An Evaluation Model of Quantitative and Qualitative Fuzzy Multi-Criteria Decision-Making Approach for Location Selection of Transshipment Ports

The role of container logistics centre as home bases for merchandise transportation has become increasingly important. The container carriers need to select a suitable centre location of transshipment port to meet the requirements of container shipping logistics. In the light of this, the main purpose of this paper is to develop a fuzzy multi-criteria decision-making (MCDM) model to evaluate the best selection of transshipment ports for container carriers. At first, some concepts and methods used to develop the proposed model are briefly introduced. The performance values of quantitative and qualitative subcriteria are discussed to evaluate the fuzzy ratings. Then, the ideal and anti-ideal concepts and the modified distance measure method are used in the proposed model. Finally, a step-by-step example is illustrated to study the computational process of the quantitative and qualitative fuzzy MCDM model. The proposed approach has successfully accomplished our goal. In addition, the proposed fuzzy MCDM model can be empirically employed to select the best location of transshipment port for container carriers in the future study

The Hurwicz decision rule’s relationship to decision making with the triangle and beta distributions and exponential utility

Non-probabilistic approaches to decision making have been proposed for situations in which an individual does not have enough information to assess probabilities over an uncertainty. One non-probabilistic method is to use intervals in which an uncertainty has a minimum and maximum but nothing is assumed about the relative likelihood of any value within the interval. The Hurwicz decision rule in which a parameter trades off between pessimism and optimism generalizes the current rules for making decisions with intervals. This article analyzes the relationship between intervals based on the Hurwicz rule and traditional decision analysis using a few probability distributions and an exponential utility functions. This article shows that the Hurwicz decision rule for an interval is logically equivalent to: (i) an expected value decision with a triangle distribution over the interval; (ii) an expected value decision with a beta distribution; and (iii) an expected utility decision with constant absolute risk aversion with a uniform distribution. These probability distributions are not exhaustive. There are likely other distributions and utility functions for which equivalence with the Hurwicz decision rule can also be established. Since a frequent reason for the use intervals is that intervals assume less information than a probability distribution, the results in this article call into question whether decision making based on intervals really assumes less information than subjective expected utility decision making

What does decision making with intervals really assume? The relationship between the Hurwicz decision rule and prescriptive decision analysis

Decision analysis can be defined as a discipline where a decision maker chooses the best alternative by considering the decision maker’s values and preferences and by breaking down a complex decision problem into simple or constituent ones. Decision analysis helps an individual make better decisions by structuring the problem. Non-probabilistic approaches to decision making have been proposed for situations in which an individual does not have enough information to assess probabilities over an uncertainty. One non-probabilistic method is to use intervals in which an uncertainty has a minimum and maximum but nothing is assumed about the relative likelihood of any value within the interval. The Hurwicz decision rule in which a parameter trades off between pessimism and optimism generalizes the current rules for making decisions with intervals. This thesis analyzes the relationship between intervals based on the Hurwicz rule and traditional decision analysis using probabilities and utility functions. This thesis shows that the Hurwicz decision rule for an interval is logically equivalent to: (i) an expected value decision with a triangle distribution over the interval; (ii) an expected value decision with a beta distribution; and (iii) an expected utility decision with a uniform distribution. The results call into question whether decision making based on intervals really assumes less information than subjective expected utility decision making. If an individual is using intervals to select an alternative—for which the interval decision rule can be described with the Hurwicz equation—then the individual is implicitly assuming a probability distribution such as a triangle or beta distribution or a utility function expressing risk preference

A fuzzy approach for adaptive reuse selection of industrial buildings in Hong Kong

With rapid economic development and restructuring, there are an increasing number of aged or obsolete buildings in large cities, such as Hong Kong. Adaptive reuse of these buildings provides an alternative for property stakeholders towards more sustainable practices instead of redevelopment or destruction. Adaptive reuse can also make great contributions to sustainable development by reducing construction waste and saving natural resources. As a result of industrial restructuring, manufacturing plants were migrated from Hong Kong to Mainland China during the 1980s and 1990s. Many industrial buildings then became vacant or under-utilised. Adaptive reuse of these industrial buildings is considered a viable way forward for all parties, including government, property stakeholders and the community. However, the problem is how to deal with multiple criteria to assess how these buildings can be reused for residential living, retail, training centres, or other purposes. Adaptive reuse of industrial buildings is discussed in this paper, and a fuzzy adaptive reuse selection model is developed for decision-making. A hypothetical example is used to demonstrate the application of the method and show its effectiveness

Bayesian Fuzzy Clustering with Robust Weighted Distance for Multiple ARIMA and Multivariate Time-Series

The paper suggests and develops a computational approach to improve hierarchical fuzzy clustering time-series analysis when accounting for high dimensional and noise problems in dynamic data. A Robust Weighted Distance measure between pairs of sets of Auto-Regressive Integrated Moving Average models is used. It is robust because Bayesian Model Selection methodology is performed with a set of conjugate informative priors in order to discover the most probable set of clusters capturing different dynamics and interconnections among time-varying data, and weighted because each time-series is 'adjusted' by own Posterior Model Size distribution in order to group dynamic data objects into 'ad hoc' homogenous clusters. Monte Carlo methods are used to compute exact posterior probabilities for each cluster chosen and thus avoid the problem of increasing the overall probability of errors that plagues classical statistical methods based on significance tests. Empirical and simulated examples describe the functioning and the performance of the procedure. Discussions with related works and possible extensions of the methodology to jointly deal with endogeneity issues and misspecified dynamics in high dimensional multicountry setups are also displayed

Bayesian Fuzzy Clustering with Robust Weighted Distance for Multiple ARIMA and Multivariate Time-Series

The paper suggests and develops a computational approach to improve hierarchical fuzzy clustering time-series analysis when accounting for high dimensional and noise problems in dynamic data. A Robust Weighted Distance measure between pairs of sets of Auto-Regressive Integrated Moving Average models is used. It is robust because Bayesian Model Selection methodology is performed with a set of conjugate informative priors in order to discover the most probable set of clusters capturing different dynamics and interconnections among time-varying data, and weighted because each time-series is 'adjusted' by own Posterior Model Size distribution in order to group dynamic data objects into 'ad hoc' homogenous clusters. Monte Carlo methods are used to compute exact posterior probabilities for each cluster chosen and thus avoid the problem of increasing the overall probability of errors that plagues classical statistical methods based on significance tests. Empirical and simulated examples describe the functioning and the performance of the procedure. Discussions with related works and possible extensions of the methodology to jointly deal with endogeneity issues and misspecified dynamics in high dimensional multicountry setups are also displayed

Inventory management under uncertainty : a military application

Inventory management under uncertainty is a widely researched field and many different types of inventory models have been used to address inventory problems in practice [1, 10, 11, 26, 50, 35]. However, there is a lack of published studies focusing on inventory planning in environments, such as the military, that are characterised by uncertainty as a result of extreme events. A critical area in military decision support is inventory management. Planning for stock levels in particular can be a daunting task, due to the uncertainty associated with the future. The military is typically an environment where improbable events can have massive impacts on operations; and the availability of the correct amount of stock can enhance the responsiveness, efficiency, and preparedness of the military, and ultimately save human lives. On the other hand, excessive stock - especially ammunition - can result in huge monetary losses through damages, stock degradation, and stock obsolescence. Excessive ammunition also poses a risk to public safety, and can ultimately challenge a country's ability to control the use of force. It is therefore very important to provide proper attention to determining the required stock levels during military inventory management. This dissertation aims, therefore, to develop a reliable decision support tool that can assist with inventory management in the military. To achieve this, a mixed multi-objective mathematical model is used that attempts to minimise cost, shortages, and stock while incorporating demand uncertainty by means of probability distributions and fuzzy numbers. The model considers three different scenarios, and determines the minimum required stock level and the best order quantity for three different stock categories, for a single ammunition item. The model is converted into its crisp, non-fuzzy, and deterministic counterpart first by transforming the fuzzy constraints into their crisp versions and then deriving the deterministic model of the crisp recourse stochastic model. The corresponding crisp, deterministic model is then solved using exact branch-and-bound embedded in the LINGO 10.0 optimisation software package and the reliability of the solutions in different scenarios is tested by means of discrete event simulation. The reliability of the model is then compared with the reliabilities of the well known (r;Q) and (s; S) inventory models in the literature. The comparison indicates that the mixed model proposed in this dissertation is more reliable in extreme scenarios than the (r;Q) and (s; S) inventory models in the literature. A sensitivity analysis is then performed and results indicate that the model yields reliable solutions with a reliability that varies between 74.54% and 100%, depending on the scenario investigated. The lower reliability is during the high demand scenario, this is caused by the ability of the inventory model to prioritise different scenarios based on their estimated possibility to ensure that stock levels are not unneccessary escalated for highly improbable events. It can be concluded that the proposed mixed multi-objective mathematical model that aims to minimise inventory cost, surplus stock, and shortages is a reliable inventory decision support model for the uncertain military environment.Dissertation (MEng)--University of Pretoria, 2011.Industrial and Systems Engineeringunrestricte

RELIABILITY ANALYSIS OF MARINE PILOTS USING ADVANCED DECISION MAKING METHODS

Seaports play a significant role in global logistics networks, contributing to the efficiency of both national and international economic growth. Dramatic changes in the supply chain encourage ports to maintain effective integration when delivering services. Ports are thus parts of complex systems operating in uncertain operational environments. Accident investigation shows that there has been a significant increase in marine accidents contributed to by human error during marine pilotage operations. The human element has been identified as a major critical factor for most operational failures. Therefore, an adequate understanding of the key factors influencing pilot reliability plays a vital role in all high-risk industries, among which maritime operations are included. This study aims to develop a new quantitative marine pilot reliability assessment methodology, known as the Marine Pilot’s Reliability Index (MPRI). The MPRI seeks to help decision makers in identifying the effects of certain factors on pilot reliability. Although human reliability has been investigated in different disciplines, there is no consensus on the selected criteria. Therefore, in this study, the researcher employed a hybrid research approach, comprised of qualitative and quantitative approaches in a sequential exploratory approach to elicit the key factors that are considered dominant in maintaining the reliability of a marine port pilot. This was conducted through a series of investigation tools such as field observation, semi-structured focus-group interviews, and port pilotage accident data analysis. This step culminated in a composite of four main criteria with thirteen sub-factors, which pilots considered dominant to their reliability. These factors are arranged in a hierarchal order forming the new developed MPRI. To ensure the applicability of the identified MPRI factors, the researcher applied a Delphi technique in examining the degree of agreement among experts towards the identified MPRIs. Two rounds of questionnaires were conducted. The results obtained show a high degree of agreement among experts towards the identified factors. This is followed by the application of the analytical hierarchal process (AHP) approach to determine the relative weights of all identified criteria. The second approach, a new conceptual MPRI interdependency model is constructed using a hybrid approach of a fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and an analytical network process (ANP). This hybrid approach helps to deal with inherent uncertainties and highlights the degree of interdependences in the developed MPRIs. To examine the feasibility of the proposed model and determine the outputs from this research, the researcher employed a fuzzy evidential reasoning (FER) for solving multiple criteria decision-making (MCDM) problems in conjunction with the aforementioned approaches to empirically assess the reliability of a marine pilot. The application of FER helps manage uncertainties resulting from the nature of operations. Three senior marine pilots have been assessed using the developed reliability assessment tool. The results reveal the novelty of this assessment tool in offering an effective and flexible reliability assessment and a diagnostic instrument for decision makers to predict a reduction in a pilot’s reliability. The developed model is partially validated using a sensitivity analysis. The novelty of this work offers a foundation towards assessing the reliability of marine pilotage operations using risk-based methodologies with variance techniques to facilitate the acquisition of qualitative and quantitative data and to ensure safe and efficient port operations