scholarly journals A novel approach for solving stochastic problems with multiple objective functions

2021 ◽  
Author(s):  
ramzi kasri ◽  
fatima bellahcene
Keyword(s):  
Solution Method ◽  
Dc Programming ◽  
Decision Makers ◽  
Stochastic Linear Programming ◽  
Multivariate Distributions ◽  
Quadratic Problem ◽  
Weighting Method ◽  
Dc Algorithm ◽  
Novel Approach ◽  
Multiobjective Approach

In this paper we suggest an approach for solving a multiobjective stochastic linear programming problem with normal multivariate distributions. Our solution method is a combination between the multiobjective approach and a nonconvex technique. The problem is first transformed into a deterministic multiobjective problem introducing the expected value criterion and an utility function that represents the decision makers’ preferences. The obtained problem is reduced to a mono-objective quadratic problem using a weighting method. This last problem is solved by DC programming and DC algorithm. A numerical example is included for illustration.

Sparse Covariance Matrix Estimation by DCA-Based Algorithms

2017 ◽  
Vol 29 (11) ◽  
pp. 3040-3077 ◽  
Author(s):  
Duy Nhat Phan ◽  
Hoai An Le Thi ◽  
Tao Pham Dinh
Keyword(s):  
Covariance Matrix ◽  
Nonconvex Programming ◽  
Real Data ◽  
Dc Programming ◽  
Covariance Matrix Estimation ◽  
Data Sets ◽  
Matrix Estimation ◽  
Dc Algorithm ◽  
Difference Of Convex Functions ◽  
Novel Approach

This letter proposes a novel approach using the [Formula: see text]-norm regularization for the sparse covariance matrix estimation (SCME) problem. The objective function of SCME problem is composed of a nonconvex part and the [Formula: see text] term, which is discontinuous and difficult to tackle. Appropriate DC (difference of convex functions) approximations of [Formula: see text]-norm are used that result in approximation SCME problems that are still nonconvex. DC programming and DCA (DC algorithm), powerful tools in nonconvex programming framework, are investigated. Two DC formulations are proposed and corresponding DCA schemes developed. Two applications of the SCME problem that are considered are classification via sparse quadratic discriminant analysis and portfolio optimization. A careful empirical experiment is performed through simulated and real data sets to study the performance of the proposed algorithms. Numerical results showed their efficiency and their superiority compared with seven state-of-the-art methods.

scholarly journals A Novel Approach for Green Supplier Selection under a q-Rung Orthopair Fuzzy Environment

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 687 ◽  
Author(s):  
Rui Wang ◽  
Yanlai Li
Keyword(s):  
Supplier Selection ◽  
Weighted Average ◽  
Economic Benefits ◽  
Decision Makers ◽  
Weighting Method ◽  
Green Supplier Selection ◽  
Fuzzy Environment ◽  
Novel Approach ◽  
Evaluation Matrix ◽  
Evaluation Information

With environmental issues becoming increasingly important worldwide, plenty of enterprises have applied the green supply chain management (GSCM) mode to achieve economic benefits while ensuring environmental sustainable development. As an important part of GSCM, green supplier selection has been researched in many literatures, which is regarded as a multiple criteria group decision making (MCGDM) problem. However, these existing approaches present several shortcomings, including determining the weights of decision makers subjectively, ignoring the consensus level of decision makers, and that the complexity and uncertainty of evaluation information cannot be adequately expressed. To overcome these drawbacks, a new method for green supplier selection based on the q-rung orthopair fuzzy set is proposed, in which the evaluation information of decision makers is represented by the q-rung orthopair fuzzy numbers. Combined with an iteration-based consensus model and the q-rung orthopair fuzzy power weighted average (q-ROFPWA) operator, an evaluation matrix that is accepted by decision makers or an enterprise is obtained. Then, a comprehensive weighting method can be developed to compute the weights of criteria, which is composed of the subjective weighting method and a deviation maximization model. Finally, the TODIM (TOmada de Decisao Interativa e Multicritevio) method, based on the prospect theory, can be extended into the q-rung orthopair fuzzy environment to obtain the ranking result. A numerical example of green supplier selection in an electric automobile company was implemented to illustrate the practicability and advantages of the proposed approach.

Alternating DC algorithm for partial DC programming problems

2021 ◽  
Author(s):  
Tao Pham Dinh ◽  
Van Ngai Huynh ◽  
Hoai An Le Thi ◽  
Vinh Thanh Ho
Keyword(s):  
Dc Programming ◽  
Dc Algorithm

A Novel Weighting Method for Finding Common Weights in DEA

2017 ◽  
Vol 34 (05) ◽  
pp. 1750027 ◽  
Author(s):  
Qing Wang ◽  
Zhaojun Liu ◽  
Yang Zhang
Keyword(s):  
Peer Evaluation ◽  
Weighting Method ◽  
Dea Model ◽  
Self Evaluation ◽  
Common Weights ◽  
Input And Output ◽  
Novel Approach ◽  
Common Set Of Weights

In the traditional DEA model, each DMU maximizes its efficiency with the most favorable weights. This leads to flexibility and unreality of input and output weights. Subsequently, it is unfair to compare and rank the efficiencies of different DMUs obtained on the basis of these weights. In this paper, we propose a novel approach to determine a common set of weights with more consensus to evaluate and rank the performance of all DMUs by weighting the rescaled weights based on the degree of consensus, where the weights obtained from DEA are rescaled for comparison among DMUs. Moreover, to overcome the non-uniqueness of the weights, a novel secondary goal is developed based on the agreement between self-evaluation and peer-evaluation. In addition, the restriction of weights is taken into account to avoid trivial weights. Finally, an example of 14 international passenger airlines is used to illustrate the performance and credibility of our proposed method.

Weighting Method of Indicators for Safety Management System Assessment

2013 ◽  
Vol 850-851 ◽  
pp. 1020-1023
Author(s):  
Chang Cheng Wu ◽  
Hong Zhao ◽  
Gui Fu
Keyword(s):  
Management System ◽  
Safety Management ◽  
Management Practice ◽  
Decision Makers ◽  
Work Safety ◽  
Analytic Hierarchy ◽  
Weighting Method ◽  
Safety Management System ◽  
System Assessment ◽  
Hierarchy Process

Safety management system assessment (SMSA) was a hot point in the field of safety management practice and research. But, the weights of safety management system assessment indicators are different in former achievements. In this study, the scores of indicators in ISRS, NOSA, API and Basic Norms for Work Safety Standardization (BNWSS) were referenced to obtain the initial weights of the primary indicators for SMSA. The decision-makers preference for the four methods was settled by means of analytic hierarchy process (AHP). After Kendalls test for consistency of the four methods, the combination weighting was made considering the preference and consistence on the basis of AHP. The rationality of weighting result was evaluated with close degree. The close degree reached 91.0% which was satisfactory.

Interactive decision support for academic advising

2016 ◽  
Vol 24 (3) ◽  
pp. 349-368 ◽  
Author(s):  
Abdallah Mohamed
Keyword(s):  
Academic Advising ◽  
Optimization Techniques ◽  
Decision Makers ◽  
Content Type ◽  
Course Scheduling ◽  
Initial Application ◽  
Interactive Decision Support ◽  
Novel Approach ◽  
Challenging Tasks ◽  
The Impact

Purpose This paper aims to support academic advising, which plays a crucial role in student success and retention. The paper focuses on one of the most challenging tasks involved in academic advising: individual course scheduling. This task includes not only careful planning for different courses over several semesters according to students’ preferences and goals but also must conform to many student constraints and administrative regulations, some of which may rely on student-specific cases.. Design/methodology/approach This paper introduces a novel approach that tries to provide meaningful support to decision makers involved in the course scheduling problem. The approach uses optimization algorithms to perform a pro-active analysis of the impact of different problem aspects and eventually suggests a balanced study plan that tries to satisfy both student preferences and advisor recommendations without violating any constraints. Findings An initial application of the proposed system is used to discuss its benefits. Originality/value The paper introduces a novel approach that uses optimization techniques to support making efficient decisions during the academic advising process.

scholarly journals Tourism Competitiveness by Shift-Share Analysis to way-forward Destination Management: A case study for Sri Lanka

2020 ◽  
Vol 11 (21) ◽  
pp. 88-102
Author(s):  
Imali N. Fernando
Keyword(s):  
Sri Lanka ◽  
Competitive Advantage ◽  
Decision Makers ◽  
Competitive Strategies ◽  
Regional Tourism ◽  
Novel Approach ◽  
Competitive Disadvantage ◽  
Tourism Destinations ◽  
Tourism Arrivals

Hospitality and Tourism are among the fastest-growing sectors and the source of foreign exchange with indirect-direct employments for quite an appreciable number of economies worldwide. The nature of the sector provides an avenue towards regional development through entrepreneurship venture creations, value addition to the abandoned resources, and regeneration of abandon natural resources with new themes as a novel approach. Tourism currently in a paradigm shift as a comparative advantage of destination is becoming less important than a competitive advantage. The traditional destinations are diminishing while creating novel destinations more relaxation-oriented while leading to residents' economically enriched livelihood. The paper critically analyzes the current tourism competitive position of Sri Lanka with a panel of five rival destinations by adopting shift-share analysis by developing two propositions. Regional Tourism arrivals in rival tourism destinations have been used to perform Shift-share analysis. Findings revealed (a) Sri Lanka as a destination is gaining the competitive advantage of four tourism regions out of six markets. The competitive strategies proposed as recommendations to gain market specialization to the regions with a competitive advantage; (a) market specialization by targeting the markets with a competitive advantage, (b) new marketing programs for markets with competitive disadvantage, and (c) collaborative programs among Asian tourism destinations. The results would be beneficial to Asian region tourism decision-makers trusted with the growth and application of competitive strategies.

scholarly journals Ada-WHIPS: Explaining AdaBoost Classification with Applications in the Health Sciences

2020 ◽  
Author(s):  
Julian Hatwell ◽  
Mohamed Medhat Gaber ◽  
R.M. Atif Azad
Keyword(s):  
Machine Learning ◽  
Statistical Tests ◽  
Black Box ◽  
Decision Makers ◽  
Medical Practitioners ◽  
Research Areas ◽  
Novel Approach ◽  
Mri Scans ◽  
Practice Methods ◽  
Logical Rules

Abstract Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning (ML) models and high dimensional data sources (electronic health records, MRI scans, cardiotocograms, etc). These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. Methods In this work, we focus on AdaBoost, a black box ML model that has been widely adopted in the CAD literature. We address the challenge -- to explain AdaBoost classification -- with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost's adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees (DT) of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model's decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting .

scholarly journals Sistem Pendukung Keputusan Rekomendasi Emiten Saham Menggunakan Metode Simple Additive Weighting

2021 ◽  
Vol 2 (3) ◽  
pp. 321
Author(s):  
Renny Puspita Sari ◽  
Muhamad Rabil Maulana
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Decision Makers ◽  
Financial Ratios ◽  
Weighting Method ◽  
Stock Recommendations ◽  
The Future ◽  
Simple Additive Weighting ◽  
The Many

In the current era, stock investing is an instrument that is currently popular with Indonesian youth, stock investing is one of the many investment options that are increasingly in demand by various groups. Investing in stocks is an activity to refrain from enjoying the present for more enjoyment in the future, this investment often brings someone to be wiser in managing their finances, choosing good stocks is not an easy thing for some investors it takes many factors and ratios - financial ratios to choose stocks that can provide financing by the initial investment objectives. Therefore we need a system to help these problems. The system is a decision support system that can assist in making decisions from the available options. This Stock Issuer Recommendation Decision Support System Using the Simple Additive Weighting Method is here to assist decision-makers to choose good issuers or stocks to collect so that they can provide good profits in the future. The results of the calculation on the system using the Simple Additive Weighting method will show the best suitable stock recommendations for the user based on the data they enter.

Novel Stable Approach with Probability Distribution for Multi-Criteria Decision-Making Problems of Multi-Valued Neutrosophic Sets

2020 ◽  
Vol 19 (05) ◽  
pp. 1271-1292
Author(s):  
Xu Libo ◽  
Li Xingsen ◽  
Cui Honglei
Keyword(s):  
Decision Making ◽  
Probability Distribution ◽  
Dynamic Change ◽  
Distribution Functions ◽  
Transformation Operator ◽  
Decision Makers ◽  
Multi Criteria Decision Making ◽  
Neutrosophic Sets ◽  
Dependent Function ◽  
Novel Approach

In this paper, a novel approach and framework based on interval-dependent degree and probability distribution for multi-criteria decision-making problems with multi-valued neutrosophic sets (MVNSs) is proposed. First, a simplified dependent function and distribution function are given and integrated into a concise formula, which is called the interval-dependent function and contains interval computing and probability distribution information in an interval. Then a transformation operator is defined and it is shown how to convert MVNSs into an interval set. Subsequently, the interval-dependent function with the probability distribution of MVNSs is deduced. Finally, an example and comparative analysis are provided to verify the feasibility and effectiveness of the proposed method. In addition, uncertainty analysis, which reflects the dynamic change of the ranking result with decision-makers’ preferences, is performed by setting different distribution functions, which increases the reliability and accuracy of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document