scholarly journals Decision-Making Method based on Mixed Integer Linear Programming and Rough Set: A Case Study of Diesel Engine Quality and Assembly Clearance Data

2019 ◽  
Vol 11 (3) ◽  
pp. 620 ◽  
Author(s):  
Wenbing Chang ◽  
Xinglong Yuan ◽  
Yalong Wu ◽  
Shenghan Zhou ◽  
Jingsong Lei ◽  
...  
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Mixed Integer Linear Programming ◽  
Rough Set Theory ◽  
Mixed Integer ◽  
Quality Level ◽  
Proposed Model

The purpose of this paper is to establish a decision-making system for assembly clearance parameters and machine quality level by analyzing the data of assembly clearance parameters of diesel engine. Accordingly, we present an extension of the rough set theory based on mixed-integer linear programming (MILP) for rough set-based classification (MILP-FRST). Traditional rough set theory has two shortcomings. First, it is sensitive to noise data, resulting in a low accuracy of decision systems based on rough sets. Second, in the classification problem based on rough sets, the attributes cannot be automatically determined. MILP-FRST has the advantages of MILP in resisting noisy data and has the ability to select attributes flexibly and automatically. In order to prove the validity and advantages of the proposed model, we used the machine quality data and assembly clearance data of 29 diesel engines of a certain type to validate the proposed model. Experiments show that the proposed decision-making method based on MILP-FRST model can accurately determine the quality level of the whole machine according to the assembly clearance parameters.

(α, β)-Multi-granulation bipolar fuzzified rough sets and their applications to multi criteria group decision making

2021 ◽  
pp. 1-36
Author(s):  
Rizwan Gul ◽  
Muhammad Shabir
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Group Decision Making ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Group Decision ◽  
Research Direction ◽  
New Approach ◽  
Accuracy Of Approximation

Pawlak’s rough set theory based on single granulation has been extended to multi-granulation rough set structure in recent years. Multi-granulation rough set theory has become a flouring research direction in rough set theory. In this paper, we propose the notion of (α, β)-multi-granulation bipolar fuzzified rough set ((α, β)-MGBFRSs). For this purpose, a collection of bipolar fuzzy tolerance relations has been used. In the framework of multi-granulation, we proposed two types of (α, β)-multi-granulation bipolar fuzzified rough sets model. One is called the optimistic (α, β)-multi-granulation bipolar fuzzified rough sets ((α, β) o-MGBFRSs) and the other is called the pessimistic (α, β)-multi-granulation bipolar fuzzified rough sets ((α, β) p-MGBFRSs). Subsequently, a number of important structural properties and results of proposed models are investigated in detail. The relationships among the (α, β)-MGBFRSs, (α, β) o-MGBFRSs and (α, β) p-MGBFRSs are also established. In order to illustrate our proposed models, some examples are considered, which are helpful for applying this theory in practical issues. Moreover, several important measures associated with (α, β)-multi-granulation bipolar fuzzified rough set like the measure of accuracy, the measure of precision, and accuracy of approximation are presented. Finally, we construct a new approach to multi-criteria group decision-making method based on (α, β)-MGBFRSs, and the validity of this technique is illustrated by a practical application. Compared with the existing results, we also expound its advantages.

Investment Risk Assessment of Hydraulic Engineering Based on Rough Set Theory

2014 ◽  
Vol 584-586 ◽  
pp. 2640-2643
Author(s):  
Zhi Ding Chen ◽  
Hai Man Gao ◽  
Qi Guo
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Hydraulic Engineering ◽  
New Method ◽  
Decision Table ◽  
Investment Risk

The rough set theory is a new method for analyzing and dealing with data. By using this theory, we proposed a risk assessment algorithm based on rough set theory, which was described in detail in this paper. the decision table can be simplified and redundant attributes can be got rid of A method of inference based on the knowledge of rough sets and an example to show how to acquire the rules of new decision making, thus filling the method with a practical and publicizing value are given.

scholarly journals Multi-Granulation Neutrosophic Rough Sets on a Single Domain and Dual Domains with Applications

Symmetry ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 296 ◽  
Author(s):  
Chunxin Bo ◽  
Xiaohong Zhang ◽  
Songtao Shao ◽  
Florentin Smarandache
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Group Decision Making ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Group Decision ◽  
Single Domain ◽  
Particle Structure ◽  
Interesting Direction

It is an interesting direction to study rough sets from a multi-granularity perspective. In rough set theory, the multi-particle structure was represented by a binary relation. This paper considers a new neutrosophic rough set model, multi-granulation neutrosophic rough set (MGNRS). First, the concept of MGNRS on a single domain and dual domains was proposed. Then, their properties and operators were considered. We obtained that MGNRS on dual domains will degenerate into MGNRS on a single domain when the two domains are the same. Finally, a kind of special multi-criteria group decision making (MCGDM) problem was solved based on MGNRS on dual domains, and an example was given to show its feasibility.

Uncertainty and Vagueness Concepts in Decision Making

2011 ◽  
pp. 901-909
Author(s):  
Georg Peters
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
17Th Century ◽  
Basic Principles ◽  
In The Beginning

One of the main challenges in decision making is how to deal with uncertainty and vagueness. The classic uncertainty concept is probability, which goes back to the 17th century. Possible candidates for the title of father of probability are Bernoulli, Laplace, and Pascal. Some 40 years ago, Zadeh (1965) introduced the concept of fuzziness, which is sometimes interpreted as one form of probability. However, we will show that the terms fuzzy and probability are complementary. Recently, in the beginning of the ’80s, Pawlak (1982) suggested rough sets to manage uncertainties. The objective of this article is to give a basic introduction into probability, fuzzy set, and rough set theory and show their potential in dealing with uncertainty and vagueness. The article is structured as follows. In the next three sections we will discuss the basic principles of probability, fuzzy sets, and rough sets, and their relationship with each other. The article concludes with a short summary.

Analysing diabetes using rough sets

2020 ◽  
pp. 533-538
Author(s):  
S. Arjun Raj ◽  
M. Vigneshwaran
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
International Diabetes Federation ◽  
Published Data ◽  
Lower And Upper Approximations

In this article we use the rough set theory to generate the set of decision concepts in order to solve a medical problem.Based on officially published data by International Diabetes Federation (IDF), rough sets have been used to diagnose Diabetes.The lower and upper approximations of decision concepts and their boundary regions have been formulated here.

How can mixed integer linear programming assist dairy manufacturers by integrating supply decisions and production planning?

2021 ◽  
Vol 11 (2) ◽  
pp. 178-193
Author(s):  
Juliana Emidio ◽  
Rafael Lima ◽  
Camila Leal ◽  
Grasiele Madrona
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Dairy Products ◽  
Raw Milk ◽  
Mixed Integer ◽  
Content Type ◽  
Proposed Model ◽  
By Products

PurposeThe dairy industry needs to make important decisions regarding its supply chain. In a context with many available suppliers, deciding which of them will be part of the supply chain and deciding when to buy raw milk is key to the supply chain performance. This study aims to propose a mathematical model to support milk supply decisions. In addition to determining which producers should be chosen as suppliers, the model decides on a milk pickup schedule over a planning horizon. The model addresses production decisions, inventory, setup and the use of by-products generated in the raw milk processing.Design/methodology/approachThe model was formulated using mixed integer linear programming, tested with randomly generated instances of various sizes and solved using the Gurobi Solver. Instances were generated using parameters obtained from a company that manufactures dairy products to test the model in a more realistic scenario.FindingsThe results show that the proposed model can be solved with real-world sized instances in short computational times and yielding high quality results. Hence, companies can adopt this model to reduce transportation, production and inventory costs by supporting decision making throughout their supply chains.Originality/valueThe novelty of the proposed model stems from the ability to integrate milk pickup and production planning of dairy products, thus being more comprehensive than the models currently available in the literature. Additionally, the model also considers by-products, which can be used as inputs for other products.

A User-Centered Medical Device Design Decision Making Approach Using Hybrid Rough Cooperative Game Model

2021 ◽  
Author(s):  
Liting Jing ◽  
Junfeng Ma
Keyword(s):  
Decision Making ◽  
Medical Device ◽  
Set Theory ◽  
Cooperative Game ◽  
Rough Set ◽  
Rough Set Theory ◽  
Design Decision ◽  
Device Design ◽  
Medical Device Design ◽  
User Centered

Abstract With the advancement of new technologies and diverse customer-centered design requirements, the medical device design decision making becomes challenge. Incorporating multiple stakeholders’ requirements into the medical device design will significantly affect the market competitiveness and performance. The classic design decision making approaches mainly focused on design criteria priority determination and conceptual schemes evaluation, which lack the capacity of reflecting the interdependence of interest among stakeholders and capturing the ambiguous influence on the overall design expectations, leading to the unreliable decision making results. In order to relax these constraints in the medical device design, this paper incorporates rough set theory with cooperative game theory model to develop a novel user-centered design decision making framework. The proposed approach is composed of three components: 1) end/professional user needs identification and classification, 2) evaluation criteria correlation diagram and scheme value matrix establishment using rough set theory; and 3) fuzzy coalition utility model development to obtain optimal desirability considering users’ conflict interests. We used a blood pressure meter case study to demonstrate and validate the proposed approach. Compared with the traditional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, the proposed approach is more robust.

An approach to evaluation of emergency plans for unconventional emergency events based on soft fuzzy rough set

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 461-473 ◽  
Author(s):  
Sun Bingzhen ◽  
Ma Weimin
Keyword(s):  
Decision Making ◽  
Emergency Management ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Management Decision ◽  
Fuzzy Rough Set ◽  
Emergency Plans ◽  
Content Type ◽  
Emergency Events

Purpose – The purpose of this paper is to present a new method for evaluation of emergency plans for unconventional emergency events by using the soft fuzzy rough set theory and methodology. Design/methodology/approach – In response to the problems of insufficient risk identification, incomplete and inaccurate data and different preference of decision makers, a new model for emergency plan evaluation is established by combining soft set theory with classical fuzzy rough set theory. Moreover, by combining the TOPSIS method with soft fuzzy rough set theory, the score value of the soft fuzzy lower and upper approximation is defined for the optimal object and the worst object. Finally, emergency plans are comprehensively evaluated according to the soft close degree of the soft fuzzy rough set theory. Findings – This paper presents a new perspective on emergency management decision making in unconventional emergency events. Also, the paper provides an effective model for evaluating emergency plans for unconventional events. Originality/value – The paper contributes to decision making in emergency management of unconventional emergency events. The model is useful for dealing with decision making with uncertain information.

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