ISO algorithms. The charging times of these algorithms are 85, 74, 72, 71, 69, 68, 66, and 53, and the costs are 17,877,16,688,16,807,16,978,16,921,17,081,16,792,15,350, respectively. It is worth noting that ISO has the largest improvement over the traditional FIFO method, reducing costs by 16.46% while reducing the number of charges by 60%.…”

“…This poses higher requirements on AGV scheduling and path planning algorithms, which need to fully consider multi-constraint conditions like AGV load constraints, loading/unloading time constraints, etc., and conduct more sophisticated transportation planning. Based on real-time dispatching strategies of AGVs, the literature [15,16] proposed a multi-task chain scheduling algorithm considering the remaining cargo capacity characteristics of AGVs and validated through a real smart manufacturing system that it can significantly reduce scheduling costs. For multi-load AGV scheduling problems with capacity constraints, the literature [17] proposed an improved ant colony optimization-simulated annealing algorithm based on multi-attribute scheduling rules, but without considering AGV quantity limits.…”