Social sustainable supplier evaluation and selection: a group decision-support approach

Organisational and managerial decisions are influenced by corporate sustainability pressures. Organisations need to consider economic, environmental and social sustainability dimensions in their decisions to become sustainable. Supply chain decisions play a distinct and critical role in organisational good and service outputs sustainability. Sustainable supplier selection influences the supply chain sustainability allowing many organisations to build competitive advantage. Within this context, the social sustainability dimension has received relatively minor investigation; with emphasis typically on economic and environmental sustainability. Neglecting social sustainability can have serious repercussions for organisational supply chains. This study proposes a social sustainability attribute decision framework to evaluate and select socially sustainable suppliers. A grey-based multi-criteria decision-support tool composed of the ‘best-worst method’ (BWM) and TODIM (TOmada de Decisão Interativa e Multicritério – in Portuguese ‘Interactive and Multicriteria Decision Making’) is introduced. A grey-BWM approach is used to determine social sustainability attribute weights, and a grey-TODIM method is utilised to rank suppliers. This process is completed in a group decision setting. A case study of an Iranian manufacturing company is used to exemplify the applicability and suitability of the proposed social sustainability decision framework. Managerial implications, limitations, and future research directions are introduced after the application of the model

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

Applying Cognitive Measures In Counterfactual Prediction

Counterfactual reasoning can be used in task-switching scenarios, such as design and planning tasks, to learn from past behavior, predict future performance, and customize interventions leading to enhanced performance. Previous research has focused on external factors and personality traits; there is a lack of research exploring how the decision-making process relates to both task-switching and counterfactual predictions. The purpose of this dissertation is to describe and explain individual differences in task-switching strategy and cognitive processes using machine learning techniques and linear ballistic accumulator (LBA) models, respectively, and apply those results in counterfactual models to predict behavior. Applying machine learning techniques to real-world task-switching data identifies a pattern of individual strategies that predicts out-of-sample clustering better than random assignment and identifies the most important factors contributing to the strategies. Comparing parameter estimates from several different LBA models, on both simulated and real data, indicates that a model based on information foraging theory that assumes all tasks are evaluated simultaneously and holistically best explains task-switching behavior. The resulting parameter values provide evidence that people have a switch-avoidance tendency, as reported in previous research, but also show how this tendency varies by participant. Including parameters that describe individual strategies and cognitive mechanisms in counterfactual prediction models provides little benefit over a baseline intercept-only model to predict a holdout dataset about real-world task switching behavior and performance, which may be due to the complexity and noise in the data. The methods developed in this research provide new opportunities to model and understand cognitive processes for decision-making strategies based on information foraging theory, which has not been considered previously. The results from this research can be applied to future task-switching scenarios as well as other decision-making tasks, both in a laboratory setting as well as the real-world, and have implications for understanding how these decisions are made

Systematic Literature Review of the Evidence for Effective National Immunisation Schedule Promotional Communications

Introduction: A systematic literature review examined the published evidence on the effectiveness of European promotional communications for national immunisation schedule (NIS) vaccinations. The review was commissioned by the European Centre for Disease Prevention and Control (ECDC) and conducted by the Institute for Social Marketing at the University of Stirling.The purpose of the review: ‘Immunisation hesitancy' has negatively impacted population uptake of routine immunisation. A substantial body of evaluated communication activity promoting nationally indicated routine immunisation has been published. This systematic review of the evidence aims to: collate and map the types of promotional communication that have been used; assess the quality of the evaluative research reporting on these promotional communications; and assess the applicability of this evidence to immunisation policy, strategy and practice priorities. The analysis and findings are intended to provide a current status report on the evidence, and evidence gaps for good practice in national immunisation promotional communications, thus supporting countries in their communication activities for the prevention and control of communicable diseases.Objectives of the review: The review aimed to answer the following research questions: Which audiences have been targeted by NIS promotional communications? Which communication methods and approaches have been used to promote or reinforce NIS vaccination uptake? What theoretical underpinnings are used to inform communication methods and approaches? Which settings and communication channels have been used to promote or reinforce NIS vaccination uptake? What is the evidence for effectiveness of communication initiatives in changing or reinforcing knowledge, attitudes or behaviour towards NIS? What is the evidence for impact of NIS communication initiatives to control communicable disease? What impact have campaign communications promoting NIS had on public acceptance and vaccine uptake rates

New Progress of Grey System Theory in The New Millennium

Purpose – The purpose of this paper is to summarize the progress in grey system research during 2000- 2015, so as to present some important new concepts, models, methods and a new framework of grey system theory. Design/methodology/approach –The new thinking, new models and new methods of grey system theory and their applications are presented in this paper. It includes algorithm rules of grey numbers based on the “Kernel” and the degree of greyness of grey numbers, the concept of general grey numbers, the synthesis axiom of degree of greyness of grey numbers and their operations; the general form of buffer operators of grey sequence operators; the four basic models of GM(1,1), such as Even Grey Model(EGM), Original Difference Grey Model(ODGM), Even Difference Grey Model(EDGM), Discrete Grey Model(DGM) and the suitable sequence type of each basic model, and suitable range of most used grey forecasting models; the similarity degree of grey incidences, the closeness degree of grey incidences and the three dimensional absolute degree of grey incidence of grey incidence analysis models; the grey cluster model based on center-point and end-point mixed triangular whitenization functions; the multi-attribute intelligent grey target decision model, the two stages decision model with grey synthetic measure of grey decision models; grey game models, grey input-output models of grey combined models; and the problems of robust stability for grey stochastic time-delay systems of neutral type, distributed-delay type and neutral distributed-delay type of grey control, etc. And the new framework of grey system theory is given as well. Findings –The problems which remain for further studying are discussed at the end of each section. The reader could know the general picture of research and developing trend of grey system theory from this paper. Practical implications – A lot of successful practical applications of the new models to solve various problems have been found in many different areas of natural science, social science, and engineering, including spaceflight, civil aviation, information, metallurgy, machinery, petroleum, chemical industry, electrical power, electronics, light industries, energy resources, transportation, medicine, health, agriculture, forestry, geography, hydrology, seismology, meteorology, environment protection, architecture, behavioral science, management science, law, education, military science, etc. These practical applications have brought forward definite and noticeable social and economic benefits. It demonstrates a wide range of applicability of grey system theory, especially in the situation where the available information is incomplete and the collected data are inaccurate. Originality/value –The reader is given a general picture of grey systems theory as a new model system and a new framework for studying problems where partial information is known; especially for uncertain systems with few data points and poor information. The problems remaining for further studying are identified at the end of each section. Keywords Grey systems theory, Operations of grey numbers, Buffer operators, Grey forecasting models, Grey incidence analysis models, Grey cluster evaluation models, Grey decision models, Combined grey models, Grey contro

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

Neurocomputational Accounts of Choice Variability and Affect during Decision-making

Humans exhibit surprising variability in behaviour, often making different choices under identical conditions. While the outcomes of these choices typically lead to explicit rewards that have been shown to influence subsequent affective states, less well understood is how the brain represents rewards that are intrinsically meaningful to an individual. The first part of this thesis examines the contributions of endogenous fluctuations in brain activity to behaviour. Resting-state studies suggest that ongoing endogenous fluctuations in brain activity can influence low-level perceptual and motor processes but it remains unknown whether such fluctuations also influence high-level cognitive processes including decision making. Using a novel application of real-time functional magnetic resonance imaging, I find that low pre-stimulus brain activity lead to increased occurrences of risky choice. Using computational modeling, I show that greater risk taking is explained by enhanced phasic responses to offers in a decision network. These findings demonstrate that endogenous brain activity provides a physiological basis for variability in complex behaviour. I then examine how the neuroanatomy of the brain in the form of tissue microstructure relates to risk preferences by leveraging on in vivo histology using magnetic resonance imaging. The second part of this thesis investigates how experienced events, such as rewards received following choice, are aggregated into affective states. Despite their relevance to ideas like goal-setting and well-being, little is known about the impact of intrinsic rewards on affective states and their representation in the brain. A reinforcement learning task incorporating a skilled performance component that did not influence payment was developed to examine this. Computational modeling revealed that momentary happiness depended on past extrinsic rewards and also intrinsic rewards related to the experience of successful skilled performance. Individuals for whom intrinsic rewards more strongly influence momentary happiness exhibit stronger ventromedial prefrontal cortex responses for successful skilled performance. These findings show that the ventromedial prefrontal cortex represents the subjective value of intrinsic rewards, and that computational models of mood dynamics provide a tool that can be used to measure implicit values of abstract goods and experiences