An Agile and Reactive Biased-Randomized Heuristic for an Agri-Food Rich Vehicle Routing Problem

Tordecilla, Rafael D.; Copado-Méndez, Pedro J.; Panadero, Javier; Martins, Leandro do C.; Juan, Ángel A.

doi:10.1016/j.trpro.2021.11.052

Cited by 1 publication

(1 citation statement)

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“…We propose a method for determining the order of tasks assigned to an AGV using estimated circuit delays and dynamically adjusting the initial agenda as new information on actual delays becomes available. Hence, the main contributions of our work can be described as follows: (i) the introduction of a single AGV task order determination with unexpected interruption events in order to minimize the total time or makespan; (ii) the use of data analytics techniques to estimate the AGV interruption and caused delay for each determined pathway during determined time-spans; and (iii) the development of a reactive heuristic [12,13], studying the resulting permutations of tasks generated using three different approaches. The remaining part of the paper is organised as follows.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%