Predictive production planning in an integrated pulp and paper mill

Figueira, Gonçalo; Furlan, Marcos Mansano; Almada-Lobo, Bernardo

doi:10.3182/20130619-3-ru-3018.00409

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…• Availability improvement -Predictive Maintenance [12], [13], [14] -Prognostics and Health Management [13], [15] • Performance improvement -Predictive Production Planning [16] -Predictive Manufacturing Control [17] • Quality improvement -Predictive Quality Control [18] -Control Chart Pattern Recognition [19], [20] These concepts provide theoretical foundations and require additional specifications for direct applicability. For this purpose, we propose a specified process model focusing on improving availability, since the costs for keeping a high availability represent 15 -60 % of total costs in manufacturing [21].…”

Section: Kdd In Manufacturingmentioning

confidence: 99%

How to Discover Knowledge for Improving Availability in the Manufacturing Domain?

Utz¹,

Neumann²,

Omid³

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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This paper presents a specific process model for Knowledge Discovery in Databases (KDD) projects aiming at availability improvement in manufacturing. For this purpose, Overall Equipment Efficiency (OEE) is analyzed and used, since it is an approved approach to monitor and improve the degree of availability in manufacturing. To define the specific process model, we use the generic CRISP-DM reference model and conduct a mapping for availability improvement. We prove the applicability of our model in the context of a specific KDD project in a large enterprise in the manufacturing industry.

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Section: Kdd In Manufacturingmentioning

confidence: 99%

How to Discover Knowledge for Improving Availability in the Manufacturing Domain?

Utz¹,

Neumann²,

Omid³

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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show abstract

“…However, each enterprise has its own unique specifics, technology and equipment, which must be taken into account when developing a solution to this problem. Therefore, the optimal production planning procedure must be developed by considering the specifics of the materials used, the features of equipment and technological processes, the requirements for quality, quantity and delivery time of finished products [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Volume Planning and Scheduling of Paper Production with Smooth Transitions by Product Grades

2023

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The article deals with the problem of calculating the volume calendar plan of a paper mill. The presented mathematical model and methods make it possible to schedule paper production orders between several paper machines (PM) to even their loading, devise cutting plans for each winder and arrange the order of their implementation. When forming cutting plans, orders are grouped in accordance with such parameters as grammage, roll diameter, core diameter, product type and number of layers. Deadlines and volumes in customer orders are taken into account. The cutting plans for each winder account for the allowable roll width limits and the maximum number of knives. To find the optimal schedule, a combination of the following criteria is used: minimal trim loss, minimal changes to the knives’ setup and smooth transitions by product grades. Solution algorithms are presented that use a combination of the simplex method, the column generation, the branch and bound methods, the greedy algorithm and the local search procedure. We tested the solution approach on real production data from a paper mill in European Russia and obtained the production sequence that better matches deadlines in customer orders compared to the plan devised manually by production planners.

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“…In the second approach, a recursive process is executed between the optimization model (typically linear) and the simulation model. Simulation results are used iteratively to refine the parameter of the analytical model (Figueira et al, 2013). In certain cases, the optimization model can be solved first and that solution can be then simulated in order to compute all the variables of interest, hence simulation helps to generate and evaluate the performance of the whole solution (SG is also known as optimization-based simulation).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of a GRASP-based approach for stochastic scheduling problems

Duarte¹,

Cepeda²,

González-Neira³

et al. 2017

10.5267/j.uscm

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Stochastic scheduling addresses several forms of uncertainty to represent better production environments in the real world. Stochastic scheduling has applications on several areas such as logistics, transportation, production, and healthcare, among others. This paper aims to evaluate the performance of various greedy functions for a GRASP-based approach, under stochastic processing times. Since simulation is used for estimating the objective function, two simulation techniques, Monte Carlo simulation and Common Random Numbers (CRN), are used to compare the performance of different greedy (utility) functions within the GRASP. In order to validate the proposed methodology, the expected total weighted tardiness minimization for a single machine problem was taken as case study. Results showed that both, CRN and Monte Carlo, are not statistically different regarding the expected weighted tardiness results. However, CRN showed a better performance in terms of simulation replications and the confidence interval size for the difference between means. Furthermore, the statistical analysis confirmed that there is a significant difference between greedy functions.Growing Science Ltd. All rights reserved. 7

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Predictive production planning in an integrated pulp and paper mill

Cited by 5 publications

References 13 publications

How to Discover Knowledge for Improving Availability in the Manufacturing Domain?

How to Discover Knowledge for Improving Availability in the Manufacturing Domain?

Optimal Volume Planning and Scheduling of Paper Production with Smooth Transitions by Product Grades

Performance evaluation of a GRASP-based approach for stochastic scheduling problems

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