Production Intralogistics Automation Based on 3D Simulation Analysis

Mahmood, Kashif; Karjust, Kristo; Raamets, Tõnis

doi:10.36897/jme/137081

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…The process for the transportation of goods by AMR on the production floor is analyzed and implemented through the approach as anticipated in our previous study [18] and illustrated in Fig. 1.…”

Section: Approach For Amr Process Analysis and Modificationmentioning

confidence: 99%

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Autonomous mobile robots for production logistics: a process optimization model modification

Raamets,

Majak,

Karjust

et al. 2024

PEAS

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Digital solutions have become increasingly important for manufacturing companies to increase their productivity, effectiveness, and competitiveness in a global market, which demands low prices, high quality, and fast delivery times. In order to improve production efficiency, it is also necessary to optimize transportation activities in the production floor via digitization and automation of those processes. Many companies have already used or are planning to use autonomous mobile robots (AMR) to manage production logistics more effectively. The rapid development of the Internet of Things (IoT) and the advanced hardware and software of AMR allow them to perform autonomous tasks in dynamic environments, where they can communicate and independently coordinate with other resources, such as machines and systems, and thus decentralize the decisionmaking steps of manufacturing processes. Decentralized decision making allows the manufacturing system to dynamically adapt to changes in the system state and environment. Such developments have affected traditional planning and control methods and decisionmaking processes, but they also require the software and embedded artificial intelligence (AI) algorithms to be more capable of executing these decisions. In this study, we describe how to use a 3D virtual factory concept to integrate an AMR system with AI functionality into the production logistics of the food industry. The paper presents an approach to analyze the performance of AMR in the transportation of goods on the manufacturing plant floor, based on the creation and simulation of the 3D layout, the monitoring of key performance indicators (KPI), and the use of AI for proactive decision making in production planning. A case study of the food industry demonstrates the relevance and feasibility of the proposed approach.

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Section: Approach For Amr Process Analysis and Modificationmentioning

confidence: 99%

“…1. Proposed approach to analyze the process of AMR for production logistics [18]. implementation of AMR in the real (physical) environ ment, while an AI model can also be formulated based on the simulation model.…”

Section: Approach For Amr Process Analysis and Modificationmentioning

confidence: 99%

Autonomous mobile robots for production logistics: a process optimization model modification

Raamets,

Majak,

Karjust

et al. 2024

PEAS

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“…The objective of company managers is an optimisation of SC performances, which could be achieved by using network models to generate a solution [31,32].…”

Section: Main Idea Of the Researchmentioning

confidence: 99%

Digitalisation of Supply Chain management system for customer quality service improvement.

Shevtshenko¹,

Maas²,

Murumaa³

et al. 2022

Journal of Machine Engineering

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The main idea of the current research is to apply customer satisfaction level Key Performance Indicators (KPIs) for supply chain reliability improvement. The Supply Chain Operations Reference (SCOR) model-based KPI metrics increase the quality of product/service by monitoring, visualising, and digitalising directly involved processes. In the long run, the solution will ultimately help reduce/eliminate the number of customer reclamations in the supply chain. An industry-oriented performance measurement model based on SCOR can be easily adapted for different sectors. The approach proposed in the current research is based on identifying key factors of supply chain performance of the SCOR model connected with the predictive and diagnostic capability of Bayesian Believe Networks. The difference in performance can be reached via applying the best practices to processes, affecting the performance on a larger scale.

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“…Material handling is a pivotal logistics function that ensures the unbroken continuum of operations by incorporating the principles of "time and place utility" within its procedural framework. The evolutionary trajectory of material handling systems (MHS), propelled by integrating smart manufacturing and digitalisation concepts, strives towards realising predefined objectives while minimising the necessity for human intervention and oversight [7]. In modern MHS frameworks, there is a discernible trend towards incorporating advanced technologies such as autonomous mobile robots (AMR), automated guided vehicles (AGV), and drones.…”

Section: Introductionmentioning

confidence: 99%

A Closed Queueing Networks Approach for an Optimal Heterogeneous Fleet Size of an Inter-Facility Bulk Material Transfer System

Amjath,

Kerbache,

Smith

2024

Logistics

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Background: This study addresses optimising fleet size in a system with a heterogeneous truck fleet, aiming to minimise transportation costs in interfacility material transfer operations. Methods: The material transfer process is modelled using a closed queueing network (CQN) that considers heterogeneous nodes and customised service times tailored to the unique characteristics of various truck types and their transported materials. The optimisation problem is formulated as a mixed-integer nonlinear programming (MINLP), falling into the NP-Hard, making exact solution computation challenging. A numerical approximation method, a modified sequential quadratic programming (SQP) method coupled with a mean value analysis (MVA) algorithm, is employed to overcome this challenge. Validation is conducted using a discrete event simulation (DES) model. Results: The proposed analytical model tested within a steel manufacturing plant’s material transfer process. The results showed that the analytical model achieved comparable optimisation of the heterogeneous truck fleet size with significantly reduced response times compared to the simulation method. Furthermore, evaluating performance metrics, encompassing response time, utilisation rate, and cycle time, revealed minimal discrepancies between the analytical and the simulation results, approximately ±8%, ±8%, and ±7%, respectively. Conclusions: These findings affirm the presented analytical approach’s robustness in optimising interfacility material transfer operations with heterogeneous truck fleets, demonstrating real-world applications.

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Production Intralogistics Automation Based on 3D Simulation Analysis

Cited by 6 publications

References 19 publications

Autonomous mobile robots for production logistics: a process optimization model modification

Autonomous mobile robots for production logistics: a process optimization model modification

Digitalisation of Supply Chain management system for customer quality service improvement.

A Closed Queueing Networks Approach for an Optimal Heterogeneous Fleet Size of an Inter-Facility Bulk Material Transfer System

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