A cost-based maritime container assignment model

A recently proposed frequency-based maritime container assignment model (Bell et al., 2011) seeks an assignment of full and empty containers to paths that minimises expected container travel time, whereas containers are in practice more likely to be assigned to minimise expected cost. A cost-based container assignment model is proposed here. It is assumed that routes and service frequencies are given so ship operating costs are also fixed. The objective is to assign containers to routes to minimise container handling costs, container rental and inventory costs. The constraints in the model are extended to include route as well as port capacities. It is shown that the problem remains a linear program. A numerical example is presented to illustrate the properties of the model. The paper concludes by considering the many applications of the proposed maritime container assignment model.     

A network model for gate assignment

In this research we developed a network model that will help the airport authorities assign flights to gates both efficiently and effectively. The model was formulated as a multi-commodity network flow problem. An algorithm based on the Lagrangian relaxation, with subgradient methods, accompanied by a shortest path algorithm and a Lagrangian heuristic was developed to solve the problem. The model was tested using data from Chiang Chiek-Shek Airport.     

A container terminal simulation model with animation capabilities

The objective of this paper is to introduce a computer simulation model with on-screen animation graphics, which can simulate the operations of a container terminal equipped with straddle carriers. The movements of the equipment are simulated as realistically as possible, to include time losses due to the mismatch in the sequence of equipment movements and to traffic congestion. Trucks are normally served in a specified area, but in some cases, straddle carrier drivers can call the truck to be served directly in the container storage areas. The experience of operators is incorporated in the model, in the form of a knowledge base, that is used to simulate the above process and determine the service discipline. The model was designed to evaluate different configurations (changes in yard layout, equipment number and productivity, truck arrival pattern and service discipline) of the simulated system. The proposed model was used to examine the differences between “the observed” operations strategy and the strategy dictated by the operational rules of the port of Piraeus. The results indicate that “the observed” strategy leads to shorter truck service time but increase the traffic conflicts in the terminal's internal transport networks.     

Storage space allocation in container terminals

Container terminals are essential intermodal interfaces in the global transportation network. Efficient container handling at terminals is important in reducing transportation costs and keeping shipping schedules. In this paper, we study the storage space allocation problem in the storage yards of terminals. This problem is related to all the resources in terminal operations, including quay cranes, yard cranes, storage space, and internal trucks. We solve the problem using a rolling-horizon approach. For each planning horizon, the problem is decomposed into two levels and each level is formulated as a mathematical programming model. At the first level, the total number of containers to be placed in each storage block in each time period of the planning horizon is set to balance two types of workloads among blocks. The second level determines the number of containers associated with each vessel that constitutes the total number of containers in each block in each period, in order to minimize the total distance to transport the containers between their storage blocks and the vessel berthing locations. Numerical runs show that with short computation time the method significantly reduces the workload imbalance in the yard, avoiding possible bottlenecks in terminal operations.     

A schedule-based time-dependent trip assignment model for transit networks

A schedule-based time-dependent trip assignment model for transit networks is presented. First the transit network model is formulated using the schedule-based approach, in which the vehicles are assumed to arrive punctually in accordance with a scheduled time-table. Based on a previously developed time-dependent shortest path algorithm, an all-or-nothing network loading procedure is employed to assign the passenger trips onto the network. Both the passenger demand and scheduled time-table are time-varying. This provides a versatile tool for the evaluation of the performance of transit networks subject to peak period loading. A case study using the Mass Transit Railway System in Hong Kong is given to illustrate the potential applications of the model.     

A dynamic traffic assignment model for a continuum transportation system

A predictive continuum dynamic user-optimal (PDUO-C) model is formulated in this study to investigate the dynamic characteristics of traffic flow and the corresponding route-choice behavior of travelers within a region with a dense urban road network. The modeled region is arbitrary in shape with a single central business district (CBD) and travelers continuously distributed over the region. Within this region, the road network is represented as a continuum and travelers patronize a two-dimensional continuum transportation system to travel to the CBD. The PDUO-C model is solved by a promising solution algorithm that includes elements of the finite volume method (FVM), the finite element method (FEM), and the explicit total variation diminishing Runge-Kutta (TVD-RK) time-stepping method. A numerical example is given to demonstrate the utility of the proposed model and the effectiveness of the solution algorithm in solving this PDUO-C problem.     

A multiobjective chance constrained programming model for supplier selection under uncertainty

Risk management is an inherent part of supplier selection. While companies are enjoying the benefits of outsourcing, risks brought by this practice should be taken into account in the process of decision making. This paper presents a multiobjective stochastic sequential supplier allocation model to help in supplier selection under uncertainty. Demand for products, capacities at suppliers as well as transportation and other variable costs are the main sources of uncertainty and are modeled using probability distributions. Disruptions are exogenous events and the model provides proactive mitigation strategies against disruptions by assigning backup suppliers who can be used in case of a default at a primary supplier. When there is no disruption, the model’s solution is an optimal supplier order assignment, considering operational risks.     

A dynamic stochastic assignment model for the analysis of general networks

The paper adopts the framework employed by the existing dynamic assignment models, which analyse specific network forms, and develops a methodology for analysing general networks. Traffic conditions within a link are assumed to be homogeneous, and the time varying O-D travel times and traffic flow patterns are calculated using elementary relationships from traffic flow theory and link volume conservation equations. Each individual is assumed to select a departure time and a route by trading off the travel time and schedule delay associated with each alternative. A route is considered as reasonable if it includes only links which do not take the traveller back to the origin. The set of reasonable routes is not consistant but depends on the time that an individual decides to depart from his origin. Equilibrium distributions are derived from a Markovian model which describes the evolution of travel patterns from day to day. Numerical simulation experiments are conducted to analyse the impact of different work start time flexibilities on the time dependent travel patterns. The similarity between link flows and travel times obtained from static and dynamic stochastic assignment is investigated. It is shown that in congested networks the application of static assignment results in travel times which are lower than the ones predicted by dynamic assignment.     

A predictive dynamic traffic assignment model in congested capacity-constrained road networks

In this paper, a predictive dynamic traffic assignment model in congested capacity-constrained road networks is formulated. A traffic simulator is developed to incrementally load the traffic demand onto the network, and updates the traffic conditions dynamically. A time-dependent shortest path algorithm is also given to determine the paths with minimum actual travel time from an origin to all the destinations. The traffic simulator and time-dependent shortest path algorithm are employed in a method of successive averages to solve the dynamic equilibrium solution of the problem. A numerical example is given to illustrate the effectiveness of the proposed method.     

A dynamic traffic assignment model for the assessment of moving bottlenecks

Moving bottlenecks in highway traffic are defined as a situation in which a slow-moving vehicle, be it a truck hauling heavy equipment or an oversized vehicle, or a long convey, disrupts the continuous flow of the general traffic. The effect of moving bottlenecks on traffic flow is an important factor in the evaluation of network performance. This effect, though, cannot be assessed properly by existing transportation tools, especially when the bottleneck travels relatively long distances in the network.This paper develops a dynamic traffic assignment (DTA) model that can evaluate the effects of moving bottlenecks on network performance in terms of both travel times and traveling paths. The model assumes that the characteristics of the moving bottleneck, such as traveling path, physical dimensions, and desired speed, are predefined and, therefore, suitable for planned conveys.The DTA model is based on a mesoscopic simulation network-loading procedure with unique features that allow assessing the special dynamic characteristics of a moving bottleneck. By permitting traffic density and speed to vary along a link, the simulation can capture the queue caused by the moving bottleneck while preserving the causality principles of traffic dynamics.     

Automated shunting of rail container wagons in ports and terminal areas

The development of intermodal container transport is hampered in part by the cost associated with the shunting of trains in marshalling yards, inland and port railway terminals. Many new technologies have been developed in the past decade, but have still not been applied because of high capital investment costs, lack of sufficient market demand and uncertain rates of return. The key for increasing the competitiveness of intermodal container transport by rail is the operation of heavy haul container trains between port and inland railway terminals more frequently with fast, flexible and automatic transhipment, shunting and coupling of container wagons. The operation of self-driven railcars equipped with automatic centre coupling on terminal tracks, which can also be train-hauled on conventional hinterland railway lines, would enable a reduction of shunting and transhipment time and costs in intermodal container terminals by more than 30%.     

A queuing model analysis of the performance of the Hong Kong container terminals

The growth of container‐handling industry and its impact on Hong Kong's economy have aroused considerable attention in recent decades. Within the recent twenty years, the rapid growth of container‐handling industry has led Hong Kong to become one of the world's busiest container port with over 11 million T.E.U. s (Twenty Feet Equivalent Units) container throughput in one year period. Also the container throughput is expected to reach 15.5 million T.E.U. s by year 2004.

As the success of container‐handling industry is significant, many studies have been conducted relating to this subject. In this paper, an application of a queuing theory model to Kwai Chung Container Terminals is developed and described. Specifically, we consider seasonal changes at the Terminals and focus on their effects on inter‐arrival time and service time of container vessel.

A crucial component of the study relates to the empirical data collected. Besides verifying the validity of the model, those data provide guidelines for developing schemes to manage the seasonal fluctuation of container throughput of the Terminals.     

A cell-based Merchant-Nemhauser model for the system optimum dynamic traffic assignment problem

A cell-based variant of the Merchant-Nemhauser (M-N) model is proposed for the system optimum (SO) dynamic traffic assignment (DTA) problem. Once linearized and augmented with additional constraints to capture cross-cell interactions, the model becomes a linear program that embeds a relaxed cell transmission model (CTM) to propagate traffic. As a result, we show that CTM-type traffic dynamics can be derived from the original M-N model, when the exit-flow function is properly selected and discretized. The proposed cell-based M-N model has a simple constraint structure and cell network representation because all intersections and cells are treated uniformly. Path marginal costs are defined using a recursive formula that involves a subset of multipliers from the linear program. This definition is then employed to interpret the necessary condition, which is a dynamic extension of the Wardrop’s second principle. An algorithm is presented to solve the flow holding back problem that is known to exist in many discrete SO-DTA models. A numerical experiment is conducted to verify the proposed model and algorithm.     

On a link-based day-to-day traffic assignment model

In this paper, we perform a rigorous analysis on a link-based day-to-day traffic assignment model recently proposed in He et al. (2010). Several properties, including the invariance set and the constrained stability, of this dynamical process are established. An extension of the model to the asymmetric case is investigated and the stability result is also established under slightly more restrictive assumptions. Numerical experiments are conducted to demonstrate the findings.     

Robust flight-to-gate assignment using flight presence probabilities

In this paper we present a novel method to improve the robustness of solutions to the Flight-to-Gate Assignment Problem (FGAP), with the aim to reduce the need for gate re-planning due to unpredicted flight schedule disturbances in the daily operations at an airport. We propose an approach in which the deterministic gate constraints are replaced by stochastic gate constraints that incorporate the inherent stochastic flight delays in such a way so as to ensure that the expected gate conflict probability of two flights assigned to the same gate at the same time does not exceed a user-specified value. The novel approach is integrated into an existing multiple time slot FGAP model that relies on a binary integer programming formulation and is tested using real-life data pertaining to Amsterdam Airport Schiphol. The results confirm that the proposed approach holds out great promise to improve the robustness of the FGAP solutions.     

A basic mathematical model for evacuation problems in urban areas

Real life situations like floods, hurricanes or chemical accidents may cause the evacuation of a certain area to rescue the affected population. To enable a fast and a safe evacuation a basic mixed-integer evacuation model has been developed that provides a reorganization of the traffic routing of a certain area for the case of an evacuation. This basic problem of evacuation minimizes the evacuation-time while prohibiting conflicts within intersections. Our evacuation model is a dynamic network flow problem with additional variables for the number and direction of used lanes and with additional complicating constraints.Because of the size of the time-expanded network, the computational effort required by standard software is already very high for tiny instances. To deal with realistic instances we propose a heuristic approach.     

A multi-objective train scheduling model and solution

This paper develops a multi-objective optimization model for the passenger train-scheduling problem on a railroad network which includes single and multiple tracks, as well as multiple platforms with different train capacities. In this study, lowering the fuel consumption cost is the measure of satisfaction of the railway company and shortening the total passenger-time is being regarded as the passenger satisfaction criterion. The solution of the problem consists of two steps. First the Pareto frontier is determined using the -constraint method, and second, based on the obtained Pareto frontier detailed multi-objective optimization is performed using the distance-based method with three types of distances. Numerical examples are given to illustrate the model and solution methodology.     

Spillback congestion in dynamic traffic assignment: A macroscopic flow model with time-varying bottlenecks

In this paper, we propose a new model for the within-day Dynamic Traffic Assignment (DTA) on road networks where the simulation of queue spillovers is explicitly addressed, and a user equilibrium is expressed as a fixed-point problem in terms of arc flow temporal profiles, i.e., in the infinite dimension space of time’s functions. The model integrates spillback congestion into an existing formulation of the DTA based on continuous-time variables and implicit path enumeration, which is capable of explicitly representing the formation and dispersion of vehicle queues on road links, but allows them to exceed the arc length. The propagation of congestion among adjacent arcs will be achieved through the introduction of time-varying exit and entry capacities that limit the inflow on downstream arcs in such a way that their storage capacities are never exceeded. Determining the temporal profile of these capacity constraints requires solving a system of spatially non-separable macroscopic flow models on the supply side of the DTA based on the theory of kinematic waves, which describe the dynamic of the spillback phenomenon and yield consistent network performances for given arc flows. We also devise a numerical solution algorithm of the proposed continuous-time formulation allowing for “long time intervals” of several minutes, and give an empirical evidence of its convergence. Finally, we carry out a thorough experimentation in order to estimate the relevance of spillback modeling in the context of the DTA, compare the proposed model in terms of effectiveness with the Cell Transmission Model, and assess the efficiency of the proposed algorithm and its applicability to real instances with large networks.