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Purpose There are many different pollution reduction strategies even within different ports in the same country. Every port can learn from these environmental protection strategies. What remains universally key, though, is to select the optimum strategy to reduce pollution. This paper aims to use a fuzzy analytic hierarchy process (FAHP) to prioritize the relative weights of key criteria assessing the port environmental protection and select the best port in Taiwan. Design/methodology/approach Four criteria and eleven sub-criteria of the FAHP model are developed through expert interviews and relevant literature review. Three alternative ports considered are Kaohsiung, Keelung and Taichung. The expert questionnaire samples (23) include four groups: shipping operators, port operators, governmental officials and academics. Findings Results indicate that finance is the most important evaluation criteria, followed by port environmental policy, technology and stakeholders. Among the three ports compared, Kaohsiung Port is selected as the best port, followed by Keelung and Taichung port. Originality/value The result of this paper can help fill the gap in the existing literature regarding decision analysis techniques for port pollution regulation and expect to present a holistic picture of the important evaluation criteria related to port environmental protection as well as raise issues of public awareness concern and consequently improve green port sustainability.

In today’s era of mega ships, rail is increasingly being recognized as an important and sustainable mode to transport containers in and out of congested marine terminals. However, rail’s potential impact on terminal and road congestion is highly dependent on the ability to fully utilize the available train space. This paper contributes to the literature by presenting an optimization model that can create high-utilization loading plans for double stack trains at marine terminals. To facilitate the solution of the model, it will be shown how the initial binary nonlinear model can be linearized. Model properties will also be derived and discussed. To illustrate the usefulness of the proposed model in a practical setting, a realistic case study is conducted. The results demonstrate that the model is able to find optimal loading plans for double-stack trains in realistic settings.

This paper extends and complements the liner shipping literature by examining the ship deployment decision in the presence of time windows at container ports. A set of tight bounds on the number of...

To relieve pressure from large vessels and intensive arrival of trucks, an increasing number of marine terminals require trucks to make appointments prior to delivering containers. The quotas are supposed not to impede truck deliveries but should be sufficiently used. For terminals where trucks are allowed to make appointments and arrive on the same day, a critical problem is the simultaneous allocation of appointment quotas and yard cranes. A bi-objective integer model is set up to balance the trade-off between terminals and trucks. A three-dimensional quota is used for characterizing the variabilities in delivery operation. It also overcomes the challenges involved, including the inside workload, and the yard closing deadline. A non-dominated genetic algorithm II-based approach is developed to solve the problem. The method is validated through extensive numerical experiments, and the results show the advantages of simultaneously balancing the workload of yard cranes instead of merely smoothing truck arrivals. Parameter analysis then reveals the influence of both larger vessel size and an increase in trade, as well as the utility ratio of quotas.

This paper examines the liner fleet deployment problem when only conditional shipping demand information is known. For such a case, it is generally not possible to determine the exact optimal solution. A set of complementary upper and lower bounds on the optimal cost are derived by exploiting the problem structure. These bounds are explicitly shown to converge to the optimal cost when a sufficiently refined partition is available. A numerical example illustrates the model.

A hinterland transport chain is the transport network on the landward side of a seaport, over which cargo moves to/from the seaport. A hinterland transport chain choice is jointly determined by seaports, dry ports, intermodal carriers, importers and exporters. In this paper, determinants of sea/dry port choice, shipper choice and intermodal carrier choice are deduced to also be determinants of hinterland transport chain choice. Furthermore, a behavioral model is presented that describes the joint choice of a hinterland transport chain, accounting for each of the above parties’ objectives: sea/dry ports seek to maximize throughput; intermodal carriers seek to maximize profits; and shippers seek to minimize logistics cost. The behavioral model is used in the analysis of unexplored areas in the hinterland transport chain literature, i.e. the fundamental issues of the existence and uniqueness of such chains.

This paper considers the integrated drayage scheduling problem. Two new models are developed that account for the uncertainty of (un)packing times in drayage operation without an explicit a...

Vessel speed optimisation has, in recent years, been recognised as a practical and important cost reduction strategy in the maritime shipping industry. This paper takes a new look at the trade-off ...

Traditionally, cargo port choice has been investigated from the perspective of port users. More recently, studies on cargo port choice from the perspective of the port service providers have appeared. Irrespective of the perspective taken, the common element in these studies is that they focus on one perspective at a time. In this paper, it is postulated that cargo port choice should really be examined from both perspectives, simultaneously, as cargo port choice is made by both users and providers of cargo port services. The port users we will focus on in this paper are the shippers. To model this simultaneous choice, a new modeling framework is proposed, i.e. cargo port choice equilibrium. This new framework enables policy makers and other maritime industry stakeholders to evaluate the network-wide impact (i.e. across all cargo ports) of a range of policy measures while considering the choices by both shippers and port service providers. One key consequence of our equilibrium model is that prior findings in the port choice literature might no longer be valid without further qualifications. Overall, this paper generalizes, unifies and extends a segment of the port choice literature.

In maritime freight transportation, carriers build collaborative relationships with other carriers while competing with each other to optimize their own profits. In such a scenario, a game of coopetition is formed. We formulate a nonlinear mixed-integer problem to determine the optimal levels of coopetition for a single company and embed the resulting problem into a general game theoretic framework. A diagonalization algorithm that incorporates an ascent direction search technique is developed to effectively evaluate the game. The numerical results show that carriers choose similar coopetition levels to maximize their profits, and the coopetition game can reach equilibrium under general conditions.

Marine container chassis have emerged at the forefront of container port operations in the U.S., with chassis dislocation being a major new problem. To manage chassis dislocation, two strategies are presented in this paper. In the first strategy, a model is presented to support the chassis repositioning decision faced by chassis pool operators. We consider the case that an (accurate) probability distribution of chassis demand is unknown. For such a case, it is generally not possible to determine the exact optimal number of chassis to reposition. Lower and upper bounds on the optimal cost are derived that are explicitly shown to converge to the minimum cost. The second strategy is chassis yard consolidation, which has the potential to reduce the variance in chassis demand and, therefore, lead to more accurate demand forecasts. An effective algorithm to iteratively reduce the variance in chassis demand is provided. Numerical experiments are presented to illustrate the strategies.

In this paper robust models are presented for the transportation service network design problem, using the ferry service network design problem as an example application. The base assumption is that only the mean and an upper bound on the passenger demand are known. In one robust model, this information is supplemented by a lower bound on the demand, whereas in a second robust model, the assumption is made that the variance of the demand is known, in addition to the mean and upper bound. The relationship between the two models is investigated and characterized analytically. A case study using the ferry service in Hong Kong is provided to illustrate the models.

Port multi-service congestion occurs when port users of two or more different services (i.e., multi-services) provided at the same port node or over the same port link interfere with one another to the extent that they experience service congestion at the shared node or over the shared link. Container port nodes shared in the provision of port multi-services include, for example, the berth and yard. Container port links shared in the provision of port multi-services include, for example, the yard-to-departure-gate link, entrance-gate-to-yard link, berth-to-yard link and yard-to-berth link. If port multi-service congestion exists at a port node (or over a port link), then port multi-service congestion can be propagated to other port nodes and links in the port as long as there is a connecting path.

Many existing studies on the sensor health problem determine an individual sensor’s health status based on the statistical characteristics of collected data by the sensor. In this research, we study the sensor health problem at the network level, which is referred to as the network sensor health problem. First, based on the conservation principle of daily flows in a network, we separate all links into base links and non-base links, such that the flows on the latter can be calculated from those on the former. In reality, the network flow conservation principle can be violated due to the existence of unhealthy sensors. Then we define the least inconsistent base set of links as those that minimize the sum of squares of the differences between observed and calculated flows on non-base links. But such least inconsistent base sets may not be unique in a general road network. Finally we define the health index of an individual sensor as the frequency that it appears in all of the least inconsistent base sets. Intuitively, a lower health index suggests that the corresponding sensor is more likely to be unhealthy. We present the brute force method to find all least inconsistent base sets and calculate the health indices. We also propose a greedy search algorithm to calculate the approximate health indices more efficiently. We solve the network sensor health problem for a real-world example with 16 nodes and 30 links, among which 18 links are monitored with loop detectors. Using daily traffic count data from the Caltrans Performance Measurement System (PeMS) database, we use both the brute-force and greedy search methods to calculate the health indices for all the sensors. We find that all the four sensors flagged as unhealthy (high value) by PeMS have the lowest health indices. This confirms that a sensor with a lower health index is more likely to be unhealthy. Therefore, we can use such health indices to determine the relative reliability of different sensors’ data and prioritize the maintenance of sensors.

Ports provide services rather than producing physical products. However, port cost functions specify port cost as functions of physical products, e.g., port cargo throughput long-run cost is a function of port resource prices and cargo throughput (a physical product). In comparison, port cargo service long-run cost is a function of port resource prices, cargo service and shipper cargo received. The shipper provides cargo that is serviced by the port. A service cannot be touched and its user is involved in its provision. This paper derives port cost functions for which port outputs are “service outputs”, e.g., cargo, vessel and vehicle services.

This paper investigates determinants of the probability of non-fatal and fatal crew injuries in container vessel accidents. The determinants are deduced from an estimated probit regression equation based upon 2001–2008 U.S. Coast Guard container vessel accident data. The estimation results suggest that a crew member is: (1) less likely to have a non-fatal injury in containership and ro-ro container vessel accidents if the vessel has a steel hull and the vessel accident occurs at nighttime, (2) more likely to have a fatal injury if fire is involved in containership and ro-ro container vessel accidents, and (3) less likely to have a fatal injury in containership and ro-ro container vessel accidents if the vessel is U.S. flagged, steel hulled, and powered by a diesel engine. These results are critical in developing new policies in reducing non-fatal and fatal crew injuries in container vessel accidents.

Investment in transportation infrastructure has been widely utilized as an instrument for inducing economic growth. Such investment usually leads to job creation and an increase in per capita income that attracts population through migration to the region. This increases the utilization of the transport infrastructure over time, resulting in high levels of congestion. The congestion negatively impacts the attractiveness of the region and the gross regional product (GRP). Regions cyclically invest in transport infrastructure that temporally spurs economic activity and migration, and reduces congestion. This research employs a system dynamics simulation approach to capture and mimic the behavior of these complex and cyclical relationships over time. Our approach suggests the modeling of key demographic, transportation infrastructure, travel behavior, and economic activity components to determine the impact of infrastructure investments on regional growth. Given a set of prospective investment scenarios, the model replicates and projects levels of productivity, travel demand, congestion, GRP, and net migration patterns over time. The model also provides insights into the duration of critical cyclical patterns given these prospective infrastructure investments. The simulation model presented in this paper seeks to be utilized as guidance to support decision-making processes that lead to the execution of more exhaustive transportation studies that organize the execution of such investments.

Environmental sustainability plays an increasingly significant role in today’s maritime industry. Green strategies such as speed reduction, fuel switching, alternative fuels can effectively decrease the harmful emissions from ships and increase environmental sustainability. In the field of green shipping, existing papers focus on using optimization models to calculate the optimal values of green efforts during shipping process. In contrary, this paper adopts a new and alternative perspective by explore the evolutionarily stable strategy for the shipping lines in the green concerning maritime market. First, the pricing decision is formulated as a model by considering the price elasticity, market competition, green investment and the level of green concerning in the market. Then, an evolutionary game theory framework embedded the pricing model is proposed to study the long-term green strategic behavior of maritime shipping lines. In the proposed game framework, each shipping line can choose to adopt a green strategy which is able to reduce the emission during shipping process or a non-green strategy which is cost-effective. A replicator dynamic system is used to examine the evolutionarily stable strategy that will eventually be chosen by the majority of the shipping lines. To illustrate the results, we conduct a simulation experiment and sensitivity analysis. The results demonstrate that the green-concerned shipping lines can gain higher payoff compared to the shipping lines whose goal are payoff maximization under green concerned market. The findings in this paper can provide guidance to maritime shipping lines in formulating their green strategies.

Port choice by users of port services has been investigated in the literature, but generally not port choice by port service providers. This paper examines the choice of a cargo port by providers of cargo port services from the perspective of a (1) cargo port service chain, (2) hinterland cargo transportation service chain and (3) port hinterland cargo transport service chain. The determinants of cargo port choice by providers of port services from each of these perspectives will be deduced.

This paper presents findings on the hurricane evacuation behavior during Hurricane Irene, using results from a large-scale phone survey among residents in the Hampton Roads (HR) area. An aggregate analysis of the key determinants affecting the evacuation decision is presented. Unlike related literature, we take on a unique approach by distinguishing between two types of evacuations: intra-regional, in which evacuees leave their homes but stay within the HR region, and inter-regional, in which evacuees leave the HR region. Our analysis revealed a clear distinction between these two types of evacuees, which signifies the importance of examining these two types of evacuations separately.

Traffic incidents occur frequently on urban roadways and cause incident-induced congestion. Predicting incident duration is a key step in managing these events. Ordinary least squares (OLS) regression models can be estimated to relate the mean of incident duration data with its correlates. Because of the presence of larger incidents, duration distributions are often right-skewed; that is, the OLS model underpredicts the durations of larger incidents. Therefore, this study applies a modeling technique known as quantile regression to predict more accurately the skewed distribution of incident durations. Quantile regression estimates the relationships between correlates and a chosen percentile—for example, the 75th or 95th percentile—while the OLS regression is based on the mean of incident duration. With the use of incident data related to more than 85,000 (2013 to 2015) incidents for highways in the Hampton Roads area of Virginia, quantile regression results indicate that the magnitudes of parameters and predictions can be quite different compared with OLS regression. In addition to predicting durations of larger incidents more accurately, quantile regressions can estimate the probability of an incident lasting for a specific duration; for example, incidents involving congestion and delay have an approximately 25% chance of lasting more than 100.8 min, while incidents excluding congestion and delay are estimated to have a 25% chance of lasting more than 43.3 min. Such information is helpful in accurately predicting durations and developing potential applications for using quantile regressions for better traffic incident management.

Despite the non-trivial number of (non-institutionalized) residents with special needs - referred to as the medically fragile population in this paper - there is virtually no research available to guide the hurricane evacuation planning for this population group. One area is their evacuation time choice behavior that can have tremendous implications for their safety as well as the logistics of the evacuation process. In this paper, we fundamentally advance our understanding of the evacuation timing behavior of this heavily understudied, and potentially vulnerable, population group. Analysis indicates that key differences exist between the medically fragile and non-medically fragile population. Language: en

The problem of optimal container vessels deployment is one of great significance for the liner shipping industry. Although the pioneering work on this problem dates back to the early 1990s, only until recently have researchers started to acknowledge and account for the significant amount of uncertainty present in shipping demand in real world container shipping. In this paper, new analytical results are presented to further relax the input requirements for this problem. Specifically, only the mean and variance of the maximum shipping demand are required to be known. An optional symmetry assumption is shown to further reduce the feasible region and deployment cost for typical confidence levels. Moreover, unlike previous work that tends to ignore stochastic dependencies between the shipping demands on the various routes (that are known to exist in the real world), our models account for such dependencies in the most general setting to date. A salient feature of our modeling approach is that the exact dependence structure does not need to be specified, something that is hard, if not simply impossible, to determine in practice. A numerical case study is provided to illustrate the proposed models.

This paper provides a novel and unique methodology for evaluating the effectiveness performance of a port’s individual services by utilizing the concept of a port service chain – a service network utilized by a port’s service providers in the provision of the port’s services that accounts for the quality-of-service relationships among the services. If such relationships are ignored, the resource allocations by the port’s service providers to improve the quality of their port services will either over- or underestimate the amount of resources needed. A cooperative port service chain will always (under certain conditions) be more effective than a non-cooperative port service chain.

Despite the widely recognized importance of evacuation planning for residents with special needs – in this paper referred to as the medically fragile population – there is virtually no research available to guide such planning, as opposed to the numerous empirical research studies on the evacuation behavior of the general population. In this paper, we provide these long-overdue insights using data from a large-scale phone survey (over 7000 samples) conducted in the aftermath of hurricane Irene in the Hampton Roads region in Virginia. Via aggregate and disaggregate analyses, we start to unravel the behavior of this heavily understudied, and potentially vulnerable population group. Special emphasis will be placed on the differences between the medically fragile and non-medically fragile population. Two alternative definitions for what constitutes medically fragile are examined in this paper. Using the broader definition, it was found that a key difference between these two groups relates to the importance of having a strong network of family members in the area. When considering a more narrow definition, we found that being a single parent household, likelihood of neighborhood flooding and knowing most of the names of one’s neighbors have significantly different impacts on the two population groups.