A scheme of supplementary production in linear programming scheduling of die casting processes

Park, Yong Kuk; Yang, Jung–Min

doi:10.1177/0954405413476861

Cited by 4 publications

(3 citation statements)

References 12 publications

(35 reference statements)

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“…Formatting the missing datasets: we cannot process the missing dataset by algorithm or programming. A string value or text to replace the missing part may include [24,25]. Now, it is needed to convert the string or text value into the desired manner.…”

Section: Implementation Of Machine Learningmentioning

confidence: 99%

Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation

BramahHazela

Hymavathi²,

Kumar³

et al. 2022

Journal of Nanomaterials

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In this paper, we represent a method for machine learning to predict the defect in foundry operation. Foundry has become a driving tool to produce the part to another industry like automobile, marine, and weapon. These foundry processes mainly have two critical problems to decrease the quality assurance. Now, we have to predict the defect to increase the quality of foundry operation. The foundry process’s failure is associated with micro shrinkage and ultimate tensile strength. We process by utilizing a machine learning classifier to predict the micro shrinkage and maximum tensile strength and describe the process, learning process, and evaluate the predataset from the foundry process to compare the accuracy and stability.

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Section: Implementation Of Machine Learningmentioning

confidence: 99%

Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation

BramahHazela

Hymavathi²,

Kumar³

et al. 2022

Journal of Nanomaterials

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“…An approach using multiple non-simultaneous furnaces is proposed by Toledo et al (2014), who present a mixed integer programming model for multiple furnaces in a foundry located in the interior of São Paulo. A linear programming model with multiple kilns and an objective function to maximize the average efficiency of the kilns is presented and solved by Park (2013).…”

Section: Theoretical Foundationmentioning

confidence: 99%

Mathematical modeling to optimize production planning and scheduling in a small foundry with multiple alternating furnaces

Bertulucci¹,

Alves²,

Camargo³

2021

GEPROS

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Purpose - This study presents an extension to a model in the literature for lot-sizing and scheduling in a small foundry with multiple alternate furnaces. The purpose of the model is to minimize delays and inventory costs. In addition, it determines the best use of the load capacity in the furnaces. Theoretical framework – Lot-sizing in foundries in the marketplace is a subject of academic interest due to its applicability and mathematical and computational complexity. Many papers address the production problem in foundries with a single furnace, however, few papers address the possibility of multiple furnaces. Design/methodology/approach - Mathematical modeling was used to represent the lot-sizing and scheduling problem in a small foundry. Data from the company's order books were collected and model validation questionnaires were applied. Findings - The extended model was able to generate good production plans at different planning horizons, with better performance than the current methods obtained by the company. Originality/value - the extension of the model contributes to the literature by addressing the existence of multiple non-simultaneous furnaces, a feature that has not been greatly explored. A comparison with other models is performed to indicate the most suitable model for actual application. Keywords: Alloys scheduling. Foundry. Lot size. Mixed integer programming.

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“…The demand quantity prefers single and small batch which is not conducive to stock-based production, and time limit of casting enterprise is a long-term problem. Therefore, the timely delivery rate is generally relatively low in many casting enterprises [1]. In order to improve the timely delivery rate, a research of an actual foundry flow shop has been taken; it is a medium-sized manufacturing enterprise and the practical manufacturing process is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

Guo

Liu

et al. 2017

Mathematical Problems in Engineering

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The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

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A scheme of supplementary production in linear programming scheduling of die casting processes

Cited by 4 publications

References 12 publications

Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation

Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation

Mathematical modeling to optimize production planning and scheduling in a small foundry with multiple alternating furnaces

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

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