scholarly journals Redistributed manufacturing system under uncertain evaluation using multi criteria decision making

2021 ◽  
Vol 12 (3) ◽  
pp. 3895-3908
Author(s):  
Ponugupati Narendra Mohan Et.al
Keyword(s):  
Decision Making ◽  
Research Work ◽  
Dynamic Programming Algorithm ◽  
Big Data Analytics ◽  
Global Market ◽  
Programming Algorithm ◽  
Multi Criteria Decision Making ◽  
Firm Level ◽  
Data Set ◽  
Value Analysis

Man In recent day’s occurrence of a global crisis in Environmental (Emission of pollutants) and in Health (Pandemic COVID-19) created a recession in all sectors. The innovations in technology lead to heavy competition in global market forcing to develop new variants especially in the automobile sector. This creates more turbulence in demand at the production of new models, maintenance of existing models that are obsolete while implementation of Bharat Standard automobile regulatory authority BS-VI of India. In this research work developed a novel model of value analysis is integrated by multi-objective function with multi-criteria decision-making analysis by incorporating the big data analytics with green supply chain management to bridge the gap in demand to an Indian manufacturing sector using a firm-level data set using matrix chain multiplication dynamic programming algorithm and the computational results illustrates that the algorithm proposed is effective.

scholarly journals INNOVATION IN EASTERN EUROPE & CENTRAL ASIA: A MULTI-CRITERIA DECISION-MAKING APPROACH

2019 ◽  
Vol 7 (3) ◽  
pp. 98-121
Author(s):  
Özgür KABADURMUŞ ◽  
Fatma Nur Karaman KABADURMUŞ
Keyword(s):  
Decision Making ◽  
Multi Criteria Decision Making ◽  
Eastern European ◽  
Competitive Advantages ◽  
Firm Level ◽  
Data Set ◽  
Process Innovations ◽  
Central Asian ◽  
Level Data ◽  
Criteria Weights

In today’s intense competition environment, innovation levels of countries determine their competitive advantages. This study compares the innovation levels of Eastern European and Central Asian (EECA) countries using multi-criteria decision-making methods. The firm-level data set of the World Bank on innovation (BEEPS data) is used to evaluate innovation levels and capabilities of the countries in the region. In our proposed TOPSIS based methodology, countries are compared in terms of four different innovation types (New Product, New Organization, New Marketing, and New Process Innovations). Also, we provide an extensive sensitivity analysis to show the changes in the innovation rankings of the countries wıth different criteria weights.

Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Henry Lau ◽  
Yung Po Tsang ◽  
Dilupa Nakandala ◽  
Carman K.M. Lee
Keyword(s):  
Decision Making ◽  
Evaluation System ◽  
Small And Medium Enterprises ◽  
Handling Time ◽  
Cold Chain ◽  
Pairwise Comparisons ◽  
Temperature Sensitive ◽  
Multi Criteria Decision Making ◽  
Firm Level ◽  
Content Type

PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

An integrated fuzzy-based multi-criteria decision-making approach for the selection of an effective manufacturing system

2018 ◽  
Vol 25 (1) ◽  
pp. 280-296 ◽  
Author(s):  
Ram Prakash ◽  
Sandeep Singhal ◽  
Ashish Agarwal
Keyword(s):  
Decision Making ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Work ◽  
Sustainable Growth ◽  
Multi Criteria Decision Making ◽  
Content Type ◽  
Manufacturing Company ◽  
Selection Of

Purpose The research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to develop an integrated fuzzy-based multi-criteria decision-making (F-MCDM) framework to assist management of the case company in the selection of most effective manufacturing system. The framework helps in prioritizing the manufacturing systems on the basis of their effectiveness affected by the barriers. Design/methodology/approach In this paper, on the basis of experts’ opinion, five barriers have been identified in a brain-storming session. The problem of prioritization of manufacturing system is a multi-criteria decision-making (MCDM) problem and hence is solved by using the F-MCDM approach using dominance matrix. Findings Manufacturing systems’ effectiveness for Indian industries is influenced by barriers. The prioritization of manufacturing systems depends on qualitative factor decision-making criteria. Among the manufacturing systems, leagile manufacturing system is given the highest priority followed by lean manufacturing system, agile manufacturing system, flexible manufacturing system and cellular manufacturing system. Research limitations/implications The selection of an appropriate manufacturing system plays a vital role for sustainable growth of the manufacturing company. In the present work, barriers which influence the effectiveness of manufacturing system have been identified. On the basis of degree of influence of barriers on the effectiveness of the manufacturing system, five alternative manufacturing systems are prioritized. The framework will help the management of the case company to take reasonable decision for the adoption of the appropriate manufacturing system. Practical implications The results of the research work are very useful for the manufacturing companies interested in analyzing the alternative manufacturing systems on the basis of their effectiveness and their sensitivity toward various barriers. The management of Indian manufacturing company will take decision to adopt a manufacturing system whose effectiveness is least sensitive toward barriers. Effectiveness of such manufacturing system will improve with time without having retardation due to barriers. With improved effectiveness of the manufacturing system, the manufacturing company would be able to survive with global competition. The result of the present work is based on the inputs from the case company and may vary for the other manufacturing company. In the present work, only five alternative manufacturing systems and five barriers have been considered. To obtain the better result, MCDM approach with more number of alternative manufacturing systems and barriers might be considered. Originality/value The research work is based on the fuzzy analytic hierarchy process framework and on the case study conducted by the authors. The work carried out is original in nature and based on the real-life case study.

scholarly journals Big Data Analytics using Swarm Intelligence based Framework for Prediction on Datasets

2019 ◽  
Vol 8 (4) ◽  
pp. 7356-7360
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Information Source ◽  
Research Work ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Raw Data

Data Analytics is a scientific as well as an engineering tool used to investigate the raw data to revamp the information to achieve knowledge. This is normally connected with obtaining knowledge from reliable information source and rapidity in information processing, and future prediction of the data analysis. Big Data analytics is strongly evolving with different features of volume, velocity and Vectors. Most of the organizations are now concentrating on analyzing information or raw data that are fascinated in deploying analytics to survive forthcoming issues and challenges. The prediction model or intelligent model is proposed in this research to apply machine learning algorithms in the data set. Then it is interpreted and to analyze the better forecast value of the study. The major objective of this research work is to find the optimum prediction from the medical data set using the machine learning techniques.

Modeling enablers of knowledge management process using multi criteria decision making approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nishant Agrawal
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Research Work ◽  
Research Area ◽  
Multi Criteria Decision Making ◽  
Interpretive Structural Modeling ◽  
Content Type ◽  
Effect Relationship ◽  
Integrated Method ◽  
Knowledge Based

Purpose Drawing from boundary-spamming knowledge processes and knowledge-based theory, the purpose of this paper is to study enablers of the knowledge management (KM) process using robust multi-criteria decision-making (MCDM) tools like interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) method. Design/methodology/approach Drawing on the knowledge-based view and through the detailed literature review among different KM success, eight enablers were identified. By using the ISM-DEMATEL approach, a systematic framework was designed, and further cause–effect relationship diagram visualized a causal relationship among the enablers. Findings The combined approach of ISM-DEMATEL showcase that “knowledge creation” and “knowledge capture” are essential enablers. These two identified enablers have considered being pillars for KM implementation. On the other side, knowledge organization, knowledge application are dependent enablers. Practical implications From a practical viewpoint, the findings of this research work enable the industry consultants to identify the most prominent driving enablers for KM implementation. Additionally, it provides a clue for the effective implementation of KM in a systematic approach. Originality/value The integrated method depending on the hierarchical model and cause–effect relationship between enablers of the KM process is a novel approach that opens a new research area in this domain. Moreover, this is the first-ever attempt to combine ISM along with DEMATEL to identify enablers of the KM process.

A hybrid multi-criteria decision-making model for optimal coal blending

2019 ◽  
Vol 14 (2) ◽  
pp. 339-359 ◽  
Author(s):  
Shankar Chakraborty ◽  
Ankan Mitra
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
Research Work ◽  
Carbon Dioxide Emission ◽  
Preference Ranking ◽  
Multi Criteria Decision Making ◽  
Content Type ◽  
Geographical Regions ◽  
Coal Blending ◽  
Decision Making Model

Purpose The purpose of this paper is thus to develop a hybrid decision-making model for optimal coal blending strategy. Coal is one of the major resources contributing to generation of electricity and anthropogenic carbon-dioxide emission. Being formed from dead plant matter, it undergoes a series of morphological changes from peat to lignite, and finally to anthracite. Because of non-uniform distribution of coal over the whole earth and continuous variation in its compositions, coals mined from different parts of the world have widely varying properties. Hence, it requires an ideal blending strategy such that the coking coal having the optimal combination of all of its properties can be used for maximum benefit to the steel making process. Design/methodology/approach In this paper, a multi-criteria decision-making approach is proposed while integrating preference ranking organization method for enrichment of evaluations (PROMETHEE II and V) and geometrical analysis for interactive aid (GAIA) method to aid in formulating an optimal coal blending strategy. The optimal decision is arrived at while taking into account some practical implications associated with blending of coal, such as coal price from different reserves. Findings Different grades of coal are ranked from the best to the worst to find out the composition of constituent coals in the final blending process. Coals from the mines of two different geographical regions are considered here so as to prove the applicability of the proposed model. Adoption of this hybrid decision-making model would subsequently improve the performance of coal after blending and help in addressing some sustainability issues, like less pollution. Originality/value As this model takes into account the purchase price of coals from different reserves, it is always expected to provide more realistic solutions. Thus, it would be beneficial to deploy this decision-making model to different blending optimization problems in other spheres of a manufacturing industry. This model can further accommodate some more realistic criteria, such as availability of coal in different reserves as a topic of future research work.

Adoption of Online Marketing for Service SMEs with Multi-Criteria Decision-Making Approach

2020 ◽  
pp. 356-374
Author(s):  
Lanndon Ocampo ◽  
Rosalin Merry Berdin Alarde ◽  
Dennis Anthony Kilongkilong ◽  
Antonio Esmero
Keyword(s):  
Decision Making ◽  
Small And Medium Enterprises ◽  
Analytic Network Process ◽  
Global Market ◽  
Service Industries ◽  
Online Marketing ◽  
Multi Criteria Decision Making ◽  
Network Process ◽  
Mcdm Method ◽  
Medium Enterprises

This chapter attempts to fill in the gap of evaluating the viability of adopting online marketing for small and medium enterprises (SMEs) in service industries. As SMEs are generally characterized by shortage of resources, the use of online marketing strategies is apparently difficult. However, the current landscape of competition among SMEs in a global market economy prompts the necessity of adopting online marketing. With these, the decision-making process of SMEs in this area becomes complex and the decisions must integrate complex and interrelating criteria and constructs in order to provide a more holistic solution. Thus, this work adopts a multi-criteria decision-making (MCDM) method particularly the analytic network process (ANP) in order to evaluate the practicability of using online marketing for service SMEs. It becomes highly relevant as it provides significant insights to decision-makers in SMEs regarding the use of online marketing strategy. The contribution of this chapter lies in the application of MCDM in evaluating viability of online marketing in service SMEs.

scholarly journals Analysis and Evaluation of Indian Industrial System Requirements and Barriers Affect During Implementation of Industry 4.0 Technologies

2021 ◽  
Author(s):  
ABINASH JENA ◽  
Saroj Kumar Patel
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Research Work ◽  
Global Market ◽  
Manufacturing Industries ◽  
Industrial System ◽  
Data Set ◽  
Term Survival ◽  
Automatic Data ◽  
System Requirements

Abstract In recent years, competition among the Indian Manufacturing Industries (IMI) has increased enormously in the global market. The current uncertainty in the market context is characterised and governed by the customised requirements of the customers. Thus, the manufacturing system in the industries should be capable of adapting the parameters like flexibility in scalability, variety, agility, system responsiveness, inter-connectivity, automatic data exchange with communication among the manufacturing systems, transparency and human-machine interaction, which are the main components and principles of Industry 4.0 (I4.0). Thus, adopting I4.0 plays a vital role to corroborate and its long-term survival in the global marketplace. However, very few research work considerations contribute to the issues induced during the adoption of I4.0 in manufacturing industries. This paper aims to minimise the gap between the existing Industrial System Requirements (ISR) and the challenges faced during the implementation of I4.0 technologies in existing Industries. The identified ISR and barriers were evaluated and analysed based on the data set collected from a questionnaire-based survey. Fuzzy multi-criteria analysis is conducted to identify the most weighted SR and barriers and ranked them concerning their importance. Furthermore, the inter-item correlation between both of them is analysed. This research work offers the researchers, practitioners, and industrialists an opportunity to formulate MCDM problems through numerous case studies, prioritising the top barriers and system requirements and the inter-relationship shared between them.

TOPSIS vs. VIKOR

2020 ◽  
pp. 225-250
Author(s):  
Semra Erpolat Taşabat ◽  
Tuğba Kıral Özkan
Keyword(s):  
Decision Making ◽  
Human Development ◽  
Human Development Index ◽  
Decision Makers ◽  
Multi Criteria Decision Making ◽  
Vikor Method ◽  
Data Set ◽  
Development Index ◽  
Multidisciplinary Nature ◽  
Development Levels

Evaluating multiple criteria and selecting and/or ranking alternatives is called Multi Criteria Decision Making (MCDM). These methods which are considered important decision-making tools for decision makers due to their multidisciplinary nature have been developed over the years. As a result, there are many MCDM methods in the literature. In this chapter, TOPSIS and VIKOR, widely used in the literature, will be discussed. The major reason for examining these two methods is that the aggregating function used by both methods is similar because VIKOR method uses linear normalization and TOPSIS method uses vector normalization. The process of the methods is shown on a data set that includes the Human Development Index (HDI) indicators, which have been developed to measure the development levels of countries as well as the unemployment indicator. It was observed that the methods yielded similar results.

Hot News Recommendation System across Heterogonous Websites Using Hadoop

2014 ◽  
Vol 989-994 ◽  
pp. 4704-4707
Author(s):  
Sheng Wu Xu ◽  
Zheng You Xia
Keyword(s):  
Recommendation System ◽  
Research Work ◽  
Big Data Analytics ◽  
Data Set ◽  
Set Size ◽  
News Websites ◽  
News Website ◽  
News Recommendation ◽  
News Services ◽  
Excellent Tool

The current most news recommendations are suitable for news which comes from a single news website, not for news from different news websites. Little research work has been reported on utilizing hundreds of news websites to provide top hot news services for group customers (e.g. Government staffs). In this paper, we present hot news recommendation system based on Hadoop, which is from hundreds of different news websites. We discuss our news recommendation system architecture based on Hadoop.We conclude that Hadoop is an excellent tool for web big data analytics and scales well with increasing data set size and the number of nodes in the cluster. Experimental results demonstrate the reliability and effectiveness of our method.

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