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This paper investigates an integrated quay crane (QC)–shuttle vehicle (SV) scheduling problem (SP) with capacitated apron buffer, which is subject to practical constraints on non-zero travelling time, precedence relationships between container-handling jobs, QC non-interference, safety margin, blocking and apron buffer capacity. The objective is to minimise the makespan of vessel handling by simultaneous optimisation of bay-to-QC assignment, the sequences of bays handled by QCs, the sequences of jobs handled by QCs, job-to-SV assignment and the sequences of jobs handled by SVs. To achieve this, a mixed-integer linear programming model is first formulated to solve the SP. The relationships of decision variables and model constraints are then analysed to generate two sub-models: assignment based and sequence based. The former sub-model is developed from the proposed model by pre-determining the assignment of bay-to-QC and job-to-SV, and the latter is developed from the proposed model by pre-determining the sequences of jobs handled by each QC and each SV. Hybrid genetic algorithms with the two sub-models are proposed for numerical experiments. Practical implications of this investigation for apron buffer zone design are discussed.

This paper investigates the dynamic recovery policies for liner shipping services with the consideration of buffer time allocation and uncertainties. The aim of this study was to allocate the buffer time at the tactical level and then determine optimal options of actions, including speed optimisation strategy, port skipping and acceleration rate choice, for recovering from disruptions due to various uncertainties or random adverse events, which cause vessel delays. To achieve this, an attempt was made to obtain the optimal balance among economic, environmental and service reliability objectives. A novel mathematical formulation is introduced to solve the robust vessel scheduling problem with short- and long-term decisions. Furthermore, two heuristics were tested to solve the proposed model. Experiments on a container liner shipping service showed the validity of the model and a set of managerial insights were gained from them.

This paper proposes a novel model for optimizing limited-stop bus corridor services with consideration of varied payment modes and different trip purposes. In the proposed model, the bus dwell time at a stop is dependent on the fare payment modes and the number of passengers getting off and waiting at the stop while those with the similar trip purpose are grouped into one user class. Given an origin–destination (OD) passenger trip matrix and a set of candidate bus lines serving a corridor, the proposed model is to minimize the total social cost that consists of the cost to the bus operator and the cost to the passengers. In the formulation of the optimization problem, a weighting parameter is adopted to balance the operator cost and the passenger cost. Numerical examples are presented to illustrate the importance of considering passenger flow impacts on bus (and passenger) travel times in the proposed model. We also investigate effects on the optimal limited-stop services (e.g., short-turn, skip-stop, and express) taking into account the choice of fare payment modes (e.g., on-board fare collection including payment by cash, magnetic strip or smart card, off-board fare collection) and different values of travel time due to passenger trip purposes. It is shown that the off-board payment mode would be more efficient in a high-demand corridor, that more passengers prefer to express and skip-stop services rather than normal regular services in the four collection systems, and that different limited-stop service plans should be used for different periods of the day in response to temporal variation in OD passenger travel patterns. The intellectual merit of this paper is not the seemingly obvious conclusions but that the proposed model can handle the problem of limited-stop bus corridor service design with the consideration of fare payment mode choice and trip purposes.

This paper adopts a simple but valid value-at-risk (VaR) approach to measuring the risk in the dry bulk shipping market by means of Baltic Dry Index (BDI). To have a better understanding of the ris...

We model the vertical investment of a container shipping line in the port capacity in a two-port system. Our analytical and numerical analyss suggests that the relative scale of the capacity invest...

The growing impact of climate change on port infrastructure and facilities currently may lead to a rising frequency of such serious natural disasters or accidents as rising sea level, increasing extreme weather, increasing intensity of tropical storms and typhoons, rising wave height surmounting breakwater design levels, heavy rain exceeding quay well drainage capacity, rising ocean temperatures, and deteriorating harbor water quality. The paper aims to apply a risk management matrix approach to vulnerability assessment and identification of desirable adaptation strategies. The data for this investigation is first collected by means of questionnaire survey of experts in shipping companies or port management offices at the Kaohsiung port. Then, a vulnerability analysis of port infrastructure and facilities is carried out under the impact of severe typhoons. Subsequently, adaptation strategies are identified for infrastructure and facilities at the Kaohsiung port to address the adverse impacts of climate change. One intellectual merit of this paper is that ten vulnerability assessment criteria are proposed for addressing the impacts of climate change on port infrastructure and facilities. Second, both ground and access roads in the port area, and drainage facilities in flooding-prone areas are shown to be damaged in the moderate risk areas potentially impacted by typhoons. The third merit is that a set of adaptation strategies has been identified for the moderate risk areas impacted by serious typhoons.

In this paper, we study the timing decision of airlines’ investments in exclusive airport facilities in the presence of demand ambiguity and competition. We model the investment decision as a real options problem under ambiguity. An airline's ambiguity-averse preferences are modeled using the multiple prior expected utility form. We obtain the optimal investment timing rule for the airline and find that it requires the airline's expected present value of its future incremental profit from investing to exceed the investment cost by the option value multiplier. We then compare the airline's optimal investment timing rule to the social optimum and discuss two possible subsidy policies, a lump-sum subsidy and a per-unit subsidy, with which the government can align the airline's investment timing rule with the social optimum. We characterize the stepwise structure of both subsidy policies, in which the optimal time to invest in an exclusive terminal depends on three thresholds: the social optimum, the airline's break-even point, and a combination of the social optimum and the airline optimum. We conclude that the two subsidy policies have equivalent effects when the government and the airline have the same ambiguity levels, as they lead to the same investment timing and require the same amount of government funds.

This paper investigates the effects of demand information sharing on concession arrangements and market equilibria, when two ports, each managed by a welfare-maximizing port authority and a profit-maximizing port operator, compete for demands. The problem is formulated as a multi-stage game, in which the authority and the operator at each port first decide whether to share demand information and make concession arrangements; then, the port operators compete a la Cournot. Alternative scenarios are compared to identify the effects of information sharing and market structure. Our analytical results identify the conditions under which demand information sharing is beneficial in port concession arrangements and highlight the importance of the underlying market structure and congestion levels in achieving these benefits. Specifically, we show that information sharing is a source of welfare improvement, and the effects are more significant when the positive externality of information sharing on welfare is large, inter-port competition is strong, and port congestion is costly. However, with no compensation, the port operators have no incentive to share their private information because otherwise, this is likely to increase concession unit-fees, limit their ability to compete effectively with each other, and ultimately reduce their expected profits. Therefore, transfer payments are necessary to encourage information sharing. With this arrangement and the assumed symmetric cost and service structure, we show that a port operator prefers sharing information if the externality of information sharing on welfare exceeds a threshold. Furthermore, when this externality is sufficiently large, the operators at both ports benefit from sharing information. Finally, when the two ports compete in price, we show that a port operator's single-side information sharing may not always benefit its port authority.

With the continuous increase in labour costs and the demands of the supply chain, improving the efficiency of automated container terminals has been a key factor in the development of ports. Automated guided vehicles (AGVs) are the main means of horizontal transport in such terminals, and problems in relation to their use such as vehicle conflict, congestion and waiting times have become very serious, greatly reducing the operating efficiency of the terminals. In this article, we model the minimum driving distance of AGVs that transport containers between quay cranes (QCs) and yard cranes (YCs). AGVs are able to choose the optimal path from pre-planned paths by testing the overlap rate and the conflict time. To achieve conflict-free AGV path planning, a priority-based speed control strategy is used in conjunction with the Dijkstra depth-first search algorithm to solve the model. The simulation experiments show that this model can effectively reduce the probability of AGVs coming into conflict, reduce the time QCs and YCs have to wait for their next task and improve the operational efficiency of AGV horizontal transportation in automated container terminals.

As a shared transport mode, public transit is important for leading transportation systems to develop in a sustainable manner. It is well known that the passenger demand along a bus line fluctuates...

A public transit system with multiple fixed bus lines faces non-uniform fluctuating passenger demand, both spatial and temporal. This non-uniformity warrants the use of public transit operational strategies to achieve efficiency. This study proposes a methodology for optimizing the operational integration of multiple bus lines to address the spatial non-uniformity of passenger demand by applying five operational strategies: full-route operation, short turn, limited stop, deadheading, and a mixture of either two or three of the latter three strategies. The operational strategies to be developed improve the efficiency of bus lines and accommodate the observed passenger demand in the most favorable manner, that is, through the consideration of passengers’ preferences with the objective of the minimum resulting cost. The methodology is first applied to a sample problem, and then to a real-life case study of multiple bus lines in Dalian, China. The results obtained demonstrate that the effectiveness of combined strategies is higher than that of any single strategy. In the real-life bus line case, a combination of strategies without considering deadheading trips saves four vehicles in comparison with the full-route operation scenario. The anticipated number of vehicles is further reduced by three by the introduction of the deadheading trip strategy, resulting in greater public transit system efficiency.

This paper aims to optimize fares and transfer discounts for public transit service along a bus-subway corridor with the consideration of effects of uncertainty in travel times and difference in stop spacing between bus and subway services on passenger behavior. The former factor is captured by the reserved time in travel cost and the latter one produces some passenger Origin–Destination (O–D) pairs along the corridor that can not be served by one mode only. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower-level capturing traveler choice behavior is a variable-demand Stochastic User Equilibrium (SUE) assignment model. A Genetic Algorithm (GA) is applied to solve the bi-level program while the Method of Successive Averages (MSA) is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the model and solution method. Numerical results indicate that the implementation of transfer discounts may be of great benefit to the social welfare and that the uncertainty in travel time and the difference in stop spacing play an important role in determining optimal fares and transfer discounts for the service along a bus-subway corridor.

This special issue is also prepared to celebrate the 110th Anniversary of Shanghai Maritime University.This special issue is attributable to the Second World Transport Convention, which took place ...

Enhancing the accuracy of freight rate forecasting is crucial when agents in the shipping industry make their business decisions and strive to avoid or reduce possible risks. Although there has been a lot of freight rate trend analysis in the literature, the time-varying and volatile nature of the shipping market makes the accurate prediction of it extremely difficult, if not impossible. In this research, we first propose the use of a dynamic fluctuation network (DFN) to transform a time series data set into an evolving directed network, which removes the noise in the data and allows us to extract time-varying features in it. We then develop a hybrid approach that combines the DFN and artificial intelligence (AI) techniques to forecast the Baltic Dry Index (BDI), which is new to the literature. The utilization of DFN with AI enables the non-linear, cyclical and dynamic features of the BDI to be extracted effectively and the prediction accuracy is not impacted by the length and time-scale of sample selection either in long-term or short-term forecasting. These advantages of DFN offset the well-known limitations of traditional AI-based algorithms and econometric models in BDI forecasting. The empirical results from applying the resultant model to multi-time-scale datasets in a random sampling case show that the model is more accurate than the model based on the AI technique only. We test the accuracy of the DFN-AI model in three challenging cases which respectively contain a sudden rise, a decline, or frequent fluctuations of the BDI. The DFN-AI model has fewer errors and a higher trend matching rate than the corresponding AI-based model. Our hybrid approach also shows its superiority in working with data containing an extreme market downturn, which shed light on the predictability of BDI. This study has important implications for overall business, commercial, and hedging strategies in the shipping industry.

To improve the security and efficiency of terminal operations, a key issue for land area layout at a foreign trade container terminal is to plan and design an inspection area. Since it directly aff...

Limited-stop bus services are a highly efficient way to release more potential of the public transit system to meet travel demand, especially under constraints on vehicle fleet size and transportation infrastructure. This work first proposes a visualized fare table for the design of limited-stop bus services along a public transit corridor, along which many lines of public transit carry a heavy load of demand back and forth every working day. Based on this proposed fare table, a set of fare strategies and desired aims of fare policy, a differentiated fare structure is established to improve social equity and increase revenue. The nature of the structure can help travellers understand how to be charged between their origins and destinations (e.g. flat, time-based, stop-based or quality-based pricing) and then plan their trips efficiently. Secondly, a model is formulated to minimize the total social cost in designing a fixed demand limited-stop bus service system with a differentiated fare structure. Thirdly, numerical results are carried out with sensitivity analysis within three scenarios of differentiated fare structures. It is found that a differentiated fare structure has a great effect on passenger path choice behaviour and resulting optimal design of bus services. An attractive feature of this differentiated fare structure is that it could not only enhance the operator’s revenue and social equity but also reduce passenger transfers and social cost.

This study develops a theoretical model to analyze the effects of two types of voluntary carbon offset schemes, particularly airlines’ incentives to collude on offset purchases as well as the potential mechanisms and corresponding market/welfare implications of such alliance. The modeling results suggest that in a Chicago Climate Exchange (CCX)-style market, airlines do not have the incentive to cooperate in offset purchases. However, in an over-the-counter (OTC) market, airlines are willing to form alliance provided that the alliance's bargaining power is sufficiently stronger than the airlines’ independent bargaining power. With coordinated output decision, the alliance increases social welfare when the marginal environmental cost of aviation is high. If the alliance partners do not coordinate their output decision, we show that an internal bargaining mechanism within the alliance can reduce the offset price and increase the alliance's profit compared with the case of output-coordinated alliance. Without such internal mechanism, the alliances’ total profits and the social welfare may decrease. In addition, we extend our models to consider the alliance synergy effect in reducing the alliance members’ marginal operating costs.

Over the years many shipping lines have established terminal operation companies, with some set up as independent firms. However, port authorities and local governments have not always welc...

Vehicle scheduling is an important component of the transit operations planning process because it is directly related to the number of vehicles required for covering all trips. However, in practice, vehicles are not scheduled by a very efficient approach, related to the fact that all vehicles use one type of operation strategy—full operation strategy—for meeting unbalanced demand patterns. This paper proposes a methodology to reduce the required number of vehicles by adjusting a few trips to operate by other strategies, including limited stop, short turn, deadheading, and mixed strategies. This proposed methodology is based on a graphical human–machine interactive technique, called deficit function (DF). The DF allows schedulers to select a variable trip schedule for reducing the number of vehicles using optimal determination of the best applied strategies. The process of adjusting variable trips to accommodate different strategies may result in passenger travel time changes. Two optimization models are developed to minimize these changes and determine a set of stops served by the variable trip schedule. This proposed methodology has been applied to a detailed example and a case study in Paris, France. The results show that the adjustment of a few trips to use certain strategies can reduce the number of buses required by three.

This paper proposes an optimal urban expressway system model with consideration to the interaction equilibrium between transportation and land use. In the suggested model, three kinds of economic a...

A hazmat transportation network design model involving two decision levels, the network regulator and the carrier, is proposed. The regulator identifies road segments that should be opened to hazma...

With the occurrence of more frequent and intense climate change events, transportation systems, including their infrastructure and operations become increasingly vulnerable. However, the existing research related to climate risks, adaptation and planning in the transport sector is still at an embryonic stage. Understanding such, this paper presents a critical review on climate risks, adaptation strategies and planning in the context of road and rail transportation systems. It aims to conduct a rigorous survey, to highlight any significant research gaps not addressed in past studies and to analyse current emerging topics to guide future directions. It critically dissects the selected papers by categorising them into several dimensions to reveal the status quo and potential challenges, including climate risk assessment, transport asset management, climate planning and policy, and adaptation of transport infrastructure to climate change. It will provide valuable references for future research and constructive insights and empirical guidance on climate adaptation, risk analysis, transport planning and other important relevant topics.

This chapter aims to compare the competitiveness of existing and potential corridors for cargo transport between the Indian Ocean region and China in the “Belt and Road Initiative” context. To achieve this aim, a multiobjective programming model is proposed to allocate cargoes to each corridor while the four objectives of minimum transport cost, minimum energy consumption, minimum greenhouse emissions, and highest safety are balanced. Three types of cargoes are taken as examples. The proposed model gives the optimal cargo volume allocation over the four corridors of interest, which has been shown numerically to vary as the priority of each objective varies. Subsequently, the rate of return on investment in the three potential corridors is analyzed. This work may not only provide good suggestions for policy makers, but also promote the implementation of the Belt and Road Initiative.

Existing two-phase signal optimization models neglect the unyielding behavior of illegal permissive left-turners at intersections in many developing countries. At permitted left-turn phase warrante...

Quay cranes (QCs) play a significant role in CO2 emission of the operations of global supply chains, since huge global container traffic requires a considerable number of handing operations of QCs. The traditional studies of QC assignment mainly aim to investigate the scheduling efficiency of QCs and seldom touch on CO2 emission or other green transportation objectives and the resulting models are normally mixed-integer programming (MIP) problems and solved by heuristic algorithms. In this paper, a convex mathematical programming model is proposed for the QC assignment problem, in which the queueing theory is used to model the queueing behavior of automatic guided vehicles (AGVs). The objective of the proposed model is to minimize CO2 emission during an unloading process of containers from QCs to AGVs by optimizing the number of QCs. The analytical solution of the number of QCs is obtained to the proposed model. It is found that the optimal number of QCs increases with the expected arrival rate of AGVs and the mean fuel consumption per AGV per hour but it decreases with the mean queue service rate of QCs and the electricity consumption per QC per hour.

The Northern Sea Route (NSR) has tremendous potential for ocean shipping between Europe and Asia due to the savings from shorter transit time and distance. However, the Arctic area is environmental...

usc Refereed/Peer-reviewed The relationship between container location dispersion for a single loading and the efficiency of quay cranes (QCs) was investigated. For this purpose, a discrete-event-based simulation model was developed to capture the container handling process at a transshipment container terminal. A series of experiments were conducted to illustrate the effects of container location dispersion on the long-term performance of QCs and container allocation. These experiments showed that the container location dispersion has a significant effect on the distribution of loading containers. An optimal choice of container location dispersions can improve the efficiency of the QC and help to manage uncertainty at a lower cost. It is suggested that yard managers consider the workload of each berth when deciding on the location dispersion for a loading service. In addition, it was found that optimal container allocation dispersion reduces the congestion caused by time window conflicts and insufficient available equipment.