Decision-supporting models for human-reliability based safety promotion in offshore Liquid Natural Gas terminal

We would like to give great thanks to the experienced engineers in the Beihai Offshore LNG Terminal for their helpful support during the preparation of this paper. We would also like to express thanks to the editor and the anonymous reviewer for the valuable comments.Peer reviewedPostprin

A review on fuzzy multi-criteria decision making land clearing for oil palm plantation

Our review paper research categorize the methods in the method of Fuzzy Multi-Criteria Decision Making (FMCDM) to find the method is widely used in the case of land clearing for plantation. Model FMCDM is used to assess the parameter in multi-criteria-based decision making. The dominant percentage of the result was obtained using Fuzzy Analytic Hierarchy Process (FAHP) method. While the application of other methods for the same problem are Fuzzy Ordered Weighted Averaging (FOWA), Fuzzy Elimination Et Choix Traduisant la Realite or Elimination and Choice Translating Reality (FELECTRE), Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), Fuzzy, Artificial Neural Networks (FANNs) has less. Some the research result also implemented hybrid in FMCDM Method to give some weight in the assessment of decision making. There was also a paper which integrates FMCDM to the GIS method on the land clearing. Therefore, it is concluded that the issue on the land clearing can be done through collaboration of several models of FMCDM, so that it can be developed by involving the decision model using multi-stakeholder mode

An Integrated Human Reliability Based Decision Pool Generating and Decision Making Method for Power Supply System in LNG Terminal

Acknowledgement We would like to give sincerely thank to Zhonghe Zhang, the principle expert in Sinopec and other relevant staff in Beihai LNG terminal for their valuable and constructive support during the development of this work. We would also like to express our very great appreciation to the respected reviewers. Their valuable suggestions and comments have enhanced the strength of this paper.Peer reviewedPostprin

Neuro-fuzzy inference systems approach to decision support system for economic order quantity

Supply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems - ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neuro-fuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts’ opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision sup- port system gives valid results. The proposed system is flexible and can be applied to various types of goods in SC

Intelligent machining methods for Ti6Al4V: a review

Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision- making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence- based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.publishe

Intelligent Multi-Attribute Decision Making Applications: Decision Support Systems for Performance Measurement, Evaluation and Benchmarking

Efficiency has been and continues to be an important attribute of competitive business environments where limited resources exist. Owing to growing complexity of organizations and more broadly, to global economic growth, efficiency considerations are expected to remain a top priority for organizations. Continuous performance evaluations play a significant role in sustaining efficient and effective business processes. Consequently, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency of various industries. Majority of these models focus solely on quantitative criteria omitting qualitative data. However, a thorough performance measurement and benchmarking require consideration of all available information since accurately describing and defining complex systems require utilization of both data types. Most evaluation models also function under the unrealistic assumption of evaluation criteria being dependent on one another. Furthermore, majority of these methodologies tend to utilize discrete and contemporary information eliminating historical performance data from the model environment. These shortcomings hinder the reliability of evaluation outcomes leading to inadequate performance evaluations for many businesses. This problem gains more significance for business where performance evaluations are tied in to important decisions relating to business expansion, investment, promotion and compensation. The primary purpose of this research is to present a thorough, equitable and accurate evaluation framework for operations management while filling the existing gaps in the literature. Service industry offers a more suitable platform for this study since the industry tend to accommodate both qualitative and quantitative performance evaluation factors relatively with more ease compared to manufacturing due to the intensity of customer (consumer) interaction. Accordingly, a U.S. based food franchise company is utilized for data acquisition and as a case study to demonstrate the applications of the proposed models. Compatible with their multiple criteria nature, performance measurement, evaluation and benchmarking systems require heavy utilization of Multi-Attribute Decision Making (MADM) approaches which constitute the core of this research. In order to be able to accommodate the vagueness in decision making, fuzzy values are also utilized in all proposed models. In the first phase of the study, the main and sub-criteria in the evaluation are considered independently in a hierarchical order and contemporary data is utilized in a holistic approach combining three different multi-criteria decision making methods. The cross-efficiency approach is also introduced in this phase. Building on this approach, the second phase considered the influence of the main and sub-criteria over one another. That is, in the proposed models, the main and sub-criteria form a network with dependencies rather than having a hierarchical relationship. The decision making model is built to extract the influential weights for the evaluation criteria. Furthermore, Group Decision Making (GDM) is introduced to integrate different perspectives and preferences of multiple decision makers who are responsible for different functions in the organization with varying levels of impact on decisions. Finally, an artificial intelligence method is applied to utilize the historical data and to obtain the final performance ranking. Owing to large volumes of data emanating from digital sources, current literature offers a variety of artificial intelligence and machine learning methods for big data analytics applications. Comparing the results generated by the ANNs, three additional well-established methods, viz., Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), are also employed for the same problem. In order to test the prediction capability of these methods, the most influencing criteria are obtained from the data set via Pearson Correlation Analysis and grey relational analysis. Subsequently, the corresponding parameters in each method are optimized via Particle Swarm Optimization to improve the prediction accuracy. The accuracy of artificial intelligence and machine learning methods are heavily reliant on large volumes of data. Despite the fact that several businesses, especially business that utilize social media data or on-line real-time operational data, there are organizations which lack adequate amount of data required for their performance evaluations simply due to the nature of their business. Grey Modeling (GM) technique addresses this issue and provides higher forecasting accuracy in presence of uncertain and limited data. With this motivation, a traditional multi-variate grey model is applied to predict the performance scores. Improved grey models are also applied to compare the results. Finally, the integration of the fractional order accumulation along with the background value coefficient optimization are proposed to improve accuracy

Examining supply chain agility using social network analysis : a thesis presented in partial fulfilment of the requirement for the degree of Master of Supply Chain Management at Massey University, Auckland, New Zealand

In the current literature of supply chain agility (SCA), the extant agility models are not only rare but are also usually developed from the viewpoint of a firm rather than from a network perspective. While social network analysis (SNA) has proven its power and capacity in the social sciences, it has been rarely applied to supply chain management (SCM) phenomena. As such, this is a primary motivation for this study to take shape. The main focus of the research is refined to build on the Scion project on Rural Value Chains. It seeks to explore the appropriateness of SNA to assess SCA and to simultaneously make a relative agility comparison between supply chains by SNA. The empirical data are collected by structured interviews in a rural area of New Zealand and then analysed as a network case by varying SNA metrics, tools, and techniques. This thesis sheds light on how SNA is appropriate to tap into the areas that are barely recognised by the extant approaches. The findings show that SNA is well able to consider interactions and linkages in complex networks, and it also enables the integrated lens of network and complex adaptive system (CAS) to examine network agility in a comprehensive and systematic manner. SNA lends itself well to phenomena that directly relates to, or results from, network topology, connectivity, and interconnectedness, such as network visibility, speed of responses, and the ability to have multiple connection options. However, if used exclusively, SNA is less appropriate to examine attributes that either have qualitative elements or which are associated with firm operations. This thesis has added to the literature the applicability of SNA to evaluate SCA and to model SCs. For policy makers, it offers a clearer understanding of the local network for regional development plans. For business owners, it proposes an alternative approach of evaluating SCA, SC relationships, and SC members, so as to build up effective SCM strategies

An empirical investigation of the demographics of Top Management Team (TMT) and its influence in forecasting organizational outcome in international architecture, engineering and construction (AEC) Firms : a fuzzy set approach

Whereas Top Management Teams (TMTs) are selected to fit a firm’s strategy, prior studies have evidenced that TMTs have significant impact on firm performance. The challenge of the two-way causality has been reflected in previous findings being ambiguous, inconsistent and sometimes conflicted. Pursing the same line of research may lead to incomplete and even error-prone conclusion. In contrast, this research suggests that inconsistency of findings among TMT demographics shown in prior work may point the possibility of studying the black-box nature of such relationships, and provide a tool to future forecast the organization outcome. More specifically, a multi-input (TMT demographics) multi-output (organization outcome) structure was used in this research to explore the future predictability power of TMT demographics for international Architects, Engineers and Construction firms (AEC firms). In order to build a reliable forecasting model, those contradictions were avoided by the utilization of artificial intelligence methods by training, testing and producing results without any prior assumptions or known structures. In particular, the Adaptive Neural Fuzzy Inference System (ANFIS) have been employed as a basis for constructing a set of fuzzy “if– then” rules with pre-tested input–output pairs. Three different forecasting strategies were constructed, the findings have demonstrated the learning and potential of the ANFIS model (time series based) in forecasting organization outcome, but at the same time, suggest that distinction should be established among different constructs of TMT demographics and outcome constructs. The results demonstrated that job-related demographics (i.e., TMT Educational Diversity, TMT Functional Diversity and TMT Tenure) could provide a satisfactory forecasting accuracy for the short-span (Liquidity) and medium-span (Cash Flow Stability and Capital Structure) outcome constructs. The future predictability power of other non-job demographics could not be evidenced in this research. Additionally, outcome constructs with dynamic nature could not be forecasted. Lastly, future research opportunities have been suggested for researchers. Most importantly, it includes the need to re-define diversity in the context of TMT composition (having different meaning as in: Variety, Separation and Disparity). Other methodological future opportunities are also suggested at the end of this study