Modeling the Material-Inventory Transportation Problem Using Multi-Objective Optimization

• URL: http://arxiv.org/abs/2206.02350v1
• Date: Mon, 6 Jun 2022 04:39:34 GMT
• Title: Modeling the Material-Inventory Transportation Problem Using Multi-Objective Optimization
• Authors: Issarapong Khuankrue, Sudchai Boonto and Yasuhiro Tsujimura
• Abstract summary: This study proposes a model to find out about the adjustment of material inventory through transportation. The objective of this model is to minimize the whole production cost and total transportation cost.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the era of industry 4.0, procurement in supply chain management is the key to developing information management systems. It directly affects production planning failure. In this case, it is the process to prepare and confirming the material inventory is in the ordinal stages and be able to produce the products in any production line. In terms of industrial informatics, it can provide information management approaches for leveraging data sharing between factories. The multiobjective optimization will be enabled by integrating material inventory, production planning and monitoring, and transportation planning collaboration. The material-inventory transportation problem is the virtual factory situation when production plan failure occurs. It becomes the cost to transport material between each factory and the distribution to clients. In this study, the question of the material-inventory transportation problem is: How can we transport other materials from one factory into another factory? This study proposed a model to find out about the adjustment of material inventory through transportation. The objective of this model is to minimize the whole production cost and total transportation cost.

Related papers

• What if? Causal Machine Learning in Supply Chain Risk Management [47.56698850802985]
  We propose and evaluate the use of causal machine learning for developing supply chain risk intervention models. Our findings highlight that causal machine learning enhances decision-making processes by identifying changes that can be achieved under different supply chain interventions.
  arXiv  Detail & Related papers  (2024-08-24T11:30:25Z)
• IPAD: Industrial Process Anomaly Detection Dataset [71.39058003212614]
  Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames. We propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage.
  arXiv  Detail & Related papers  (2024-04-23T13:38:01Z)
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)
• A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications [1.5136939451642133]
  We present a use case in which the task is to transport and assemble goods through a model factory following predefined rules. The objective is to transport the goods to the assembly station, where two rivets are installed in each product, connecting the upper part to the lower part. The study focuses on the application of reinforcement learning techniques to address this problem and improve the efficiency of the production process.
  arXiv  Detail & Related papers  (2023-06-02T11:00:46Z)
• BioTrak: A Blockchain-based Platform for Food Chain Logistics Traceability [0.0]
  BioTrak is a platform capable of registering and visualizing the whole chain of transformation and transportation processes. The platform includes Business Process Modelling methods to aid food supply chain stakeholders to optimize their processes.
  arXiv  Detail & Related papers  (2023-04-19T12:16:50Z)
• A Generative Approach for Production-Aware Industrial Network Traffic Modeling [70.46446906513677]
  We investigate the network traffic data generated from a laser cutting machine deployed in a Trumpf factory in Germany. We analyze the traffic statistics, capture the dependencies between the internal states of the machine, and model the network traffic as a production state dependent process. We compare the performance of various generative models including variational autoencoder (VAE), conditional variational autoencoder (CVAE), and generative adversarial network (GAN)
  arXiv  Detail & Related papers  (2022-11-11T09:46:58Z)
• Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes [68.8204255655161]
  Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production.
  arXiv  Detail & Related papers  (2022-09-21T08:14:34Z)
• 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)
• From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report) [12.160299682018636]
  We propose a design methodology, using active learning to enhance learning capabilities, for building a model of production outcome using a constrained amount of raw material training data. The proposed methodology is demonstrated using an actual application in the milk industry, where milk is gathered from small milk farms and brought to a dairy production plant to be processed into cottage cheese.
  arXiv  Detail & Related papers  (2022-04-26T13:31:37Z)
• 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)

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.