Search

This study explores the optimal subsidy policy to maximize the benefits associated with the suburbanization of university campuses. A transport accessibility index is introduced, and a model is developed to analyze faculty housing relocation, incorporating factors such as transport accessibility, housing price, relocation subsidy, and the influence of children. The impact of housing relocation is assessed using a regional output model that considers both production and consumption aspects. Subsequently, a decision-making model is established to determine the optimal subsidy level and the number of faculty to relocate, with the overarching goal of maximizing total regional benefits. The findings reveal that an increase in subsidies correlates with a rise in the willingness of faculty to relocate, leading to heightened benefits for the region. However, the rate of benefit increase shows diminishing returns with each increment change in the subsidy. Notably, the study demonstrates that 70% of the additional benefits to the region emanate from the housing market, accurately reflecting the current financial landscape in China. This insight underscores why city governments frequently leverage land markets to actively promote suburbanization.

In response to the evolving challenges of the integration and combination of multiple container terminal operations under berth water depth constraints, the multi-terminal dynamic and continuous berth allocation problem emerges as a critical issue. Based on computational logistics, the MDC-BAP is formulated to be a unique variant of the classical resource-constrained project scheduling problem, and modeled as a mixed-integer programming model. The modeling objective is to minimize the total dwelling time of linerships in ports. To address this, a Dueling Double DQN-based reinforcement learning algorithm is designed for the multi-terminal dynamic and continuous berth allocation problem A series of computational experiments are executed to validate the algorithm’s effectiveness and its aptitude for multiple terminal joint operation. Specifically, the Dueling Double DQN algorithm boosts the average solution quality by nearly 3.7%, compared to the classical algorithm such as Proximal Policy Optimization, Deep Q Net and Dueling Deep Q Net also have better results in terms of solution quality when benchmarked against the commercial solver CPLEX. Moreover, the performance advantage escalates as the number of ships increases. In addition, the approach enhances the service level at the terminals and slashes operation costs. On the whole, the Dueling Double DQN algorithm shows marked superiority in tackling complicated and large-scale scheduling problems, and provides an efficient, practical solution to MDC-BAP for port operators.

A new working mode of overloaded traffic control for rural highways is presented, and a location-routing model is built to optimize the check base distribution and the control vehicles’ routing schemes. Then, for the location-routing model with a large set of location alternatives and an unknown settable number of check bases, a multiple ant colony optimization algorithm is designed to solve the model. Furthermore, actual data from Guiyang rural highways are used to perform a numerical analysis. The results indicate that the model can be used to obtain the optimal base location-vehicle routing scheme to verify the feasibility of the model and the algorithm. The model and algorithm can help managers to make decisions on locating the check bases and routing the control vehicles.

To clarify the impact of new transport services on consumers’ shopping behaviors and shopping-related transport emissions, a back-propagation neural network shopping channel choice model is established to estimate the number of times that consumers engage in online and offline shopping. A brick-and-mortar store choice model and travel mode choice model are developed, and a method to measure the quality of life of consumers is established to evaluate the impact of new transport services on shopping behaviors and the corresponding shopping transport emissions. The findings reveal that a new passenger transport service increases the number of times that consumers shop in brick-and-mortar stores and correspondingly shopping transport emissions; a new commodity transport service reduces the number of times that consumers shop in brick-and-mortar stores and in turn shopping transport emissions. In a scenario with both new commodity services and new passenger transport services, although online shopping is convenient, consumers are still willing to pay travel expenses for offline shopping; in a scenario with the new commodity transport service but without the new passenger transport service, the emissions from shopping-related transport are the lowest.

To improve the space utilization and traffic capacity of a signalized junction, this paper proposes a dynamic reversible lane assignment method for approaches of signalized junctions that consider the game equilibrium between road users and traffic controllers. To theoretically analyze the behaviors of the players involved in the leader-follower strategic game, a bi-level programming model is established. To minimize the total queue length of approaches at signalized junctions, the upper model dynamically optimizes the reversible lane assignments and can be solved with the enumeration method or the Monte Carlo algorithm. The lower model outputs the traffic assignment at road sections using a Logit-based stochastic user equilibrium model that is solved by the method of successive averages. The general impedance of road section in lower model consists of the road travel time cost and the intra-time cost at the signalized junction connected with current road section. In addition, the interaction between the two levels is simulated in an iterative optimization procedure. Finally, this paper uses two numerical experiments to validate the proposed approach.

Express coach (EC) lost a considerable share of passengers after high-speed rail (HSR) was implemented. This paper proposes a door-to-door operation mode for the EC system and builds a model to design an EC trip-end network in the origin city with the aim of maximizing the EC’s daily operating profit. A case study is undertaken, and the results show that the operating profit of the EC system first increases and then decreases with the growth of the trip-end routes. In the HSR era, door-to-door operation can effectively guarantee the market share and operating profits of the EC.

This paper proposes a bilevel programming model to optimize the parking supply in a central business district (CBD) considering the interaction between car traveling and car parking. The upper model (UM) determines the number and locations of parking lots in a CBD with the objective of minimizing the average impedance of all car trips. The lower model (LM) is a modal split and assignment combination model for calculating the traffic flow under various parking supply schemes. In addition to the bilevel model, a gravity model (GM) is proposed to calculate the car trips that are induced by the added parking lots. The interaction between car traveling and parking can be simulated by the feedbacks between the UM and LM. A case study is performed with real data from Dalian City. The results show that there is a negative correlation between parking supply increments and the average traveling impedance when the number of parking spaces is lower than the optimal value; however, the average traveling impedance will start to increase with the increase in parking supply when the number of parking spaces is higher than the optimal value.

The paper analyzes the current state of the Chinese cruise market and presents the idea of building a business model of coastal cruising. The cruise demand of middle-income families, which includes the desired travel days, ports of call, is surveyed. The data of the previous non-cruise travels and the data of future cruises of middle-income families are used to develop a model designed to identify the maximum passenger volume with minimum operating costs while taking cruise itineraries and schedules into account. A matrix coding genetic algorithm was designed to solve the model. The case study found that a voyage of 4.79 days results in equilibrium, that the annual demand is 200,840 passengers, and that the daily voyage cost is 0.843 million Yuan.

This paper designs the timetable for the airport coach which is a new access service provided by the airport with the purpose of attracting more passengers. Firstly, the ‘time–space’ network is constructed for analyzing the formation of the passengers’ trip chain; Secondly, the timetable design model is built with the aim to minimize the unit operation cost per transported passenger of the airport coach and a genetic algorithm with matrix coding is used for solving the model. In the model, the coach departure time and the flight schedule are connected with each other; the quality of the coach service and the passenger’s demand are both considered. Finally, the coach of Dalian airport in China is taken as an example to test the proposed method. Through solving the model and sensitivity analysis, we obtain a coach timetable for Dalian airport. The results show that the proposed model can provide a practical method to design the timetable for the airport coaches with ‘hub-and-spoke’ network. First published online 28 January 2015

A Synchronous Optimization for Multiship Shuttle Tanker Fleet Design and Scheduling is solved in the context of development of floating production storage and offloading device (FPSO). In this paper, the shuttle tanker fleet scheduling problem is considered as a vehicle routing problem with hard time window constraints. A mixed integer programming model aiming at minimizing total transportation cost is proposed to model this problem. To solve this model, we propose an exact algorithm based on the column generation and perform numerical experiments. The experiment results show that the proposed model and algorithm can effectively solve the problem.

Fragile X syndrome (FXS) is an inheritable neuropsychological disease caused by silence of the fmr1 gene and the deficiency of Fragile X mental retardation protein (FMRP). Patients present neuronal alterations that lead to severe intellectual disability and altered sleep rhythms. However, the neural circuit mechanisms underlying FXS remain unclear. Previous studies have suggested that metabolic glutamate and gamma-aminobutyric acid (GABA) receptors/circuits are two counter-balanced factors involved in FXS pathophysiology. More and more studies demonstrated that attenuated GABAergic circuits in the absence of FMRP are critical for abnormal progression of FXS. Here, we reviewed the changes of GABA neural circuits that were attributed to intellectual-deficient FXS, from several aspects including deregulated GABA metabolism, decreased expressions of GABA receptor subunits, and impaired GABAergic neural circuits. Furthermore, the activities of GABA neural circuits are modulated by circadian rhythm of FMRP metabolism and reviewed the abnormal condition of FXS mice or patients.

A multidepot VRP is solved in the context of total urban traffic equilibrium. Under the total traffic equilibrium, the multidepot VRP is changed to GDAP (the problem of Grouping Customers + Estimating OD Traffic + Assigning traffic) and bilevel programming is used to model the problem, where the upper model determines the customers that each truck visits and adds the trucks’ trips to the initial OD (Origin/Destination) trips, and the lower model assigns the OD trips to road network. Feedback between upper model and lower model is iterated through OD trips; thus total traffic equilibrium can be simulated.

Double cycling is an efficient tool to increase the efficiency of quay crane (QC) in container terminals. In this paper, an optimization model for double cycling is developed to optimize the operation sequence of multiple QCs. The objective is to minimize the makespan of the ship handling operation considering the ship balance constraint. To solve the model, an algorithm based on Lagrangian relaxation is designed. Finally, we compare the efficiency of the Lagrangian relaxation based heuristic with the branch-and-bound method and a genetic algorithm using instances of different sizes. The results of numerical experiments indicate that the proposed model can effectively reduce the unloading and loading times of QCs. The effects of the ship balance constraint are more notable when the number of QCs is high.

The capacity of the ship-lock at the Three Gorges Dam has become bottleneck of waterway transport and caused serious congestion. In this article, a continual network design model is established to solve the problem with minimizing the transport cost and environmental emission as well as infrastructure construction cost. In this bi-level model, the upper model gives the schemes of ship-lock expansion or construction of pass-dam highway. The lower model assigns the containers in the multi-mode network and calculates the transport cost, environmental emission, and construction investment. The solution algorithm to the model is proposed. In the numerical study, scenario analyses are done to evaluate the schemes and determine the optimal one in the context of different traffic demands. The result shows that expanding the ship-lock is better than constructing pass-dam highway.

In order to solve the difficulty in simulation and prediction the evolution of oil spill incidents in port water area caused by system complexity and environment variety, a simulation model based on Dynamic Data-Driven Application System (DDDAS) was developed. By assimilating real-time data, simulation model can update its initial condition and solutions, and modify its parameters. Firstly, the framework for DDDAS-based simulation model was designed. Then the method for mapping of real-time data to simulation model, the method for recovery of initial data for DDDAS, and the algorithms to simulate the behavior of oil spill were studied. Finally, numerical tests were provided to illustrate the validity of the proposed model. Results indicate that the DDDAS-based simulation method can improve the prediction accuracy of oil spill incidents. First published online: 16 Oct 2013

The objective of operation scheduling in container terminals is to determine a schedule that minimizes time for loading or unloading a given set of containers. This paper presents a method integrating reinforcement learning and simulation to optimize operation scheduling in container terminals. The introduced method uses a simulation model to construct the system environment while the Q-learning algorithm (reinforcement learning algorithm) is applied to learn optimal dispatching rules for different equipment (e.g. yard cranes, yard trailers). The optimal scheduling scheme is obtained by the interaction of the Q-learning algorithm and simulation environment. To evaluate the effectiveness of the proposed method, a lower bound is calculated considering the characteristics of the scheduling problem in container terminals. Finally, numerical experiments are provided to illustrate the validity of the proposed method.

The expansion of high-speed railway (HSR) has opened up new freight capacity on traditional railway (TR) lines in China. In order to increase China’s railway volume of containers transported to port terminals, this paper estimates the HSR and TR modal splits for passenger transport. Then, the TR capacity used for passengers is calculated via traffic assignment model, and the number of passenger trains on TR lines is estimated and then deducted from total TR capacity. The remaining balance gives the freight capacity of a TR line. Moreover, a bi-level programming model is built to optimise the location of railway freight terminals and the corresponding train dispatching frequencies in a case in which trains on TR lines and trucks on highways are both used to transport containers to port terminals. The ports of Shanghai and Ningbo with their hinterlands are used as a case study for numerical analysis. [ABSTRACT FROM AUTHOR]