A simulation-based vessel-truck coordination strategy for lighterage terminals

Lighterage contributes significantly to the maritime industry since thousand tons of necessary supplies have to be delivered to mother vessels anchored near sea every day. A single lighter waits to receive cargoes from multiple suppliers at the berth, while the trucks wait for their turns to enter the terminal. Due to the limited space, the terminal operation becomes vulnerable to the lack of coordination of their arrivals. In this study, despite a traditional industry, we are interested in applying the emerging technologies to improve the operation efficiency. We develop a simulation-based coordination strategy to construct coordinated schedules for both the lighters and suppliers to reduce congestions in the lighterage terminal The discrete event simulation model is developed based on understanding the real-world terminal in Singapore, and the controlled arrival method, determining coordinated arrival schedules of trucks, is introduced and embedded to the simulation model. The simulation model is tuned up with a benchmark setting of 6-month historical data. To find the optimal strategy, an advanced bi-objective simulation optimization algorithm is employed. According to our findings, the proposed strategy could significantly improve the efficiency of both lighters and trucks in various indicators. At the end, a mobile application prototype is proposed to deliver the coordinated schedules to different parties, and improve the communication between parties.     

Strategic trade-off between vessel delay and schedule recovery: an empirical analysis of container liner shipping

In this paper, we empirically test the relationship between the delay of containerships and the scheduled operations in a terminal, based on a dataset containing information on 352 containership arrivals during a 9-month period at seven terminals of three North American ports. We find that a vessel is less likely to be delayed when there are more operations scheduled shortly (up to 3 days) after the vessel’s berthing window in the terminal. Moreover, we also find that the more containers a vessel needs to unload in the terminal, the less likely that it would be delayed. Both findings support the hypothesis that liners strategically balance the trade-off between delay cost and schedule recovery cost.     

A transit ridership‐revenue model

The paper outlines the theoretical underpinnings of an urban mass transit revenue and ridership model designed to provide medium term forecasts of future trends in situations of data sparsity. The specific example laid out in the paper relates to the Greater Vancouver Regional District but the framework is of general applicability. Much of the informational input at the initial stage is of a general kind and details of the specific transit system and local area are of the sort which should be readily available to most urban authorities. The model developed is designed for use on a desk‐top micro‐computer and offers an inter‐active method of forecasting. The operator has the facility to both consider fare policy sensitivity and review alternative scenarios about future trends in exogenous factors. A selection of forecasts developed for the GVRD is provided to reveal the main features of the approach.     

A Hybrid Heuristics for Irregular Flight Recovery

Adverse weather conditions, congestion at airports, and mechanical failures often disrupt regular flight schedules. The irregular flight recovery problem aims to recover these schedules through reassignments of flights and cancellations. In this article, we develop the classic resource assignment model for the irregular flight recovery problem, and a new hybrid heuristic procedure based on greedy random adaptive search procedure (GRASP) and simulated annealing algorithm is presented to solve this problem. As compared with the original GRASP method, the proposed algorithm demonstrates quite a high global optimization capability. Computational experiments on large-scale problems show that the proposed procedure is able to generate feasible revised flight schedules of good quality in less than five seconds.     

Efficient Transit Schedule Design of timing points: A comparison of Ant Colony and Genetic Algorithms

This work defines Transit Schedule Design (TSD) as an optimization problem to construct the transit schedule with the decision variables of the location of timing points and the amount of slack time associated with each timing point. Two heuristic procedures, Ant Colony and Genetic Algorithms, are developed for constructing optimal schedules for a fixed bus route. The paper presents a comparison of the fundamental features of the two algorithms. They are then calibrated based on data generated from micro-simulation of a bus route in Melbourne, Australia, to give rise to (near) optimal schedule designs. The algorithms are compared in terms of their accuracy and efficiency in providing the minimum cost solution. Although both procedures prove the ability to find the optimal solution, the Ant Colony procedure demonstrates a higher efficiency by evaluating less schedule designs to arrive at a ‘good’ solution. Potential benefits of the developed algorithms in bus route planning are also discussed.     

Airline competition and market frequency: A comparison of the s-curve and schedule delay models

We compare two common ways of incorporating service frequency into models of airline competition. One is based on the so called s-curve, in which, all else equal, market shares are determined by frequency shares. The other is based on schedule delay—the time difference between when travelers wish to travel and when flights are available. We develop competition models that differ only with regard to which of the above approaches is used to capture the effect of frequency. The demand side of both models is an approximation of a nested logit model which yields endogenous travel demand by including not traveling in the choice set. We find symmetric competitive equilibrium for both models analytically, and compare their predictions concerning market frequency with empirical evidence. In contrast to the s-curve model, the schedule delay model depicts a more plausible relationship between market share and frequency share and accurately predicts observed patterns of supply side behavior. Moreover, the predictions from both models are largely the same if we employ numerical versions of the model that capture real-world aspects of competition. We also find that, for either model, the relationship between airline frequency and market traffic is the same whether frequency is determined by competitive equilibrium, social optimality, or social optimality with a break-even constraint.     

How to mix per-flight and per-passenger based airport charges

This paper investigates the questions of why carriers advocate for higher per-passenger airport charges and lower per-flight charges, and whether and when this proposal is welfare-enhancing. Specifically, the paper compares the optimal mix of per-flight and per-passenger based airport charges from both a monopoly carriers’ and the social viewpoints conditional on airport cost recovery. It focuses on the trade-off between price and frequency (i.e., schedule delays) when time valuations are uniform, or differ, between business and leisure passengers. We identify an easy test for the evaluation of the mix of per-passenger and per-flight based airport charges by policy makers, which is simply to check whether the carrier’s preferred per-flight charge is zero. Our analysis suggests that there is no need for immediate regulatory corrections of the current trend towards the strong use of per-passenger based airport charges.     

Flight schedule punctuality control and management: a stochastic approach

The insufficiency of infrastructure capacity in an air transport system is usually blamed for poor punctuality performance when implementing flight schedules. However, investigations have revealed that ground operations of airlines have become the second major cause of flight delay at airports. A stochastic approach is used in this paper to model the operation of aircraft turnaround and the departure punctuality of a turnaround aircraft at an airport. The aircraft turnaround model is then used to investigate the punctuality problem of turnaround aircraft. Model results reveal that the departure punctuality of a turnaround aircraft is influenced by the length of scheduled turnaround time, the arrival punctuality of inbound aircraft as well as the operational efficiency of aircraft ground services. The aircraft turnaround model proposed is then employed to evaluate the endogenous schedule punctuality of two turnaround aircraft. Model results, when compared with observation data, show that the operational efficiency of aircraft ground services varies among turnarounds. Hence, it is recommended that the improvement of departure punctuality of turnaround aircraft may be achieved from two approaches: airline scheduling control and the management of operational efficiency of aircraft ground services.     

工期及最优目标

以光缆敷设为例 ,分析阻工现象对工程进度的影响 ,建立有约束条件的最短工期优化模式 ,搜索最优目标。     

Traveler delay costs and value of time with trip chains, flexible activity scheduling and information

The delay costs of traffic disruptions and congestion and the value of travel time reliability are typically evaluated using single trip scheduling models, which treat the trip in isolation of previous and subsequent trips and activities. In practice, however, when activity scheduling to some extent is flexible, the impact of delay on one trip will depend on the actual and predicted travel time on itself as well as other trips, which is important to consider for long-lasting disturbances and when assessing the value of travel information. In this paper we extend the single trip approach into a two trips chain and activity scheduling model. Preferences are represented as marginal activity utility functions that take scheduling flexibility into account. We analytically derive trip timing optimality conditions, the value of travel time and schedule adjustments in response to travel time increases. We show how the single trip models are special cases of the present model and can be generalized to a setting with trip chains and flexible scheduling. We investigate numerically how the delay cost depends on the delay duration and its distribution on different trips during the day, the accuracy of delay prediction and travel information, and the scheduling flexibility of work hours. The extension of the model framework to more complex schedules is discussed.