Simulation-based Optimization of Material Requirements Planning Parameters

Werth, Bernhard; Karder, Johannes; Beham, Andreas; Altendorfer, Klaus

doi:10.1016/j.procs.2022.12.310

Cited by 4 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, if the available inventory is lacking, it will hamper production activities and eventually cause loss of consumers and sales. There is uncertainty about the time of ordering, supply from suppliers to unclear requests, all of which need to be regulated so that they do not arise in a company [8].…”

Section: Introductionmentioning

confidence: 99%

Material requirements planning method for controlling inventory of raw materials

Cipta

Aprilia

Kurniawan

2023

J. Teknik Informatika CIT Medicom,

View full text Add to dashboard Cite

The existence of shortages and excesses of production raw materials often occurs in the NR Brownies company where this situation greatly influences the smooth production process, especially in the supply of raw materials which results in sub-optimal expenditure costs. The aim of this research is to find solution how to prevent companies from experiencing these problems on a regular basis which results in less than optimal spending costs. In overcoming this problem, the Material Requirement Planning method is applied, this method is expected to minimize expenditure costs so that the benefits obtained are more optimal. From the research results, the Material Requirement Planning Method for EOQ calculations produces the minimum costs for sugar and oil raw materials of 18.295.130 and 19.591.230 respectively, while the POQ calculation produces the minimum costs for raw materials 14.839.500 for flour, 15.450.000 for eggs, 1.356.650 for cocoa flour, and 2.504.387 for baking powder.

show abstract

Section: Introductionmentioning

confidence: 99%

Material requirements planning method for controlling inventory of raw materials

Cipta

Aprilia

Kurniawan

2023

J. Teknik Informatika CIT Medicom,

View full text Add to dashboard Cite

show abstract

“…The optimization of suitable requirements, the implementation of realtime autonomous judgment and autonomous driving technologies, and the use of RL, a sequential action decision technique, are crucial methodologies for addressing these concerns. However, challenges such as insufficient training data, the high cost of trial and error, and potential risk of accidents resulting from trial and error remain [6]. These systems can increase the efficiency of the logistics system of a factory.…”

Section: Introductionmentioning

confidence: 99%

Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices

Lim,

Jeong

2023

Applied Sciences

View full text Add to dashboard Cite

In this study, reinforcement learning (RL) was used in factory simulation to optimize storage devices for use in Industry 4.0 and digital twins. Industry 4.0 is increasing productivity and efficiency in manufacturing through automation, data exchange, and the integration of new technologies. Innovative technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are smartly automating manufacturing processes and integrating data with production systems to monitor and analyze production data in real time and optimize factory operations. A digital twin is a digital model of a physical product or process in the real world. It is built on data and real-time information collected through sensors and accurately simulates the behavior and performance of a real-world manufacturing floor. With a digital twin, one can leverage data at every stage of product design, development, manufacturing, and maintenance to predict, solve, and optimize problems. First, we defined an RL environment, modeled it, and validated its ability to simulate a real physical system. Subsequently, we introduced a method to calculate reward signals and apply them to the environment to ensure the alignment of the behavior of the RL agent with the task objective. Traditional approaches use simple reward functions to tune the behavior of reinforcement learning agents. These approaches issue rewards according to predefined rules and often use reward signals that are unrelated to the task goal. However, in this study, the reward signal calculation method was modified to consider the task goal and the characteristics of the physical system and calculate more realistic and meaningful rewards. This method reflects the complex interactions and constraints that occur during the optimization process of the storage device and generates more accurate episodes of reinforcement learning in agent behavior. Unlike the traditional simple reward function, this reflects the complexity and realism of the storage optimization task, making the reward more sophisticated and effective.The stocker simulation model was used to validate the effectiveness of RL. The model is a storage device that simulates logistics in a manufacturing production area. The results revealed that RL is a useful tool for automating and optimizing complex logistics systems, increasing the applicability of RL in logistics. We proposed a novel method for creating an agent through learning using the proximal policy optimization algorithm, and the agent was optimized by configuring various learning options. The application of reinforcement learning resulted in an effectiveness of 30–100%, and the methods can be expanded to other fields.

show abstract

Reconfigurable and Resilient Closed-Loop Supply Chain Design: A Simulation–Optimization Approach

Gholizadeh,

Pasha,

Hejazi

et al. 2024

Glob J Flex Syst Manag

View full text Add to dashboard Cite

Simulation-based Optimization of Material Requirements Planning Parameters

Cited by 4 publications

References 31 publications

Material requirements planning method for controlling inventory of raw materials

Material requirements planning method for controlling inventory of raw materials

Factory Simulation of Optimization Techniques Based on Deep Reinforcement Learning for Storage Devices

Reconfigurable and Resilient Closed-Loop Supply Chain Design: A Simulation–Optimization Approach

Contact Info

Product

Resources

About