Port choice by intra-regional container service operators : an application of decision-making techniques to liner services between Malaysian and other Asian ports

Intra-regional container service operators are challenged to design regular and reliable liner services connecting regional ports at the lowest cost and shortest transit time while considering customer demand. This paper focuses on the selection of ports of call in regular intra-regional container services, an under-researched part of the container shipping market. A combination of decision-making techniques (i.e. Analytical Hierarchy Process, fuzzy link-based and Evidential Reasoning) are presented to assist intra-regional container service operators in selecting ports of call. The proposed methodology is empirically applied to container services between Malaysian and other nearby Asian ports. While Port Klang is the main gateway to Malaysia, the results show that other Malaysian ports should play a more prominent role in accommodating intra-Asian container services. This research can assist maritime stakeholders in evaluating intra-regional port-to-port liner service configurations. Furthermore, the novel mix of decision-making techniques complements and enriches existing academic literature on port choice and liner service configuration

A new hybrid approach to human error probability quantification-applications in maritime operations

Human Reliability Analysis (HRA) has always been an essential research issue in safety critical systems. Cognitive Reliability Error Analysis Method (CREAM), as a well-known second generation HRA method is capable of conducting both retrospective and prospective analysis, thus being widely used in many sectors. However, the needs of addressing the use of a deterministic approach to configure common performance conditions (CPCs) and the assignment of the same importance to all the CPCs in a traditional CREAM method reveal a significant research gap to be fulfilled. This paper describes a modified CREAM methodology based on an Evidential Reasoning (ER) approach and a Decision Making Trial and Evaluation Laboratory (DEMATEL) technique for making human error probability quantification in CREAM rational. An illustrative case study associated with maritime operations is presented. The proposed method is validated by sensitivity analysis and the quantitative analysis result is verified through comparing the real data collected from Shanghai coastal waters. Its main contribution lies in that it for the first time addresses the data incompleteness in HEP, given that the previous relevant studies mainly focus on the fuzziness in data. The findings will provide useful insights for quantitative assessment of seafarers' errors to reduce maritime risks due to human errors

AN INTERVAL TYPE 2 FUZZY EVIDENTIAL REASONING APPROACH TO PERSONNEL RECRUITMENT

Recruitment process is a procedure of selecting an ideal candidate amongst different applicants who suit the qualifications required by the given institution in the best way. Due to the multi criteria nature of the recruitment process, it involves contradictory, numerous and incommensurable criteria that are based on quantitative and qualitative measurements. Quantitative criteria evaluation are not always dependent on the judgement of the expert, they are expressed in either monetary terms or engineering measurements, meanwhile qualitative criteria evaluation depend on the subjective judgement of the decision maker, human evaluation which is often characterized with subjectivity and uncertainties in decision making. Given the uncertain, ambiguous, and vague nature of recruitment process there is need for an applicable methodology that could resolve various inherent uncertainties of human evaluation during the decision making process. This work thus proposes an interval type 2 fuzzy evidential reasoning approach to recruitment process. The approach is in three phases; in the first phase in order to capture word uncertainty an interval type 2(IT2) fuzzy set Hao and Mendel Approach (HMA) is proposed to model the qualification requirement for recruitment process. This approach will cater for both intra and inter uncertainty in decision makers’judgments and demonstrates agreements by all subjects (decision makers) for the regular overlap of subject data intervals and the manner in which data intervals are collectively classified into their respective footprint of uncertainty. In the second phase the Intervaltype 2 fuzzy Analytical hierarchical process was employed as the weighting model to determine the weight of each criterion gotten from the decision makers. In the third phase the interval type 2 fuzzy was hybridized with the ranking evidential reasoning algorithm to evaluate each applicant to determine their final score in order to choose the most ideal candidate for recruitment.The implementation tool for phase two and three is Java programming language. Application of this proposed approach in recruitment process will resolve both intra and inter uncertainty in decision maker’s judgement and give room for consistent ranking even in place of incomplete requirement

Fuzzy belief structure based VIKOR method: an application for ranking delay causes of Tehran metro system by FMEA criteria

Public transport is a critical part of civilization in this decade. The amount of money invested and the criticality of transferring people in an acceptable time and without any conflict made it a challenging problem for managers, especially in metropolises. Absolutely, making effective decisions in this area requires considering different aspects. Waiting time is a key criterion in apprising quality of public transport. In this paper, a real world case study of ranking causes of delay in Tehran (Iran) metro system is solved by developing multi attribute group decision-making VIšeKriterijumska Optimizacija I KOmpromisno Rešenje (in Serbian, VIKOR) method under uncertainty, where this uncertainty is captured by Fuzzy Belief Structures (FBS). The obtained results are then compared with a previously proposed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method with FBSs. The results show that human related issues, along with the problems related to line and transportation system are the most important causes of delay. The obtained results of the problem seem acceptable for decision makers

Risk-based framework for ballast water safety management

Ballast water has been identified as a major vector for the translocation of Non- Indigenous Invasive Species (NIS) and pathogens across zoogeographical regions and subsequent discharged into recipient port states/regions. This is bound to increase given factors like the globalization of trade and the economy of scale of the ship size. Established NIS has posed significant threat to the human health, economy, finances and marine bio-diversity of recipient regions and port states. The risks associated with the discharged NIS are uncertain and difficult to assess due to the stochastic nature of species assemblages and dispersal mechanism. The safest control measure advocated by the IMO is the conduct of ballast water exchange at sea while appropriate and effective proto-type treatment technologies are being developed and approved for future application. This study has been conducted while recognizing the inability of probabilistic approaches applied in ballast water risk management to addressing uncertainty and inadequacy of data. A qualitative approach using powerful multi-criteria decision making techniques and the safety principles of the Formal Safety Assessment framework have been utilized in this research to develop three generic models for ballast water hazard estimation, risk evaluation and decision-making analysis respectively. The models are capable of being modified and utilized in the industry to address the problems of uncertainty and inadequacy of data in ballast water management. This is particularly useful as an interim measure for port states in developing economies (with insufficient data and technology) to developed robust ballast water management plans. While recognising the huge impact of ballast water pollution in recipient regions this study recommends that ballast water management programmes be given due recognition as an important element of sustainable development programmes at national and international levels. The non-availability of a benchmark based on previous research on which to fully validate the research outcome was identified as a major limitation of this research study. The models developed will therefore be subject to modifications as new data become available

Decision Making Analysis for an Integrated Risk Management Framework of Maritime Container Port Infrastructure and Transportation Systems

This research proposes a risk management framework and develops generic risk-based decision-making, and risk-assessment models for dealing with potential Hazard Events (HEs) and risks associated with uncertainty for Operational Safety Performance (OSP) in container terminals and maritime ports. Three main sections are formulated in this study: Section 1: Risk Assessment, in the first phase, all HEs are identified through a literature review and human knowledge base and expertise. In the second phase, a Fuzzy Rule Base (FRB) is developed using the proportion method to assess the most significant HEs identified. The FRB leads to the development of a generic risk-based model incorporating the FRB and a Bayesian Network (BN) into a Fuzzy Rule Base Bayesian Network (FRBN) method using Hugin software to evaluate each HE individually and prioritise their specific risk estimations locally. The third phase demonstrated the FRBN method with a case study. The fourth phase concludes this section with a developed generic risk-based model incorporating FRBN and Evidential Reasoning to form an FRBER method using the Intelligence Decision System (IDS) software to evaluate all HEs aggregated collectively for their Risk Influence (RI) globally with a case study demonstration. In addition, a new sensitivity analysis method is developed to rank the HEs based on their True Risk Influence (TRI) considering their specific risk estimations locally and their RI globally. Section 2: Risk Models Simulations, the first phase explains the construction of the simulation model Bayesian Network Artificial Neural Networks (BNANNs), which is formed by applying Artificial Neural Networks (ANNs). In the second phase, the simulation model Evidential Reasoning Artificial Neural Networks (ERANNs) is constructed. The final phase in this section integrates the BNANNs and ERANNs that can predict the risk magnitude for HEs and provide a panoramic view on the risk inference in both perspectives, locally and globally. Section 3: Risk Control Options is the last link that finalises the risk management based methodology cycle in this study. The Analytical Hierarchal Process (AHP) method was used for determining the relative weights of all criteria identified in the first phase. The last phase develops a risk control options method by incorporating Fuzzy Logic (FL) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to form an FTOPSIS method. The novelty of this research provides an effective risk management framework for OSP in container terminals and maritime ports. In addition, it provides an efficient safety prediction tool that can ease all the processes in the methods and techniques used with the risk management framework by applying the ANN concept to simulate the risk models

Analytical Quality Control in Shipping Operation Using Six Sigma Principles

A large number of benefits achieved through the successful implementation of Six Sigma programmes in different industries have been documented. However, very little research has been conducted on their applications in the shipping sector, especially in the Onshore Service Functions (OSFs) of shipping companies. Literature shows that heavy human involvement in the service industries such as shipping leads to a high volume of uncertainties which are difficult to be correctly and effectively measured or managed by simply using the traditional data analysis and statistical methods in Six Sigma. The aim of this study is to develop new quantitative analytical methodologies to enable the application and implementation of Six Sigma to improve the service quality of OSFs in shipping companies. Intensive investigations on the feasibility and effectiveness of the developed new methods and models through case studies in world leading container ship lines and shipping management companies have been carried out to ensure the achievement of the aim.This study firstly reviews the evolvement of quality control and some typical methods in the area, the development of Six Sigma, its tools and current applications, especially in the service industries. It is followed by a new framework of the Six Sigma implementation in the OSFs of shipping companies which is supported by a few real process excellence projects carried out in a world-leading ship line. In the process of the framework development, various issues and challenges appear largely due to the existence of uncertainties in data such as ambiguity and incompleteness caused by extensive subjective judgements. Advanced methods and models are developed to tackle the above challenges as well as complement the traditional Six Sigma tools so that the new Six Sigma methodologies can be confidently applied in situations where uncertainties in data exist at different levels.A new fuzzy Technique for Order Preference by Similarity to an Ideal Solution ii(TOPSIS) method is developed by combining the traditional TOPSIS, fuzzy numbers and interval approximation sets to facilitate the effective selection of Six Sigma projects and achieve the optimal use of resources towards the company objectives. A revised Failure Mode and Effects Analysis (FMEA) model is proposed in the “Analyse” step in Six Sigma to improve the capability of classical FMEA in failure identification in service industries. The new FMEA model uses the Analytical Hierarchy Process (AHP) and Fuzzy Bayesian Reasoning (FBR) approaches to increase the accuracy of failure identification while not compromising the easiness and visibility of the Risk Priority Number (RPN) method. Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) methods are incorporated with Fuzzy logic and Evidential Reasoning (ER), for the very first time to generate a Key Performance Indicators (KPIs) management method where the weights of indicators are rationally assigned by considering the interdependency among the indicators. Incomplete and fuzzy evaluations of the KPIs are synthesised in a rational way to achieve a compatible and comparable result.It is concluded that the newly developed Six Sigma framework together with its supporting quantitative analytical models has made significant contribution to facilitate the quality control and process improvement in shipping companies. It has been strongly evidenced by the success of the applications of the new models in real cases. The financial gains and continuous benefits produced in the investigated shipping companies have attracted a wider range of interests from different service industries. It is therefore believed that this work will have a high potential to be tailored for a wide range of applications across sectors and industries when the uncertainties in data exceed the ability that the classical Six Sigma tools and methods possess