Toward Automated Planning Algorithms Applied to Production and Logistics

Sousa, Alexandre Rodrigues de; Tavares, José Jean-Paul Zanlucchi de Souza

doi:10.3182/20130911-3-br-3021.00081

Cited by 10 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over time, various extensions to PDDL have been developed, supporting, for example, modal operators, ontologies, probabilistic effects, partial observability, goal rewards, durative actions, hierarchical action expansion, and many more. Applications of some of these PDDL extensions in the context of production and logistics, including a description of the techniques used in the solvers, are discussed by Sousa and Tavares [40], while durative actions have, e.g., been utilized by Wally et al [41].…”

Section: Ai Planning and Schedulingmentioning

confidence: 99%

A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities

et al. 2023

View full text Add to dashboard Cite

Industry 4.0 smart production systems comprise industrial systems and subsystems that need to be integrated in such a way that they are able to support high modularity and reconfigurability of all system components. In today’s industrial production, manufacturing execution systems (MESs) and supervisory control and data acquisition (SCADA) systems are typically in charge of orchestrating and monitoring automated production processes. This article explicates an MES architecture that is capable of autonomously composing, verifying, interpreting, and executing production plans using digital twins and symbolic planning methods. To support more efficient production, the proposed solution assumes that the manufacturing process can be started with an initial production plan that may be relatively inefficient but quickly found by an AI. While executing this initial plan, the AI searches for more efficient alternatives and forwards better solutions to the proposed MES, which is able to seamlessly switch between the currently executed plan and the new plan, even during production. Further, this on-the-fly replanning capability is also applicable when newly identified production circumstances/objectives appear, such as a malfunctioning robot, material shortage, or a last-minute change to a customizable product. Another feature of the proposed MES solution is its distributed operation with multiple instances. Each instance can interpret its part of the production plan, dedicated to a location within the entire production site. All of these MES instances are continuously synchronized, and the actual global or partial (i.e., from the instance perspective) progress of the production is handled in real-time within one common digital twin. This article presents three main contributions: (i) an execution system that is capable of switching seamlessly between an original and a subsequently introduced alternative production plan, (ii) on-the-fly AI-powered planning and replanning of industrial production integrated into a digital twin, and (iii) a distributed MES, which allows for running multiple instances that may depend on topology or specific conditions of a real production plant. All of these outcomes are demonstrated and validated on a use-case utilizing an Industry 4.0 testbed, which is equipped with an automated transport system and several industrial robots. While our solution is tested on a lab-sized production system, the technological base is prepared to be scaled up to larger systems.

show abstract

Section: Ai Planning and Schedulingmentioning

confidence: 99%

A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The latest version of the language is PDDL 3.1 [26] but there exist many variants/extensions that support various features like goal rewards, probabilistic effects, multi-agent planning, temporal planning, etc. An overview of several extensions of PDDL including explanation of techniques in successful solvers is provided in [27].…”

Section: B Automated Planningmentioning

confidence: 99%

Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL

Wally

Vyskočil

Novák

et al. 2019

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Model-driven engineering (MDE) provides tools and methods for the manipulation of formal models. In this letter, we leverage MDE for the transformation of production system models into flat files that are understood by general purpose planning tools and that enable the computation of "plans", i.e., sequences of production steps that are required to reach certain production goals. These plans are then merged back into the production system model, thus enriching the formalized production system knowledge.Index Terms-AI-based methods, factory automation, intelligent and flexible manufacturing.

show abstract

“…Due to the expressive modelling capabilities inherent in automated planning, planners have become viable tools in various real-world applications, including robotics (Barrios et al 2011;Karpas and Magazzeni 2020), logistics (Sousa and Tavares 2013;Pedersen and Krüger 2015), healthcare (Fuentetaja et al 2020;Lindsay et al 2022) and much more. Traditionally, the primary focus of planners has been on generating a single optimal or high-quality satisficing plan.…”

Section: Introductionmentioning

confidence: 99%

Bi-Criteria Diverse Plan Selection via Beam Search Approximation

Zhong,

Shati,

Cohen

2024

SOCS

View full text Add to dashboard Cite

Recent work on diverse planning has focused on a two-step setting where the first step consists of generating a large number of plans, and the second step consists of selecting a subset of plans that maximizes diversity. For the second step, previous work has focused on solving a combinatorial optimization problem for diverse subset selection that can be approximated using greedy search. In this work, we propose a flexible, bi-criteria framework for diverse plan selection. Our framework consists of optimizing both quality and diversity, generalizing previous work and providing flexibility to prioritize one objective over the other. We consider two quality and two diversity measures and show that greedy search guarantees an approximation with a constant ratio for certain configurations based on established results in the literature. To allow users to trade off additional computation for better solutions, we introduce a beam search approximation that generalizes the greedy search, and we provide approximation guarantees on the obtained solutions. Finally, we conduct extensive experiments that show that: (1) our flexible bi-criteria framework allows us to obtain solutions of better quality while still maintaining a high degree of diversity; (2) our beam search approximation obtains significant improvement in performance over greedy search and, for a large number of instances, is able to generate solutions that are equal to or better than those obtained by an exact MIP solver with a significantly higher runtime limit.

show abstract

Toward Automated Planning Algorithms Applied to Production and Logistics

Cited by 10 publications

References 9 publications

A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities

A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities

Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL

Bi-Criteria Diverse Plan Selection via Beam Search Approximation

Contact Info

Product

Resources

About