Lean Process Optimization Model for Improving Processing Times and Increasing Service Levels Using a Deming Approach in a Fishing Net Textile Company

2021 ◽  
pp. 443-451
Author(s):  
Anthuane Carrillo-Corzo ◽  
Erick Tarazona-Gonzales ◽  
Juan Quiroz-Flores ◽  
Gino Viacava-Campos
Keyword(s):  
Process Optimization ◽  
Optimization Model ◽  
Service Levels ◽  
Processing Times

A Regional Pulp and Paper Process Optimization Model for Energy Analysis and Its Application for R&D Decision Making

1980 ◽  
Vol 12 (2) ◽  
pp. 186-198
Author(s):  
David A. Pilati ◽  
F. T. Sparrow ◽  
Jason Chang ◽  
Richard A. Rosen
Keyword(s):  
Decision Making ◽  
Process Optimization ◽  
Optimization Model ◽  
Energy Analysis ◽  
Pulp And Paper

scholarly journals Lab Scale Implementation of Industry 4.0 for an Automatic Yogurt Filling Production System—Experimentation, Modeling, and Process Optimization

2021 ◽  
Vol 11 (21) ◽  
pp. 9821
Author(s):  
Bashir Salah ◽  
Razaullah Khan ◽  
Muawia Ramadan ◽  
Rafiq Ahmad ◽  
Waqas Saleem
Keyword(s):  
Process Optimization ◽  
Feed Rate ◽  
Production System ◽  
Industry 4.0 ◽  
Data Exchange ◽  
Service Providers ◽  
Real Life ◽  
Conveyor Belt ◽  
Time Data ◽  
Processing Times

Currently, Industry 4.0 is word of mouth, and its implementation has gained increased attention from industrial and academic researchers, entrepreneurs, and service providers all over the world. With Industry 4.0, the integration of facilities and products enables real-time data exchange, and the overall production system becomes self-reliant and intelligent to predict and maintain its operational performance. In this research, the lab-scale implementation of Industry 4.0 is implemented for an automatic yogurt filling production system. A mathematical model for the process optimization of Industry 4.0 was also developed. A real-life problem was solved optimally using linear programming techniques with the objective of maximizing the speed of the conveyor belt. Moreover, the sequencing of processing orders using single-dimensional rules was performed. The effects of changes in the feed rate of the yogurt valve and length of the conveyor belt on the feed rate of the flavor valve, speed of conveyor belt, waiting time, processing times, and the different performance measures were investigated at the end.

scholarly journals Load Distribution of Evolutionary Algorithm for Complex-Process Optimization Based on Differential Evolutionary Strategy in Hot Rolling Process

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xu Yang ◽  
Chang-bin Hu ◽  
Kai-xiang Peng ◽  
Chao-nan Tong
Keyword(s):  
Evolutionary Algorithm ◽  
Process Optimization ◽  
Hot Rolling ◽  
Optimization Model ◽  
Load Distribution ◽  
Rolling Force ◽  
Rolling Process ◽  
Evolutionary Strategy ◽  
Hot Rolling Process ◽  
Complex Process

Based on the hot rolling process, a load distribution optimization model is established, which includes rolling force model, thickness distribution model, and temperature model. The rolling force ratio distribution and good strip shape are integrated as two indicators of objective function in the optimization model. Then, the evolutionary algorithm for complex-process optimization (EACOP) is introduced in the following optimization algorithm. Due to its flexible framework structure on search mechanism, the EACOP is improved within differential evolutionary strategy, for better coverage speed and search efficiency. At last, the experimental and simulation result shows that evolutionary algorithm for complex-process optimization based on differential evolutionary strategy (DEACOP) is the organism including local search and global search. The comparison with experience distribution and EACOP shows that DEACOP is able to use fewer adjustable parameters and more efficient population differential strategy during solution searching; meanwhile it still can get feasible mathematical solution for actual load distribution problems in hot rolling process.

Digital Platform for E&P Assets Business Process Optimization with a Module for Estimation and Optimizing of Portfolio of Investment Projects for Oil and Gas Production. Case Study.

2021 ◽  
Author(s):  
Irina Igorevna Frolova ◽  
Dmitry Olegovich Tailakov ◽  
Nikita Konstantinovich Kayurov ◽  
Stanislav Anatolyevich Frolov ◽  
Denis Nikolaevich Tokarev ◽  
...  
Keyword(s):  
Process Optimization ◽  
Optimization Model ◽  
Business Processes ◽  
Oil And Gas ◽  
Gas Production ◽  
Economic Indicators ◽  
Process Ontology ◽  
Oil And Gas Production ◽  
Investment Projects ◽  
Wide Range

Abstract Due to increase in cost and complexity of oil and gas production processes it becomes necessary to develop a platform for automated analysis of the main business processes of oil and gas assets for the automatic formation of investment projects portfolios for oil and gas production, taking into account the variability of technical and infrastructural characteristics, the mutual influence of objects and profitability indicators. The paper presents a description of platform prototype designed to form an optimized portfolio of investment projects for oil and gas production. The platform was developed using a process ontology, modern optimization tools and up-to-date techniques. The peculiarity of the proposed platform is in the use of a process ontology, which allows to link the life-time processes of objects with each other and throughout the entire time of asset assessment. The platform makes it easy to operate with real objects and their characteristics. The platform is based on the following models: – simulation model - repeats the business processes of the enterprise, indicating bottlenecks and improvement zones for management, it is a set of direct mathematical problems; – optimization model - creates opportunities for multi-criteria analysis, by eliminating manual processing, for modeling and managing various processes, as well as creating a wide range of development options (geological capabilities, geophysical interpretation, etc.). It is a solution to inverse problems with respect to specified criteria related to economic indicators. This paper shows the effect of implementing software based on the digital twin of enterprise processes, implemented as an integrated platform with the ability to connect specialized programs and simulators (SAP, 1C, IPM GAP, Repos, Eclipse, etc.) To calculate the economic indicators (FCF, NPV, PI, DPP) of individual investment projects and the formation, the possibilities of process optimization were taken into account in order to achieve the target indicators of the enterprise. As a result of the optimization, there is an enumeration of the options according to the algorithm and the selection of the most optimal ones, taking into account a variety of technological, landscape and hydrodynamic characteristics. Thus, a high-quality assessment of investment projects and taking into account a variety of characteristics when forming a portfolio of investment projects when forming a strategy for the development of an oil and gas asset is required. The novelty of the work lies in the developed multi-criteria optimization model. As a result of the performed work, the accuracy of calculations of technical and economic indicators and the optimality of the selected project portfolios for a given target function, taking into account the restrictions, implemented on the basis of digital twins of key business processes of oil and gas enterprises, were confirmed.

An outcome-based process optimization model using fuzzy-based association rules

2018 ◽  
Vol 118 (6) ◽  
pp. 1138-1152 ◽  
Author(s):  
Henry Lau ◽  
C.K.M. Lee ◽  
Dilupa Nakandala ◽  
Paul Shum
Keyword(s):  
Decision Making ◽  
Process Optimization ◽  
Association Rules ◽  
Optimization Model ◽  
Process Mining ◽  
Optimization Approach ◽  
Content Type ◽  
Actual Environment ◽  
Human Thinking ◽  
Fuzzy Regions

Purpose The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment. Design/methodology/approach This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes. Findings The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome. Research limitations/implications The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results. Originality/value Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

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