A Logic Programming Approach to Global Logistics in a Co-Design Environment

• URL: http://arxiv.org/abs/2308.15892v1
• Date: Wed, 30 Aug 2023 09:06:34 GMT
• Title: A Logic Programming Approach to Global Logistics in a Co-Design Environment
• Authors: Emmanuelle Dietz (Airbus Central Research & Technology, Hein-Sass-Weg 22, 21129 Hamburg, Germany), Tobias Philipp (secunet Security Networks AG, Germany), Gerrit Schramm (Airbus Central Research & Technology, Hein-Sass-Weg 22, 21129 Hamburg, Germany), Andreas Zindel (Airbus Central Research & Technology, Hein-Sass-Weg 22, 21129 Hamburg, Germany)
• Abstract summary: This paper considers the challenge of creating and optimizing a global logistics system for the construction of a passenger aircraft. The product in question is an aircraft, comprised of multiple components, manufactured at multiple sites worldwide. The goal is to find an optimal way to build the aircraft taking into consideration the requirements for its industrial system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a co-design environment changes need to be integrated quickly and in an automated manner. This paper considers the challenge of creating and optimizing a global logistics system for the construction of a passenger aircraft within a co-design approach with respect to key performance indicators (like cost, time or resilience). The product in question is an aircraft, comprised of multiple components, manufactured at multiple sites worldwide. The goal is to find an optimal way to build the aircraft taking into consideration the requirements for its industrial system. The main motivation for approaching this challenge is to develop the industrial system in tandem with the product and making it more resilient against unforeseen events, reducing the risks of bottlenecks in the supply chain. This risk reduction ensures continued efficiency and operational success. To address this challenging and complex task we have chosen Answer Set Programming (ASP) as the modeling language, formalizing the relevant requirements of the investigated industrial system. The approach presented in this paper covers three main aspects: the extraction of the relevant information from a knowledge graph, the translation into logic programs and the computation of existing configurations guided by optimization criteria. Finally we visualize the results for an effortless evaluation of these models. Internal results seem promising and yielded several new research questions for future improvements of the discussed use case.

Related papers

• Integrated Modeling, Verification, and Code Generation for Unmanned Aerial Systems [10.292890852621346]
  Unmanned Aerial Systems (UAS) are widely used in safety-critical fields such as industrial production, military operations, and disaster relief. This paper aims to investigate an integrated approach to modeling, verification, and code generation for UAS.
  arXiv  Detail & Related papers  (2024-06-13T14:53:40Z)
• Dealing with Data for RE: Mitigating Challenges while using NLP and Generative AI [2.9189409618561966]
  Book chapter explores the evolving landscape of Software Engineering in general, and Requirements Engineering (RE) in particular. We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems. Book provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary.
  arXiv  Detail & Related papers  (2024-02-26T19:19:47Z)
• Generative AI in the Construction Industry: A State-of-the-art Analysis [0.4241054493737716]
  There is a gap in the literature on the current state, opportunities, and challenges of generative AI in the construction industry. This study aims to review and categorize the existing and emerging generative AI opportunities and challenges in the construction industry. It proposes a framework for construction firms to build customized generative AI solutions using their own data.
  arXiv  Detail & Related papers  (2024-02-15T13:39:55Z)
• Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design [0.0]
  This paper introduces a novel set of benchmark problems aimed at advancing research in both single and multi-objective optimization. These benchmark problems are unique in that they incorporate real-world design considerations such as fluid dynamics and material mechanics. We propose three difficulty levels and a wing segmentation parameter in these problems, allowing for scalable complexity to suit various research needs.
  arXiv  Detail & Related papers  (2023-12-14T14:01:41Z)
• Octopus: Embodied Vision-Language Programmer from Environmental Feedback [59.772904419928054]
  Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. In this paper, we introduce Octopus, a novel VLM designed to proficiently decipher an agent's vision and textual task objectives. Our design allows the agent to adeptly handle a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games.
  arXiv  Detail & Related papers  (2023-10-12T17:59:58Z)
• On a Uniform Causality Model for Industrial Automation [61.303828551910634]
  A Uniform Causality Model for various application areas of industrial automation is proposed. The resulting model describes the behavior of Cyber-Physical Systems mathematically. It is shown that the model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.
  arXiv  Detail & Related papers  (2022-09-20T11:23:51Z)
• Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes [78.120734120667]
  This paper describes an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture. The approach is explained using an example based on industrial requirements.
  arXiv  Detail & Related papers  (2022-05-06T18:44:29Z)
• A Research Agenda for Artificial Intelligence in the Field of Flexible Production Systems [53.47496941841855]
  Production companies face problems when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches aiming to encapsulate production functions in the sense of services have shown to be promising in order to increase flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding production plans based on provided functionalities for a set of requirements, especially when there is no direct (i.e., syntactic) match between demanded and provided functions.
  arXiv  Detail & Related papers  (2021-12-31T14:38:31Z)
• CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning [68.74447489372037]
  We present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning. A core component of our work is to introduce textitagency, such that it is simple to define and create complex scenarios. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment.
  arXiv  Detail & Related papers  (2021-06-25T00:21:41Z)
• The Factory Must Grow: Automation in Factorio [3.877356414450363]
  In this paper we define the logistic transport belt problem and define integer programming model of it. We present results for Simulated Annealing, quick Genetic Programming and Evolutionary Reinforcement Learning, three different meta-heuristic techniques to optimize this novel problem.
  arXiv  Detail & Related papers  (2021-02-09T15:14:27Z)
• Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning [49.367020832638794]
  The aerospace industry is poised to capitalize on big data and machine learning. Recent trends will be explored in context of critical challenges in design, manufacturing, verification and services.
  arXiv  Detail & Related papers  (2020-08-24T22:40:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.