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The shipping industry transports nearly 80% of the goods worldwide and requires large funding. The shipping industry shifted from debt to equity as the source of funding in the last decade. Because most shipping companies already had their initial public offering before 2013, these companies tend to engage in follow-on equity offerings (FPO). However, the challenge faced by the shipping companies is the lack of knowledge on successful FPO. The purpose of this study is to identify the most influential factors affecting shipping companies’ FPO from the investor perspective. This research applies a hybrid multiple-criteria decision-making model integrating the fuzzy-Delphi method and Decision-Making Trial and Evaluation Laboratory, processing survey responses covering four dimensions and 16 criteria from 33 investment experts. The results show that financial indicator is the primary cause affecting offering condition, technical indicators. An increase in earnings per share would help the financial performance of the shipping companies to appear most attractive to investors.

The shipping industry transports nearly 80% of the goods worldwide and requires large funding. The shipping industry shifted from debt to equity as the source of funding in the last decade. Because most shipping companies already had their initial public offering before 2013, these companies tend to engage in follow-on equity offerings (FPO). However, the challenge faced by the shipping companies is the lack of knowledge on successful FPO. The purpose of this study is to identify the most influential factors affecting shipping companies’ FPO from the investor perspective. This research applies a hybrid multiple-criteria decision-making model integrating the fuzzy-Delphi method and Decision-Making Trial and Evaluation Laboratory, processing survey responses covering four dimensions and 16 criteria from 33 investment experts. The results show that financial indicator is the primary cause affecting offering condition, technical indicators. An increase in earnings per share would help the financial performance of the shipping companies to appear most attractive to investors. [ABSTRACT FROM AUTHOR]

The bullwhip effect is a critical issue in supply chain management and is addressed by using a novel multi-layer demand-responsive logistics control method. The intra-member and inter-member relationships are analyzed, linking with demand-related information flows and physical flows, and specifying a conceptual framework to illustrate the potential effect of demand-oriented information deviation on a typical 5-layer supply chain. A stochastic optimal control based algorithm has been developed to accomplish the goal of systematical equilibrium and which also reduces the chain-based logistics operational costs.

A new automatic incident detection (AID) methodology is presented for real-time detection and characterization of freeway incidents. The performance of the real-time AID algorithm is conducted on the basis of simulated and real incident data and the results obtained are discussed.

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Copyright of NTU Management Review is the property of NTU Management Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work solves an integrated model of the continuous berth allocation and specific quay crane (QC) assignment problem with QC coverage range. Based on the analysis of vessel berth position and QC coverage range, a nonlinear mixed-integer programming model is developed. A novel heuristic method based on spatiotemporal conflict analysis is applied to derive a high-quality berth-QCs plan. The initial pessimistic priorities of vessels are employed to reflect the preferences of vessels for terminal resources. The proposed cluster of vessels focuses on spatiotemporal conflicts among vessels. Coordinated assignments of resources for the clustered vessels are realized via the branch-and-bound method. The QC feasibility check identifies infeasibility of QCs-to-vessel assignment. Several experiments are conducted to validate the effectiveness of the proposed method. Numerical results demonstrate that high-quality berth-QCs plans can be obtained within an acceptable time. Further, the influence of coverage range of QC is discussed in detail. [ABSTRACT FROM AUTHOR]

In response to the growing need to reduce greenhouse gas (GHG) emissions worldwide, the emission trading scheme (ETS) has become one of the most efficient instruments to reduce GHG emissions. For example, the European Union (EU) now charges a carbon emission fee to airlines flying into or out of EU airports. This work investigates the effect of carbon permits (CPs) under the cap-and-trade scheme on competition and green transportation strategies of global airlines using a behavioral economics approach. One feature of the proposed model is that passenger attitudes toward green transportation service with perceived uncertainties are conceptualized into airline response functions to characterize how airlines respond to the CP instrument via pricing and investments in green transportation. Qualitative analyses and a numerical study of the ETS-CP instrument provide important managerial insights into airline repricing and green transportation investment strategies. Most importantly, this work generates a relationship between the green transportation and consumer behavior fields via behavioral economics theory. [ABSTRACT FROM AUTHOR]

Under third-party power intervention (TPPI), which increases uncertainty in task environments, complex channel power interplays and restructuring are indispensable among green supply chain members as they move toward sustainable collaborative relationships for increased viability and competitive advantage. From the resource dependence perspective, this work presents a novel conceptual model to investigate the influence of political and social power on channel power restructuring and induced green supply chain collaboration in brander-retailer bidirectional green supply chains of fashionable consumer electronics products (FCEPs). An FCEP refers to the consumer electronics product (e.g., personal computers, mobile phones, computer notebooks, and game consoles) with the features of a well-known brand associated, a short product lifecycle, timely and fashionable design fit for market trends, and quick responsiveness to the variations of market demands. The proposed model is tested empirically using questionnaire data obtained from retailers in the FCEP brander-retailer distribution channels. Analytical results reveal that as an extension of political and social power, TPPI positively affects the reciprocal interdependence of dyadic members and reduces power asymmetry, thereby enhancing the collaborative relationship of dyadic members and leading to improved green supply chain performance. Therein, reciprocal interdependence underlying collaborative relationship is the key to reducing the external environmental uncertainties in the TPPI context. [ABSTRACT FROM AUTHOR]

Short-term prediction of dynamic traffic states remains critical in the field of advanced traffic management systems and related areas. In this article, a novel real-time recurrent learning (RTRL) algorithm is proposed to address the above issue. We dabble in comparing pair predictability of linear method versus RTRL algorithms and simple non-linear method versus RTRL algorithms individually using a first-order autoregressive time-series AR(1) and a deterministic function. A field study tested with flow, speed and occupancy series data collected directly from dual-loop detectors on a freeway is conducted. The numerical results reveal that the performance of RTRL algorithms in predicting short-term traffic dynamics is satisfactorily accepted. Furthermore, it is found that the dynamics of short-term traffic states characterised in different time intervals, collected in diverse time lags and times of day may have significant effects on the prediction accuracy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic optimal-control-based approach to real-time incident-responsive coordinated ramp control. The proposed coordinated ramp-control methodology includes two major functions, i.e., 1) identification of control-zone-based congestion patterns using the modified generalized-sequential-probability-ratio-testing technology and 2) group-based ramp control using stochastic optimal control coupled with extended Kalman filtering technologies. With the aid of the Paramics microscopic traffic simulator, numerical studies under various simulated incident-induced congestion conditions were conducted. Corresponding numerical results indicate the feasibility of the proposed ramp-control method in responding to diverse lane-blocking incident-induced traffic-congestion conditions [ABSTRACT FROM PUBLISHER]

Queue overflow is a critical issue in developing queue prediction technologies for applications in Advanced Transportation Management System (ATMS). Conventional queue prediction methods, however, are limited to incident-free queue length prediction where traffic arrivals can be readily obtained using detectors. Despite the problems posed by queue overflow, studies addressing queue-overflow issues, or for predicting queue overflows beyond detectors, appear inadequate. This paper describes an advanced methodology which uses a stochastic system modeling approach and random processes for predicting queue lengths beyond detectors in real time. Lane changing is taken into account in developing the queue-overflow prediction model because lane changing accompanies queue overflow in most cases. A discrete-time, nonlinear stochastic system is specified for modeling the queues and lane changes beyond detectors during queue-overflow occurrence. The noise terms of the recursive equations of the model account for the effects of queues and a variety of arriving volumes on vehicular lane-changing maneuvers during queue-overflow occurrence. The unknown traffic arrivals beyond detectors are predicted employing random processes. In addition, a recursive estimation algorithm for predicting real-time queue overflows is developed utilizing the extended Kalman filtering technique. Preliminary test results indicate that the proposed methodology is promising for real-time prediction of queue overflows. The predicted queue overflows can be used not only in understanding the phenomenon of lane traffic patterns during queue-overflow occurrence, but also in developing related advanced technologies such as real-time road traffic congestion control and management systems. [ABSTRACT FROM AUTHOR]

Queue overflow is a critical issue in developing queue prediction technologies for applications in Advanced Transportation Management Systems (ATMS). Conventional queue prediction methods, however, are limited to incident-free queue length prediction where traffic arrivals can be readily obtained using detectors. Despite the problems posed by queue overflow, studies addressing queue-overflow issues, or for predicting queue overflows beyond detectors, appear inadequate. This paper describes an advanced methodology which uses a stochastic system modeling approach and random processes for predicting queue lengths beyond detectors in real time. Lane changing is taken into account in developing the queue-overflow prediction model because lane changing accompanies queue overflow in most cases. A discrete-time, nonlinear stochastic system is specified for modeling the queues and lane changes beyond detectors during queue-overflow occurrence. The noise terms of the recursive equations of the model account for the effects of queues and a variety of arriving volumes on vehicular lane-changing maneuvers during queue-overflow occurrence. The unknown traffic arrivals beyond detectors are predicted employing random processes. In addition, a recursive estimation algorithm for predicting real-time queue overflows is developed utilizing the extended Kalman filtering technique. Preliminary test results indicate that the proposed methodology is promising for real-time prediction of queue overflows. The predicted queue overflows can be used not only in understanding the phenomenon of lane traffic patterns during queue-overflow occurrence, but also in developing related advanced technologies such as real-time road traffic congestion control and management systems. [ABSTRACT FROM AUTHOR]

Real-time, incident-responsive traffic control and management is vital to development of advanced incident management systems in ITS. More importantly, it provides, from an academic point of view, the linkages between incident detection, incident management, and traffic signal control. This study explores the application of a stochastic optimal control approach to real-time, incident-responsive traffic control at isolated intersections. In the methodology development, time-varying lane traffic state variables and control variables are specified to characterize sectionwide interlane and intralane traffic states under conditions of lane-blocking incidents. Following specification of system states, we formulated a discretetime nonlinear stochastic model that comprises four types of equations, namely (1) recursive equations, (2) measurement equations, (3) incident-induced delay equations, and (4) boundary constraints. From the proposed stochastic model we then developed a stochastic optimal control algorithm to update the time-varying control variables and lane traffic state variables in real-time with lane-blocking incidents at isolated intersections. To generate traffic data used in model tests efficiently, we employed an advanced microscopic traffic simulator, Paramics, Version 3.0, which is developed to model and analyze ITS traffic flow conditions. The preliminary test results indicate that the proposed method can accomplish the goal of real-time, incident-responsive traffic signal control. In addition to proposing a new methodology, we hope that this study can initiate investigation into real-time, incident-responsive traffic control and management to achieve the final goal of networkwide incident-responsive, traffic-optimal control for incident management. [ABSTRACT FROM AUTHOR]

Presents a study which explored sequential decision methodology integrating a generalized sequential probability ratio testing (GSPRT) approach with a strategy-value matrix analytical tool to determine the developmental priorities of commercial vehicle operations (CVO) technical packages for CVO time-based strategic planning. Overview of matrix-based analytical tools used for strategic planning and the fundamentals of modified-GSPRT; Framework construction of the strategy-value matrix; Survey design and data processing used; Description of the sequential group decision-making algorithm.

It is clear that sustainable transport is a challenging research and policy task. It requires a solid quantitative modelling, a collection of extensive databases, an analysis of the span of control of governments and stakeholders and a creative open mind in regard to the future. The case of a more mobile society and of justified demands of the Third World will prompt many intricate concerns, in which a blend of technological, behavioural and regulatory research and policy initiatives will be needed. Some progress has been made in the past years, but much more progress is necessary in the years to come. In particular, due attention is needed for the relationship between sustainability and efficiency in transport. This paper introduces both the topic of efficient and sustainable transportation systems and also, briefly, the papers from the special issue. [ABSTRACT FROM AUTHOR]