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

Development of decision support systems towards supply chain performance appraisement

Purpose: The aim of this research is to develop various Decision Support Systems (DSS) towards supply chain (SC) performance appraisement as well as benchmarking. The purpose of this work is to understand multi-level (measures and metrics) performance appraisement index system to evaluate overall supply chain performance extent, monitor ongoing performance level and to identify ill-performing areas of the supply chain network. Design/methodology/approach: Fuzzy logic as well as grey theory has been explored in developing a variety of SC performance appraisement modules (evaluation index systems). Generalized fuzzy numbers, generalized intervalvalued fuzzy numbers theory have been utilized in order to tackle decision-makers’ linguistic evaluation information towards meaningful and logical interpretation of procedural hierarchy embedded to the said appraisement modules. Fuzzy-grey relation theory, MULTIMOORA method coupled with fuzzy logic as well as grey theory have also been adapted to facilitate overall SC performance assessment, performance benchmarking and related decision making. Findings: Supply chain performance index has been computed in terms of fuzzy as well as grey context, suggesting the present performance status of the said organizational supply chain. Ill-performing areas of the SC have been identified too. Fuzzy as well as grey based MULTIMOORA (MOORA: Multi-Objective Optimization by Ratio Analysis), fuzzy-grey relation analysis, thus adapted, appeared helpful in evaluating performance ranking order (and selecting the best) of various candidate alternatives (industries/enterprises) operating under similar supply chain architecture according to the ongoing SC performance. Empirical illustrations exhibited the fruitful application potential of the developed decision support tools. Practical implications: The decision support tools thus proposed may be proved fruitful for companies that are trying to identify key business performance measures for their supply chains. Ill-performing areas can easily be identified; companies can seek for possible means in order to improve those SC aspects so as to improve/enhance overall SC performance extent. Benchmarking may help in identifying best practices in relation to the SC which is performing as ideal (benchmarked practices). Best practices of the ideal organization need to be transmitted to the others. Companies can follow their peers in order to improve overall performance level of the entire supply chain. In view of this, the work reported in this dissertation may be proved as a good contributor for effective management of organizational SC. Research limitations: The methodology and presentation is conceptual, yet the tool can provide very useful interpretations for both researchers as well as management practitioners. Accessibility and availability of data are the main limitations affecting which model will be applied. Procedural steps towards implementing the said decision support tools have been demonstrated through empirical research. The decision support tools tools have neither been validated by practical case study nor have these been tested for assessing their reliability. Originality/value: This work articulates various approaches for supply chain performance evaluation considering multiple evaluation criteria (subjective evaluation indices), with a flexibility to modify and analyze using the available data sets collected from a group of experts (decision-makers). The approaches of performance evaluation index system are attempted due to structure and fuzzy (as well as grey) sets. The work is aimed at operational researchers, engineers and special managers

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Age composition and survival of public housing stock in Hong Kong

Emerging notably in more developed regions, building stock ageing which is characterised by shrinking new completions and falling “mortality” has been posing challenges to various stakeholders in built environment. To find way out of this transition, we need to know how long buildings will last these days and the factors leading to their “mortality”. By using data from 1950s till to date, a comprehensive investigation is conducted to analyse the age composition and life expectancy of public housing stock in Hong Kong. What comes after are survival analysis and empirical analysis of those demolished to identify the key factors leading to demolition. Presented in this paper are the preliminary findings as well as the research agenda on the theme to model age composition and survival of both private and public building stocks in Hong Kong and other similar cities in Asia Pacific Rim such as Adelaide and Singapore, together with research activities to formulate policies for sustainable urban management

Evaluation of the new Design Summer Year weather data using parametrical buildings

The Charted Institution of Building Services Engineers (CIBSE) updated the near extreme weather (Design Summer Year – DSY) for all 14 locations in the UK in 2016. This new release attempts to address the underlying shortcomings of the previous definition where the averaged dry bulb temperature was the sole metric to choose DSY among source weather years. The aim of this research is to evaluate whether the new definition of the probabilistic DSYs can consistently represent near extreme condition. London historical weather data and their correspondent DSYs were used in this research. Dynamic thermal modelling using EnergyPlus was carried out on large number single zone offices (parametric study) which represent a large portion of cellular offices in the UK. The predicted indoor warmth from the sample building models show that these new definitions are not always able to represent near extreme conditions. Using multiple years as DSY is able to capture different types of summer warmth but how to use one or all of these DSYs to make informed judgement on overheating is rather challenging. The recommended practice from this research is to use more warm years for the evaluation of overheating and choose the near extreme weather from the predicted indoor warmt