CONTAINER SHIPPING RISK MANAGEMENT: A CASE STUDY OF TAIWAN CONTAINER SHIPPING INDUSTRY

Whilst container shipping has become increasingly important over the past few decades due to its obvious advantages, container shipping companies have faced various risks from different sources in their operations. Systematic academic studies on this topic are few; and in light of this, this study aims to systematically explore and analyse the risks in container shipping operations and to examine the applicable risk mitigation strategies in a logistics perspective, including information flow, physical flow, and payment flow. This thesis uses Taiwan container shipping industry as a case study, and borrows four steps of risk management as the main method, which includes risk identification, risk analysis, risk mitigation strategies identification, and strategies evaluation. In order to ensure the analysis is inclusive and systematic, risk factors and risk mitigation strategies are identified through a related literature review and are validated through a set of interviews. Risk analysis is conducted through using questionnaires, and then through risk ranking, risk matrix, risk mapping, and P-I graph. Risk mitigation strategies are evaluated through classic AHP and fuzzy AHP analysis. A number of significant findings have been obtained. Firstly, 35 risk factors are identified and classified into three categories: risks associated with information flow, risks associated with physical flow, and risks associated with payment flow. After collecting and analysing the risk-factor survey, the results indicate that the risk associated with physical flow has the more significant impact on shipping companies’ operation. However, one risk factor associated with information flow, “shippers hiding cargo information”, has the most significant impact among the 35 risk factors. Secondly, 20 risk mitigation strategies are identified and classified into three categories: intra-organisational strategies, intra-channel strategies, and inter-channel strategies. After collecting the AHP survey and analysing through classic AHP and fuzzy AHP, the result indicates that “slot exchange, slot charter, joint fleet, ship charter with other container shipping companies” is the most important strategy. The main contributions of this thesis include: (1) based on the literature review, there have been no research on risk management in the context of container shipping operation from a broad logistics perspective, and this thesis is the first attempt to fill this research gap; (2) this thesis uses Taiwan shipping industry as a case study to apply the framework, which generates useful managerial insights; (3) the conceptual model of risk management developed in this thesis can be applied to container shipping operations in other countries and regions; (4) compared with several studies using secondary data, this thesis uses empirical data to conduct the risk analysis, and make the results more close to the reality situation in container shipping; (5) in terms of risk analysis, this thesis ranks the total 35 risk factors rather than only identify the most important one, this can be used to be generalised to the whole container shipping companies in Taiwan, or even to the whole world; (6) in terms of risk management, the previous studies usually analyse only the importance of strategies. However, this thesis analyses the results of AHP from three different angles: reducing financial loss, reducing reputation loss, and reducing safety and security incident related loss. This can provide different angles for the managers who are considering different aspects

Qualitative and Quantitative Approaches for Evaluation of Safety Risks in Coal Mines

The safety in underground coal mines continues to be a major problem in the Indian mining industry. Despite significant measures taken by the Directorate General of Mines Safety (DGMS) to reduce the number of mining accidents in underground coal mines, the number remains high. To improve the safety conditions, it has become a prerequisite to performing risk assessment for various operations in Indian mines. It is noted that many research studies conducted in the past are limited to either statistical analysis of accidents or study of single equipment or operation using qualitative and quantitative techniques. Limited work has been done to identify, analyse, and evaluate the safety risks of a complete underground coal mine in India. The present study attempts to determine the appropriate qualitative and quantitative risk assessment approaches for the evaluation of safety risks in Indian underground coal mines. This thesis addresses several important objectives as (i) to identify the type of safety risk analysis techniques suitable for evaluating various mining scenarios (ii) to identify and analyse the hazard factors and hazardous events that affects the safety in underground coal using the qualitative and quantitative approaches (iii) to evaluate the risk level (RL) of the hazardous factors/groups, hazardous events, and the overall mine using the proposed methodology. In this research work, the qualitative techniques, i.e. Failure Mode and Effects Analysis (FMEA), Workplace Risk Assessment and Control (WRAC), and the quantitative techniques, i.e. Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) were applied in an underground coal mine to identify and analyse the hazard factors and hazard events. The analysis of FMEA and WRAC results concluded that the qualitative risk assessment is easy to execute and practical as they are not dependent on the historical data; rather they need experience and close examination. On the other hand, they may yield subjective results due to instinctive human assessment. The analysis of the FTA and ETA results concluded that the quantitative risk assessment could not be performed in Indian underground coal mines due to lack of probability, exposure, and consequence data. To overcome the mentioned problems in qualitative and quantitative techniques, a methodology was proposed for evaluation of the safety risks of hazard events, hazard groups, and overall mine. The proposed methodology is the unification of fuzzy logic, VIKOR (In Serbia: VIseKriterijumska Optimizacija I Kompromisno Resenje, that means: Multi-criteria Optimization and Compromise Solution), and Analytic Hierarchy Process (AHP) techniques. Because of the imprecise nature of the information available in the mining industry, fuzzy logic was employed to evaluate the risk of each hazardous event in terms of consequence, exposure, and probability. VIKOR as was used to rank the evaluated risk of hazardous events. AHP technique helps to determine the relative importance of the risk factors. Therefore, AHP technique was integrated into the risk model so that the risk evaluation can progress from hazardous event level to hazard factor level and finally to overall mine level. To reduce the calculation time significantly and to increase the speed of the proposed risk assessment process, a user-friendly Graphical User Interface (TRAM) was developed using the C# language through Microsoft Visual Studio 2015 and .Net libraries. The proposed methodology developed in this thesis was applied to six underground coal mines. The results presented the risk level of hazard events, hazards groups and overall mine of six mines. The mine-5 has the highest risk level among the evaluated mines. The ranking order of the mines observed based on the overall risk level is mine-5> mine-1 > mine-2 > mine-3 > mine-6 > mine-4. The results of the proposed methodology were compared with DGMS proposed rapid ranking method. This is observed that the proposed methodology presents better evaluation than other approaches. This study could help the mine management to prepare safety measures based on the risk rankings obtained. It may also aid to evaluate accurate risk levels with identified hazards while preparing risk management plans

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 a decision support system for decision-based part/fixture assignment and fixture flow control = Ukusungulwa kohlelo lokuxhaswa kwezinqumo mayelana nokwabiwa kwezingxenye ezakhiwayo kanye nokuhanjiswa kwazo.

Doctoral Degree. University of KwaZulu-Natal, Durban.ABSTRACT: An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach. Keywords: Decision support systems, case-based reasoning, analytic hierarchy process, fuzzy set theory, object-oriented methods, discrete-event simulation, fixtures. IQOQA LOCWANINGO : Ukuncintisana okunezinhlelo eziguquguqukayo kulesi sikhathi samanje sekwenze ukuthi kube nezidingo ezintsha ezinhlelweni zokukhiqiza. Phakathi kwakho konke lokhu izingxenye (fixtures) zingezinye zezinto ezidala izinkinga. Intengo yokwakha uhlaka lwengxenye kanye nokuyikhiqiza kubiza amaphesenti ayi-10 kuya kwangama-20 entengo yokukhiqiza. Amafemu akhiqizayo avamise ukusebenzisa izindlela ezindala zomsebenzi wokwaba izingxenye. Lezi zindlela zidla kakhulu izinsizangqangi futhi kuthatha isikhathi eside ukubala izingxenye ezikhona nokuqinisekisa ukuthi kunesibalo esanele kulokho okumele kube yikho ohlelweni lokusebenza. Inhloso yalolu cwaningo bekungukucwaninga nokusungula i-Decision Support System (DSS) ebe lusizo ekwenzeni umsebenzi wokuthatha izinqumo ngokwabiwa kwezingxenye kanye nokuhanjiswa kwazo ngezikhathi ezimiselwe ukukhiqiza. I-DSS yakhelwa ukusiza labo abayisebenzisayo ukuze basebenzise noma bazisebenzise lapho zingakaze zisetshenziswe khona lezo zingxenye ezibuyisiwe, noma kwakhiwe ezintsha kuya ngokuthi zibuyiswe zinjani lezi ezibuyisiwe nokuthi ziyafana yini nalezo ezintsha. I-DSS isebenzise amasu ahlanganise i-Case-Based Reasoning (CBR), injulalwazi echazwa ngokuthi i-fuzzy, ne-Analytic Hierarchy Process (AHP) ne-Discrete-Event Simulation (DES). I-Artificial Intelligence (AI) eyingxenye ye-DSS isebenzise kakhulu uhlelo lwe-fuzzy CBR luhlangene ne-fuzzy AHP kulandelwa imithetho yolwazi olumayelana nohlobo lomsebenzi. I-CBR isetshenziswe ukumelela lezo zimo zamanani ezingaqondakali nezingaphelele kulezo zingxenye. I-AHP e-fuzzy yasetshenziswa ukuze kutholakale ulwazi kochwepheshe olubeka phambili lezo zingxenye. Ama-oda ezingxenye ezintsha kanye namasampuli asetshenziselwa ukuqeqesha avezwe njengamasha kanye nabekade evele ekhona ngokulandelana kusetshenziswa indlela eyaziwa ngokuthi yi-Object-Oriented (OO) method lapho kubuyiswa izinto noma kunezinqumo eziphakanyiswayo. Izindlela ezijwayelekile zokulandelanisa nokufanisa zisetshenziswe ohlelweni lokubuyisa izinto. Kusetshenziswe isu eliyi-DES ukuhlaziya ukusebenza kwezisombululo eziphakanyiswe yindlela ye-CBR e-fuzzy. Le ndlela iphinde yaveza izimo ezintathu eziphakanyiswe ukuba zibe yisisombululo esibalweni sezingxenye ezihlongozwayo. Ukusebenza kwalezi zimo kuhlungwe ngokusebenzisa indlela ye-DES kwase kuvela inqubo engcono. Ukungajwayeleki kwalolu cwaningo kusebenzise ingxube yezindlela ze-fuzzy CBR ne-DES ngoba lolu hlobo lwengxube belungakaze lusetshenziswe. Kusetshenziswe isibonelo sezibalo ekwethuleni ukusebenza kwale nqubo yokusebenza ehlongozwayo

Fuzzy Set-based Risk Management for Construction Projects

Efficient and comprehensive risk management is critical for successful delivery of engineering, procurement, and construction management (EPCM) projects. Complexity of construction projects is on the rise, which makes it necessary to model uncertainties and to manage risk items related to this class of projects. For decades, researchers and construction practitioners worked together to introduce methods for risk identification and assessment. Considerably less effort was directed towards the development of methods for mitigation, monitoring, and control. The respective individual limitations of these methods prevent the development of comprehensive model which satisfies the needs of practitioners. In this research a comprehensive risk management model “CRMM” is developed to address the limitations of existing methods and to fill the gap between research and practice. The developed model implements a micro system approach to introduce a novel risk identification methodology that provides a systematic procedure to identify risk associated with construction projects. The identification procedure implements root cause analysis and brainstorming technique to identify risk items, consequences, and root causes. The developed CRMM also introduces new method for determination of risk ownership utilizing fuzzy set theory and “One Risk – One Owner” concept. The ownership determination method allocates risk to the owner with highest ability, effectiveness, and capacity to deal with that risk. It also introduces a new qualitative and quantitative evaluation process that utilizes fuzzy set theory and fuzzy probability theory, as well as a new risk mapping procedure which allows for the determination of risk level associated with any project component (e.g., category). The quantitative assessment methodology allows for the use of linguistic and numeric fuzzy evaluations. Fuzzy Linguistic-Numeric Conversion Scheme (FLNCS) is introduced to convert the linguistic evaluations into numeric. The quantitative assessment methodology also introduces the pre-mitigation contingency that represents the contingency fund required for a risk in case no mitigation strategy is implemented. In this respect a novel risk mitigation framework is developed to generate and evaluate possible mitigation strategies for each risk being considered. It also provides a selection procedure which allows users to select the most effective mitigation strategy; making use of fuzzy set theory. The mitigation methodology introduces the post-mitigation contingency that quantifies the contingency required for the selected mitigation strategy. Performance of selected mitigation strategy is monitored using a newly developed risk monitoring method that compares the actually depleted contingency to the post mitigation contingency. The developed monitoring method provides an early warning that alerts users of detected possible failure of selected mitigation strategy. It also determines the correct time for initiation of control process based on a set of qualitative factors. Once risk control process is initiated, the developed control method identifies, evaluates, and selects the most effective control action(s) to support the selected mitigation strategy. In cases where the selected control action fails, the developed control method notifies the user to revise the risk management plan. These notifications allows user to avoid potential failures of similar risk items which are expected to occur in the future. The developed CRMM was coded using VB.Net under Microsoft® windows and .NET framework environment to facilitate its application. A set of case studies are collected from literature and analysed to validate the developed methods within CRMM and to illustrate their essential features. Also, a numerical example elucidates the complete computational processes of the developed comprehensive model

Strategic Logistics Outsourcing:Integrated Models for Evaluating and Selecting Logistics Service Providers (LSPs) Upstream/Downstream Supply Chain Comparison

This research aims to maximize the logistics outsourcing benefits through developing new hybrid models for evaluating and selecting Logistics Service Providers (LSPs). The growing demand for logistics outsourcing and the increase in the number and type of LSPs highlight the increasing importance of the LSP evaluation and selection process. Firms use various approaches to evaluate and select their LSP partners. Most of these approaches seem to have overlooked the strategic side of the logistics outsourcing process. Additionally, the uncertainty issue of data, the complexity of the decision and the large number of criteria involved increase the attractiveness of the Multi-Criteria Decision-Making (MCDM) approaches. A comparative literature review was used in order to identify crucial factors and methods that are used in logistics literature in fragmented ways and therefore, to establish and design a conceptual framework and models for logistics outsourcing. First, a long list of evaluation criteria was developed. Three main dimensions were identified: logistics performance, logistics resources and logistics services. Then a conceptual framework was developed using the three main dimensions with their related factors. Based on the comparative literature review outcomes, a number of integrated models have been developed and used to achieve this aim with emphasis given to FDEMATEL, FTOPSIS and FQFD techniques. Whereas the FDEMATEL technique contributed to construct influence relationships between factors under each dimension, develop impact-relationship maps and identify dependent and independent success factors (ISFs), the FTOPSIS technique used the weighted success factors to evaluate, rank and select the best LSP in three case studies. Twenty-one ISFs have been identified to be used in the final approach. These ISFs consist of eight LKPIs, seven logistics services and six logistics resources and capabilities. All of the factors were used to evaluate and select the best LSP alternative and ISFs were used to conduct the evaluation process. Different sensitivity analysis tests are used to confirm models’ robustness. Based on the outcomes of both cases, decision makers can use independent factors alone to evaluate and select the best LSP, which simplified the logistics outsourcing process in our study. The FQFD technique was used to link the LSUs strategic objectives with logistics requirements and the ISFs to develop a new strategic logistics outsourcing approach. Finally, two case studies representing the supply chain upstream and downstream are used to demonstrate the new hybrid approach effectiveness. The comparison of both cases’ findings highlighted their differences in terms of strategic objectives, logistics requirements and ISFs

Development of a decision support system for decision-based part/fixture assignment and fixture flow control.

Doctoral Degree. University of KwaZulu-Natal, Durban.An intense competition in a dynamic situation has increased the requirements that must be considered in the current manufacturing systems. Among those factors, fixtures are one of the major problematic components. The cost of fixture design and manufacture contributes to 10-20% of production costs. Manufacturing firms usually use traditional methods for part/fixture assignment works. These methods are highly resource consuming and cumbersome to enumerate the available fixtures and stabilise the number of fixtures required in a system. The aim of this study was to research and develop a Decision Support System (DSS), which was useful to perform a decision-based part/fixture assignment and fixture flow control during planned production periods. The DSS was designed to assist its users to reuse/adapt the retrieved fixtures or manufacture new fixtures depending upon the state of the retrieved fixtures and the similarities between the current and retrieved cases. This DSS combined Case-Based Reasoning (CBR), fuzzy set theory, the Analytic Hierarchy Process (AHP) and Discrete-Event Simulation (DES) techniques. The Artificial Intelligence (AI) component of the DSS immensely used a fuzzy CBR system combined with the fuzzy AHP and guiding rules from general domain knowledge. The fuzzy CBR was used to represent the uncertain and imprecise values of case attributes. The fuzzy AHP was applied to elicit domain knowledge from experts to prioritise case attributes. New part orders and training samples were represented as new and prior cases respectively using an Object-Oriented (OO) method for case retrieval and decision proposal. Popular fuzzy ranking and similarity measuring approaches were utilised in the case retrieval process. A DES model was implemented to analyse the performances of the proposed solutions by the fuzzy CBR subsystem. Three scenarios were generated by this subsystem as solution alternatives that were the proposed numbers of fixtures. The performances of these scenarios were evaluated using the DES model and the best alternative was identified. The novelty of this study employed the combination of fuzzy CBR and DES methods since such kinds of combinations have not been addressed yet. A numerical example was illustrated to present the soundness of the proposed methodological approach.Please refer to the PDF for author's keywords

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

Decision making trial and evaluation laboratory (DEMATEL) is considered as an effective method for the identification of cause-effect chain components of a complex system. It deals with evaluating interdependent relationships among factors and finding the critical ones through a visual structural model. Over the recent decade, a large number of studies have been done on the application of DEMATEL and many different variants have been put forward in the literature. The objective of this study is to review systematically the methodologies and applications of the DEMATEL technique. We reviewed a total of 346 papers published from 2006 to 2016 in the international journals. According to the approaches used, these publications are grouped into five categories: classical DEMATEL, fuzzy DEMATEL, grey DEMATEL, analytical network process- (ANP-) DEMATEL, and other DEMATEL. All papers with respect to each category are summarized and analyzed, pointing out their implementing procedures, real applications, and crucial findings. This systematic and comprehensive review holds valuable insights for researchers and practitioners into using the DEMATEL in terms of indicating current research trends and potential directions for further research.Peer Reviewe

Optimization Model for Sustainable Renovations in Buildings

Buildings consume a substantial amount of energy and adversely affect the global climate and environment. According to the US Department of Energy (DOE), buildings account for 39% of total primary energy consumption and 71% of the electricity consumption. The construction and operation phases constitute the largest proportion of the total energy end-use worldwide (Ma et al. 2012). An innovative and comprehensive set of sustainable materials aiming at the envelope of buildings excluding the roofs is employed to define the renovation alternatives in order to ameliorate the sustainability status of the buildings. The model is comprised of a NSGA-II multi-objective optimization algorithm integrated into a simulation engine. Simulation runs are performed to compute the objective function values and transfer them to the optimization algorithm. A hybrid fuzzy simulation-based optimization model is developed to select the optimum renovation alternatives. The model simultaneously minimizes annual energy consumption and capital cost of an existing office building based on a multi-objective optimization problem. Fuzzy set theory is assigned to the objective functions to address the uncertainty associated with calculation of energy consumption and capital cost values. Conclusively, the model is implemented on a sample case to substantiate the capabilities of the developed model. The case study is a one-story office building with a double skin facade on the south facing facade in Montreal. The results illustrate nine Pareto optimal points and demonstrate that the generated optimum solutions are capable of causing an average of 35% decrease in the annual energy consumption compared to the conventional building scenario