An optimization model for integrated transit-parking policy planning

The optimization model proposed in this paper is aimed to assist city councils (or other transport authorities) in the planning of integrated transit-parking policies. The objective is to minimize the joint operating deficit of the transit and parking operators while ensuring given minimum levels of (motorized) mobility in a city. The key decision variables are transit fares and parking fees. The impact of price changes on transit and car modal shares are described by logit functions of the generalized travel costs. The practical utility of the model is illustrated with a case study involving a midsize city in Portugal.

     

Flexibility in planning and development of a container terminal: an application of an American-style call option

The prosperity and social progress of developed and developing economies is highly dependent on the existence of efficient transport infrastructure. Nevertheless, current budgetary constraints are jeopardizing the necessary investments in new or existing infrastructure. New models for planning and managing infrastructure are now necessary to overcome the lack of public economic resources available. Port infrastructure is no exception and, due to the vast number of uncertainties involving these projects, it is relevant to maximize the capture of the latent value of flexible options. Incorporating flexibility in these projects, prior to the implementation phase, can be a solution that allows port managers to address future uncertainties and mitigate risk exposure. This paper analyzes the incorporation of flexibility in port planning through the use of an American call option to the physical capacity expansion problem. The rationale is to implement a flexible expansion plan, through options that can be exercised at any given time, that are able to deal with uncertainty in demand. The paper uses a case study – Terminal Container of Ferrol, in Spain – and the results support the hypothesis that imbedded flexibility will robustly increase the net present value of the project.     

Efficient planning of berth allocation for container terminals in Asia

Berth allocation is essential for efficient terminal utilization in container ports, especially those in Asia. This paper is concerned with a berth allocation problem(BAP) that minimizes the sum of port staying times of ships and that minimizes dissatisfaction of the ships in terms of the berthing order. In general there exist tradeoffs between these objectives. An algorithm is presented to identify noninferior solutions to the BAP. The algorithm is demonstrated with some sample problems and the results indicate the importance of the problem in efficient terminal utilization.     

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.     

Multi-period yard template planning in container terminals

This paper is about yard management in container ports. As a tactical level decision-making tool in a port, a yard template determines the assignment of spaces (subblocks) in a yard for arriving vessels, which visit the port periodically. The objective of yard template planning is to minimize the transportation cost of moving containers around the yard. To handle yard template planning, a mixed integer programming model is proposed that also takes into account traffic congestion in the yard. A further complication is that the cycle time of the vessels' periodicities is not uniform and varies among them, perhaps being one week, ten days, or two weeks, etc. However, this multiple cycle time of the periodicities of vessel arrival patterns, which complicates the yard template decision, is also considered in the model. Moreover, a local branching based solution method and a Particle Swarm Optimization based solution method are developed for solving the model. Numerical experiments are also conducted to validate the effectiveness of the proposed model, which can save around 24% of the transportation costs of yard trucks when compared with the commonly used First-Come-First-Served decision rule. Moreover, the proposed solution methods can not only solve the proposed model within a reasonable time, but also obtain near-optimal results with about 0.1–2% relative gap.     

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%.     

Energy-aware control for automated container terminals using integrated flow shop scheduling and optimal control

The performance of container terminals needs to be improved to handle the growth of transported containers and maintain port sustainability. This paper provides a methodology for improving the handling capacity of an automated container terminal in an energy-efficient way. The behavior of a container terminal is considered as consisting of a higher level and a lower level represented by discrete-event dynamics and continuous-time dynamics, respectively. These dynamics represent the behavior of a large number of terminal equipment. The dynamics need to be controlled. For controlling the higher level dynamics, a minimal makespan problem is solved. For this, the minimal time required by equipment for performing an operation at the lower level is needed. The minimal time for performing an operation at the lower level is obtained using Pontryagin’s Minimum Principle. The actual operation time allowed by the higher level for processing an operation at the lower level is subsequently determined by a scheduling algorithm at the higher level. Given an actual operation time, the lower level dynamics are controlled using optimal control to achieve minimal energy consumption while respecting the time constraint. Simulation studies illustrate how energy-efficient management of equipment for the minimal makespan could be obtained using the proposed methodology.     

Multiobjective bilevel optimization for transportation planning and management problems

Many previous studies have formulated the decision‐making problems in transportation system planning and management as single‐objective bilevel optimization models. However, real‐world decision‐making processes always have several social concerns and thus multiple objectives need to be achieved simultaneously. In most cases, these objective functions conflict with each other and are also not simple enough to be combined into a single one. Therefore it is necessary to apply multiobjective optimization to generate non‐dominated or Pareto optimal alternatives. It can be foreseen that the multiobjective bilevel modeling approach can become a powerful, and possibly interactive, decision tool, allowing the decision‐makers to learn more about the problem before committing to a final decision. Such multiobjective bilevel models are difficult to solve due to their intrinsic nonconvexity and multiple objectives. This paper consequently proposes a solution algorithm for the multiobjective bilevel models using genetic algorithms. The proposed algorithm is illustrated, using the numerical example taken from the previous study. It is found that the proposed algorithm is efficient to search simultaneously the Pareto optimal solutions.     

Modeling of yard congestion and optimization of yard template in container ports

As a tactical-level plan, a yard template determines the assignment of spaces in a container port yard for arriving vessels. This paper investigates the concept of yard congestion quantitatively in the context of yard truck interruptions, and develops a combination of probabilistic and physics-based models for truck interruptions. The above work enables us to exactly evaluate the expected link travel time, which then acts as the basis for proposing a mixed-integer programming model that minimizes the total expected travel time of moving containers around the yard. A Squeaky Wheel Optimization based meta-heuristic is developed to solve the model. Experiments are also conducted to validate the effectiveness of the model and the solution method.     

Waiting profiles: An efficient protocol for enabling distributed planning of container barge rotations along terminals in the port of Rotterdam

We consider the problem of aligning barge rotations with quay schedules of terminals in the port of Rotterdam. Every time a barge visits the port, it has to make a rotation along, on average, eight terminals to load and unload containers. A central solution, e.g., a trusted party that coordinates the activities of all barges and terminals, is not feasible for several reasons. We propose a multi-agent based approach of the problem, since a multi-agent system can mirror to a large extent the way the business network is currently organized and can provide a solution that is acceptable to each of the parties involved. We examine the value of exchanging different levels of information and evaluate the performance by means of simulation. We compare the results with an off-line scheduling algorithm. The results indicate that, in spite of the limited information available, our distributed approach performs quite well when compared to the central approach. In addition, our experiments indicate that an information exchange based on waiting profiles reduces the average tardiness per barge with almost 80% when compared to the situation with no information exchange. We therefore think that waiting profiles provide a promising protocol to tackle this problem.     

Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains

This paper proposes a methodology to generate a robust logistics plan that can mitigate demand uncertainty in humanitarian relief supply chains. More specifically, we apply robust optimization (RO) for dynamically assigning emergency response and evacuation traffic flow problems with time dependent demand uncertainty. This paper studies a Cell Transmission Model (CTM) based system optimum dynamic traffic assignment model. We adopt a min–max criterion and apply an extension of the RO method adjusted to dynamic optimization problems, an affinely adjustable robust counterpart (AARC) approach. Simulation experiments show that the AARC solution provides excellent results when compared to deterministic solution and sampling based stochastic programming solution. General insights of RO and transportation that may have wider applicability in humanitarian relief supply chains are provided.     

A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass

The integrated timetable and speed profile optimization model has recently attracted more attention because of its good achievements on energy conservation in metro systems. However, most previous studies often ignore the spatial and temporal uncertainties of train mass, and the variabilities of tractive force, braking force and basic running resistance on energy consumption in order to simplify the model formulation and solution algorithm. In this paper, we develop an integrated metro timetable and speed profile optimization model to minimize the total tractive energy consumption, where these real-world operating conditions are explicitly considered in the model formulation and solution algorithm. Firstly, we formulate a two-phase stochastic programming model to determine the timetable and speed profile. Given the speed profile, the first phase determines the timetable by scheduling the arrival and departure times for each station, and the second phase determines the speed profile for each inter-station with the scheduled arrival and departure times. Secondly, we design a simulation-based genetic algorithm procedure incorporated with the optimal train control algorithm to find the optimal solution. Finally, we present a simple example and a real-world example based on the operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results of the real-world example show that, during peak hours, off-peak hours and night hours, the total tractive energy consumptions can be reduced by: (1) 10.66%, 9.94% and 9.13% in comparison with the current timetable and speed profile; and (2) 3.35%, 3.12% and 3.04% in comparison with the deterministic model.     

An approximate dynamic programming approach for the empty container allocation problem

The objective of this study is to demonstrate the successful application of an approximate dynamic programming approach in deriving effective operational strategies for the relocation of empty containers in the containerized sea-cargo industry. A dynamic stochastic model for a simple two-ports two-voyages (TPTV) system is proposed first to demonstrate the effectiveness of the approximate optimal solution obtained through a simulation based approach known as the temporal difference (TD) learning for average cost minimization. An exact optimal solution can be obtained for this simple TPTV model. Approximate optimal results from the TPTV model utilizing a linear approximation architecture under the TD framework can then be compared to this exact solution. The results were found comparable and showed promising improvements over an existing commonly used heuristics. The modeling and solution approach can be extended to a realistic multiple-ports multiple-voyages (MPMV) system. Some results for the MPMV case are shown.     

Agent-based model for continuous activity planning with an open planning horizon

The paper proposes the microscopic travel demand model continuous target-based activity planning (C-TAP) that generates multi-week schedules by means of a continuous planning approach with an open planning horizon. C-TAP introduces behavioral targets to describe people’s motivation to perform activities, and it uses a planning heuristic to make on-the-fly decisions about upcoming activities. The planning heuristic bases its decisions on three aspects: a discomfort index derived from deviations from agents’ past performance with regard to their behavioral targets; the effectiveness of the immediate execution; and activity execution options available in the near future. The paper reports the results of a test scenario based on an existing 6-week continuous travel diary and validates C-TAP by comparing simulation results with observed behavioral patterns along several dimensions (weekday similarities, weekday execution probabilities of activities, transition probabilities between activities, duration distributions of activities, frequency distributions of activities, execution interval distributions of activities and weekly travel probability distributions). The results show that C-TAP has the capability to reproduce observed behavior and the flexibility to introduces new behavioral patterns.     

Scenario construction for transport planning

This paper argues that the types of uncertainty having the most serious consequences for transport planning are not captured by existing modelling procedures, which typically rely on a limited amount of sensitivity testing of plans based on extrapolative forecasts. It recommends as an alternative scenario‐based planning, an approach which is finding increasing application in private sector policy formation. The paper summarises the origins of scenario planning and the gradual broadening in its range of application, before going on to suggest methods of scenario construction suitable for transport planning applications. It considers the arguments for and against associating probabilities with scenarios and also addresses the problems of formulating transport strategies and evaluating them within the context of a scenario‐based approach to strategy choice.     

Collaborative truck scheduling and appointments for trucking companies and container terminals

Appointment systems for truck arrivals at container terminals have been applied in many ports to reduce truck congestion. This study suggests a new appointment process by which trucking companies and terminals collaboratively determine truck operation schedules and truck arrival appointments. This study formulates a mathematical model involving a sub-problem for each trucking company to determine the optimal dispatching schedules for trucks and the other sub-problem for the terminal to estimate the expected truck system time in each time interval. An iterative collaboration process is proposed based on a decomposed mathematical formulation. Numerical experiments are conducted to investigate the performance of the decision process and the robustness of the process in practical operation conditions.     

The impact of job assignment rules for straddle carriers on the throughput of container terminals

Straddle carriers represent a critical resource in the handling of containers within a container terminal. It is essential that they are deployed in an as efficient manner as possible. The research presented in this paper is motivated by the need to critically evaluate job assignment rules for straddle carriers that operate in a multiple straddle environment. This is achieved by developing a discrete event simulation model using industrial simulation software to model a container terminal located in Melbourne, Australia. The model accounts for variables such as the number of straddle carriers needed, the speed of straddle carriers, the arrival rate of trucks, and the job assignment rule. A principal finding of the study is that increasing the number of straddles in the present set‐up from 6 to 7 has a negligible effect on daily throughput. However, an increase in the number of straddles to 7 is expected to have a profound effect on reducing the average waiting time of trucks within the terminal from over 16 minutes to under 9 minutes, a decrease of 46.5%. However, a further increase in the number of straddles results in no further increase in daily throughput. It was observed that the throughput of the terminal is very sensitive to the speeds at which straddles travel. The management of the terminal has proposed a new heuristic job assignment rule for straddles, because the present rule does not assign the jobs to straddles closest to the truck requesting to load or unload a container. As a result a new heuristic job assignment rule was tested. The simulation results revealed that both the old and new rules performed equally well using performance indicators such as average container flow time, daily throughput, average waiting time of jobs, number of jobs in the queue, and straddle utilisation. Therefore, the new rule will not improve these performance measures if implemented