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

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.

Artikel Strategi tentang Pengambilan Keputusan

2019 ◽  
Author(s):  
Winda Safitri Caniago ◽  
Hade Afriansyah
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Heuristic Model ◽  
Selection Of

Decision making is an action with determine the result in solving problem with choose a rule action between alternative through a mental of process, logic of process and etc. This purpose article is to help make it easier to solve a problem. This article explain some strategy decision making such as optimization model, satisfying model, mixed scanning model, heuristic model, and last the selection of certain model.

Expected consistency-based model and multiplicative DEA cross-efficiency for group decision-making with incomplete distribution linguistic preference relations

2021 ◽  
pp. 1-21
Author(s):  
Jinpei Liu ◽  
Longlong Shao ◽  
Ligang Zhou ◽  
Feifei Jin
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Optimization Model ◽  
Group Decision ◽  
Preference Relation ◽  
Preference Relations ◽  
Priority Vector ◽  
Linguistic Preference Relation ◽  
Multiplicative Preference Relations ◽  
High Consistency

Faced with complex decision problems, Distribution linguistic preference relation (DLPR) is an effective way for decision-makers (DMs) to express preference information. However, due to the complexity of the decision-making environment, DMs may not be able to provide complete linguistic distribution for all linguistic terms in DLPRs, which results in incomplete DLPRs. Therefore, in order to solve group decision-making (GDM) with incomplete DLPRs, this paper proposes expected consistency-based model and multiplicative DEA cross-efficiency. For a given incomplete DLPRs, we first propose an optimization model to obtain complete DLPR. This optimization model can evaluate the missing linguistic distribution and ensure that the obtained DLPR has a high consistency level. And then, we develop a transformation function that can transform DLPRs into multiplicative preference relations (MPRs). Furthermore, we design an improved multiplicative DEA model to obtain the priority vector of MPR for ranking all alternatives. Finally, a numerical example is provided to show the rationality and applicability of the proposed GDM method.

scholarly journals Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jianfei Ye ◽  
Huimin Ma
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Preventive Maintenance ◽  
Production Scheduling ◽  
Job Shop ◽  
Joint Optimization ◽  
Planning Problem ◽  
Flexible Job Shop ◽  
Maintenance Planning ◽  
Joint Decision

In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

scholarly journals Constraint Programming Approach for Optimizing Business Asset Maintenance Strategy

2019 ◽  
Vol 1 (01) ◽  
Author(s):  
Gilang Ramadhan ◽  
Shu Shun Liu
Keyword(s):  
Decision Making ◽  
Constraint Programming ◽  
Optimization Model ◽  
Maintenance Treatment ◽  
Treatment Options ◽  
Programming Approach ◽  
Building Maintenance ◽  
Maintenance Program ◽  
Building Condition ◽  
Decision Making Tool

There are many buildings with various conditions in Indonesia and some of them are not in finest conditions that need maintenance treatment urgently. The absence of building maintenance decision-making tool and limited budget are among main factors that cause unmanageable maintenance program. Therefore, this study has been conducted to propose an optimization model that is capable to determine the most appropriate building maintenance treatment. This study applied Constraint Programming (CP) approach to select the most economical maintenance treatment for a certain building and to allocate annual maintenance budget. CP-based model in this study subjects to constraint of budget and targeted level of building condition. In this study, maintenance treatment options, budget, time period, building deterioration rates, and the minimum standard of building condition were set. The model was run in IBM ILOG CPLEX Optimization Studio since the software is very efficient and effective in processing the optimization model. Furthermore, a case study was carried out to run the model involving 41 buildings in a 10-year period, and two different scenarios were conducted to examine the optimization model. The results successfully validated that the model can be a decision-making tool in selecting and prioritizing effective maintenance treatment.

scholarly journals Business Intelligence System via Group Decision Making

2016 ◽  
Vol 16 (3) ◽  
pp. 219-229 ◽  
Author(s):  
Daniela Borissova ◽  
Ivan Mustakerov ◽  
Dilian Korsemov
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Business Intelligence ◽  
Optimization Model ◽  
Web Application ◽  
Group Decision ◽  
Evaluation Criteria ◽  
Decision Makers ◽  
Intelligence System ◽  
Three Layer Architecture

Abstract In the paper a business intelligence tool based on group decision making is proposed. The group decision making uses a combinatorial optimization modeling technique. It takes into account weighted coefficients for evaluation criteria assigned by decision makers together with their scores for the alternatives in respect of these criteria. The proposed optimization model for group decision making considers also the knowledge level of the group members involved as decision makers. This optimization model is implemented in three-layer architecture of Web application for business intelligence by group decision making. Developed Web application is numerically tested for a representative problem for software choice considering six decision makers, three alternatives and 19 evaluation criteria. The obtained results show the practical applicability and effectiveness of the proposed approach.

scholarly journals The optimization model of the logging machinery usage in forestry practice

2009 ◽  
Vol 57 (6) ◽  
pp. 105-112 ◽  
Author(s):  
Jitka Janová ◽  
M. Lindnerová
Keyword(s):  
Decision Making ◽  
Czech Republic ◽  
Decision Support ◽  
Decision Support Systems ◽  
Optimization Model ◽  
Support Systems ◽  
Decision Problems ◽  
Optimization Models ◽  
Ongoing Research ◽  
The Impact

The decision support systems commonly used in industry and economy managerial practice for optimizing the processes are based on algoritmization of the typical decision problems. In Czech forestry business, there is a lack of developed decision support systems, which could be easily used in daily practice. This stems from the fact, that the application of optimization methods is less successful in forestry decision making than in industry or economy due to inherent complexity of the forestry decision problems. There is worldwide ongoing research on optimization models applicable in forestry decision making, but the results are not globally applicable and moreover the cost of possibly arising software tools are indispensable. Especially small and medium forestry companies in Czech Republic can not afford such additional costs, although the results of optimization could positively in­fluen­ce not only the business itself but also the impact of forestry business on the environment. Hence there is a need for user friendly optimization models for forestry decision making in the area of Czech Republic, which could be easily solved in commonly available software, and whose results would be both, realistic and easily applicable in the daily decision making.The aim of this paper is to develop the optimization model for the machinery use planning in Czech logging firm in such a way, that the results can be obtained using MS EXCEL. The goal is to identify the integer number of particular machines which should be outsourced for the next period, when the total cost minimization is required. The linear programming model is designed covering the typical restrictions on available machinery and total volume of trees to be cut and transported. The model offers additional result in the form of optimal employment of particular machines. The solution procedure is described in detail and the results obtained are discussed with respect to its applicability in practical forestry decision making. The possibility of extension of suggested model by including additional requirements is mentioned and the example for the wood manipulation requirement is shown.

INDUCING DYNAMIC RULES OF NATIONS' COMPETITIVENESS FROM 2001–2005 MCI-WCY

2009 ◽  
Vol 08 (03) ◽  
pp. 549-580 ◽  
Author(s):  
HAN-LIN LI ◽  
YU-CHIEN KO
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Regression Model ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization Model ◽  
Business Decision ◽  
The World

A nation's competitiveness has become more and more important in forming government strategy and business decision making. This study proposes an optimization model, instead of regression model or neural network model, to induce rules for dynamic nations' competitiveness based on the Major Competitiveness Indicators of the World Competitiveness Yearbook. Fourteen attributes are used to form the dynamic rules expressed in "IF…THEN…" forms. According to the induced rules, the strategic implications are suggested for various groups of nations to improve or to sustain their competitiveness.

Bayesian Belief Networks for the metamodeling of simulation-optimization model to identify optimum water allocation scenario, Application in Miyandoab plain, Urmia Lake basin, Iran

2021 ◽  
Author(s):  
Amirhossein Dehghanipour ◽  
Gerrit Schoups ◽  
Hossein Babazadeh ◽  
Majid Ehtiat ◽  
Bagher Zahabiyoun
Keyword(s):  
Decision Making ◽  
Optimization Model ◽  
Water Demand ◽  
Simulation Optimization ◽  
Bayesian Belief Networks ◽  
Environmental Water ◽  
Lake Basin ◽  
Decision Making Models ◽  
Validation Tests ◽  
Urmia Lake Basin

<p>In this study, decision-making models in uncertain conditions are developed to identify optimal strategies for reducing competition between agricultural and environmental water demand. The decision-making models are applied to the irrigated Miyandoab Plain, located upstream of endorheic Lake Urmia in Northwestern Iran. Decision-making models are conceptualized based on static and dynamic Bayesian Belief Networks (BBN). The static BBN evaluates the effects of management strategies and drought conditions on environmental flow and agricultural profit at the annual scale, while the dynamic BBN accounts for monthly dynamics of water demand and conjunctive use. The reliability and performance of BBNs depend on the quantity and quality of data used to train the BBN and create conditional probability tables (CPTs). In this study, simulated outputs from a multi-period simulation-optimization model (Dehganipour et al., 2020) are used to populate the CPTs in each BBN and reduce the BBN training error. Cross-validation tests and sensitivity analysis are used to evaluate the effectiveness of the resulting BBNs. Sensitivity analysis shows that drought conditions have the most significant impact on environmental flow compared to other variables. Cross-validation tests show that the BBNs are able to reproduce outputs of the complex simulation-optimization model used for training, and therefore provide a computationally fast alternative for decision-making under uncertainty.</p><p><strong>Reference:</strong> Dehghanipour, A. H., Schoups, G., Zahabiyoun, B., & Babazadeh, H. (2020). Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran. Agricultural Water Management, 241, 106353.</p>

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