Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

A FBWM-PROMETHEE approach for industrial robot selection

Industrial engineering; Multidisciplinary design optimization; Manufacturing engineering; Technology management; Operations management; Industry management; Business management; Industrialization; Industrial robots; Fuzzy best-worst method; PROMETHEE; MCDM; Robot selection; Criteria.publishersversionpublishe

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Selection of industrial robots using the Polygons area method

Selection of robots from the several proposed alternatives is a very important and tedious task. Decision makers are not limited to one method and several methods have been proposed for solving this problem. This study presents Polygons Area Method (PAM) as a multi attribute decision making method for robot selection problem. In this method, the maximum polygons area obtained from the attributes of an alternative robot on the radar chart is introduced as a decision-making criterion. The results of this method are compared with other typical multiple attribute decision-making methods (SAW, WPM, TOPSIS, and VIKOR) by giving two examples. To find similarity in ranking given by different methods, Spearman’s rank correlation coefficients are obtained for different pairs of MADM methods. It was observed that the introduced method is in good agreement with other well-known MADM methods in the robot selection problem

A Multi-Objective Optimization Approach for Multi-Head Beam-Type Placement Machines

This paper addresses a highly challenging scheduling problem in the field of printed circuit board (PCB) assembly systems using Surface Mounting Devices (SMD). After describing some challenging optimization sub-problems relating to the heads of multi-head surface mounting placement machines, we formulate an integrated multi-objective mathematical model considering of two main sub-problems simultaneously. The proposed model is a mixed integer nonlinear programming one which is very complex to be solved optimally. Therefore, it is first converted into a linearized model and then solved using an efficient multi-objective approach, i.e., the augmented epsilon constraint method. An illustrative example is also provided to show the usefulness and applicability of the proposed model and solution method.PCB assembly. Multi-head beam-type placement machine. Multi-objective mathematical programming. Augmented epsilon-constraint method

Identifying and Prioritising Future Robot Control Research with Multi-Criteria Decision-Making

The gap between researchers who carry out scientific exploration and practitioners who can make use of the research results is well known. In addition, while practitioners place a high value on research, they do not read many research papers. This paper attempts to define and prioritise future research in robotics using the analytical hierarchy process (AHP). Fifteen research alternatives and gaps, five performance criteria, eight industry types, and six production processes, investigated by both academics and practitioners, are filtered to six alternatives, four performance criteria, three industry types, and three production processes, respectively, based on the most important factors in decision-making. Subsequently, they are analysed by the Expert Choice software. This research aims at bridging the gap between academics and practitioners in robotics research and at conducting research that is relevant to industry. The results indicate that the research in multi-robot control ranked first with 26.8%, followed by the research in safe control with 23.3% and the research in remote robot supervision with 19.0%. The research in force control ranked fourth with 17.8%, followed by the research in 3D vision and wireless communication with 8.4% and 6.4%, respectively. Based on the results, the academics involved in robotics research should direct their effort to the research activities that received the highest priority in the AHP model

Surveying the sense of urgency of the tactical-level management to adopt industry 4.0 technologies: Ranking of three sister plants based on BWM-CRITIC-TOPSIS

Purpose:Although the decision to adopt Industry 4.0 technologies is commonly strategical, the selection and implementation of technology are the responsibilities of the tactical level management. The tactical level management will also directly experience the impact of adopting the technology towards the organizational performances in their functional areas. The comparative survey study aims to measure the tactical level management’s sense of urgency of the nine pillars in three plants of a single manufacturing organization. Design/methodology/approach: The research methodology starts with a literature review to collect the criteria appertaining to the pillars. Based on the 95 constituting criteria, the second step prepares and conducts a questionnaire survey with 32 participants on three sister plants. Next, rough BWM-CRITIC-TOPSIS ranks these plants at the pillar and criteria levels. The ranking method integrates Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (CRITIC), and technique for order performance by similarity to ideal solution (TOPSIS). The top management discussed and rendered insights into the results. Findings: Results show that the high-mix and labor-intensive plant (Plant 1) has the highest urgency, whereas the largely automated plant (Plant 3) has the lowest urgency to adopt the nine pillars. The findings provide empirical evidence of the effect of the recent Industry 4.0 awareness programs in Plant 1 and advanced infrastructure would lead to organization inertia (Plant 3) to aggressively pursue technological change. The most urgent pillar is cybersecurity, and the least urgent pillar is additive manufacturing (AM), outlining the concern over cyber threats when product information is increasingly integrated into the supply chain and technology immaturity of AM in production. Research limitations/implications: A limitation of this study is that the comparative survey only focused on three plants and the tactical level management of an organization. Originality/value: This study contributes to the knowledge of Industry 4.0 readiness by being the first to show different levels in the sense of urgency of the tactical level managements on the relevant technologies, which potentially affect the direction and the pace of Industry 4.0 adoptionPeer Reviewe

A solution to robot selection problems using data envelopment analysis

Selection of industrial robots for the present day's manufacturing organizations is one of the most difficult assignments due to the presence of a wide range of feasible alternatives. Robot manufacturers are providing advanced features in their products to sustain in the globally competitive environment. For this reason, selection the most suitable robot for a given industrial application now becomes a more complicated task. In this paper, four models of data envelopment analysis (DEA), i.e. Charnes, Cooper and Rhodes (CCR), Banker, Charnes and Cooper (BCC), additive, and cone-ratio models are applied to identify the feasible robots having the optimal performance measures, simultaneously satisfying the organizational objectives with respect to cost and process optimization. Furthermore, the weighted overall efficiency ranking method of multi-attribute decision-making theory is also employed for arriving at the best robot selection decision from the short-listed competent alternatives. In order to demonstrate the relevancy and distinctiveness of the adopted DEA-based approach, two real time industrial robot selection problems are solved

VIKOR Technique:A Systematic Review of the State of the Art Literature on Methodologies and Applications

The main objective of this paper is to present a systematic review of the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method in several application areas such as sustainability and renewable energy. This study reviewed a total of 176 papers, published in 2004 to 2015, from 83 high-ranking journals; most of which were related to Operational Research, Management Sciences, decision making, sustainability and renewable energy and were extracted from the “Web of Science and Scopus” databases. Papers were classified into 15 main application areas. Furthermore, papers were categorized based on the nationalities of authors, dates of publications, techniques and methods, type of studies, the names of the journals and studies purposes. The results of this study indicated that more papers on VIKOR technique were published in 2013 than in any other year. In addition, 13 papers were published about sustainability and renewable energy fields. Furthermore, VIKOR and fuzzy VIKOR methods, had the first rank in use. Additionally, the Journal of Expert Systems with Applications was the most significant journal in this study, with 27 publications on the topic. Finally, Taiwan had the first rank from 22 nationalities which used VIKOR technique

Opportunities for using eye tracking technology in manufacturing and logistics: Systematic literature review and research agenda

Workers play essential roles in manufacturing and logistics. Releasing workers from routine tasks and enabling them to focus on creative, value-adding activities can enhance their performance and wellbeing, and it is also key to the successful implementation of Industry 4.0. One technology that can help identify patterns of worker-system interaction is Eye Tracking (ET), which is a non-intrusive technology for measuring human eye movements. ET can provide moment-by-moment insights into the cognitive state of the subject during task execution, which can improve our understanding of how humans behave and make decisions within complex systems. It also enables explorations of the subject’s interaction mode with the working environment. Earlier research has investigated the use of ET in manufacturing and logistics, but the literature is fragmented and has not yet been discussed in a literature review yet. This article therefore conducts a systematic literature review to explore the applications of ET, summarise its benefits, and outline future research opportunities of using ET in manufacturing and logistics. We first propose a conceptual framework to guide our study and then conduct a systematic literature search in scholarly databases, obtaining 71 relevant papers. Building on the proposed framework, we systematically review the use of ET and categorize the identified papers according to their application in manufacturing (product development, production, quality inspection) and logistics. Our results reveal that ET has several use cases in the manufacturing sector, but that its application in logistics has not been studied extensively so far. We summarize the benefits of using ET in terms of process performance, human performance, and work environment and safety, and also discuss the methodological characteristics of the ET literature as well as typical ET measures used. We conclude by illustrating future avenues for ET research in manufacturing and logistics