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.

     

An optimization model for integrated planning of railway traffic and network maintenance

Railway transportation systems are important for society and have many challenging and important planning problems. Train services as well as maintenance of a railway network need to be scheduled efficiently, but have mostly been treated as two separate planning problems. Since these activities are mutually exclusive they must be coordinated and should ideally be planned together. In this paper we present a mixed integer programming model for solving an integrated railway traffic and network maintenance problem. The aim is to find a long term tactical plan that optimally schedules train free windows sufficient for a given volume of regular maintenance together with the wanted train traffic. A spatial and temporal aggregation is used for controlling the available network capacity. The properties of the proposed model are analyzed and computational experiments on various synthetic problem instances are reported. Model extensions and possible modifications are discussed as well as future research directions.     

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.     

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.     

Essential elements in tactical planning models for container liner shipping

Tactical planning models for liner shipping problems such as network design and fleet deployment usually minimize the total cost or maximize the total profit subject to constraints including ship availability, service frequency, ship capacity, and transshipment. Most models in the literature do not consider slot-purchasing, multi-type containers, empty container repositioning, or ship repositioning, and they formulate the numbers of containers to transport as continuous variables. This paper develops a mixed-integer linear programming model that captures all these elements. It further examines from the theoretical point of view the additional computational burden introduced by incorporating these elements in the planning model. Extensive numerical experiments are conducted to evaluate the effects of the elements on tactical planning decisions. Results demonstrate that slot-purchasing and empty container repositioning have the largest impact on tactical planning decisions and relaxing the numbers of containers as continuous variables has little impact on the decisions.     

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.     

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.     

A tabu search algorithm to solve the integrated planning of container on an inter-terminal network connected with a hinterland rail network

Transport demand for containers has been increasing for decades, which places pressure on road transport. As a result, rail transport is stimulated to provide better intermodal freight transport services. This paper investigates mathematical models for the planning of container movements in a port area, integrating the inter-terminal transport of containers (ITT, within the port area) with the rail freight formation and transport process (towards the hinterland). An integer linear programming model is used to formulate the container transport across operations at container terminals, the network interconnecting them, railway yards and the railway networks towards the hinterland. A tabu search algorithm is proposed to solve the problem. The practical applicability of the algorithm is tested in a realistic infrastructure case and different demand scenarios. Our results show the degree by which internal (ITT) and external (hinterland) transport processes interact, and the potential for improvement of overall operations when the integrated optimization proposed is used. Instead, if the planning of containers in the ITT system is optimized as a stand-alone problem, the railway terminals may suffer from longer delay times or additional train cancellations. When planning the transport of 4060 TEU containers within one day, the benefits of the ITT planning without considering railway operations account for 17% ITT cost reduction but 93% railway operational cost growth, while the benefits of integrating ITT and railway account for a reduction of 20% in ITT cost and 44% in railway operational costs.     

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%.     

Performance analysis of cargo-handling equipment from a green container terminal perspective

This study employs a green container terminal perspective to compare the performance of four types of cargo handling equipment used in container yards – automatic rail, rail, electric tire, and tire transtainers – based on working efficiency, energy saving performance, and carbon reductions. It is found that automatic rail and electric tire transtainers are the optimal types of green cargo handling equipment.     

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.     

Sequencing twin automated stacking cranes in a block at automated container terminal

In the considered automated container terminal (ACT) that is designed for Shanghai Yangsha Terminal, two automated stacking cranes (ASCs) are configured for each block and they interact with automated lifting vehicles (ALVs) at the two ends of a block individually. To increase the capacity, container yards with multiple rows of blocks perpendicular to the terminal’s shoreline are considered. To utilize the yard spaces, the twin ASCs are devised to share the same tracks installed at the two sides of a block, while interferences between the ASCs challenge the routing and sequencing operations. To isolate the control and simplify the coordination of the two ASCs, the interference between ASCs is formulated by analyzing the minimal temporal intervals between any two tasks. Three models are then established to sequence the container handling tasks under the minimization of the makespan. An exact algorithm and a genetic algorithm are designed to solve the problem. Numerical experiments show that the algorithms are competitive comparing to on-the-shelf solvers. Practical implications are investigated based on the formulations and experimental results. The managerial implications and technological aspects of applying the formulations and algorithms to practical situations to real-world ACTs are discussed.     

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.     

Control of interacting machines in automated container terminals using a sequential planning approach for collision avoidance

The control of automated container terminals is complex since Quay Cranes (QCs), Automated Guided Vehicles (AGVs) and Automated Stacking Cranes (ASCs) interact intensively for transporting containers, while collision avoidance of equipment must be ensured. This paper proposes a methodology to generate collision-free trajectories of free-ranging AGVs in automated container terminals, while minimizing the makespan of the whole container handling system. A hierarchical control architecture is proposed to integrate the scheduling of interacting machines and trajectory planning of AGVs. Following a so-called overall graph sequence by a scheduler, the collision-free trajectories of AGVs are determined by solving a collection of mixed integer linear programming problems sequentially. Simulation results illustrate the potential of the proposed methodology.     

A joint optimization model for liner container cargo assignment problem using state-augmented shipping network framework

This paper proposes a state-augmented shipping (SAS) network framework to integrate various activities in liner container shipping chain, including container loading/unloading, transshipment, dwelling at visited ports, in-transit waiting and in-sea transport process. Based on the SAS network framework, we develop a chance-constrained optimization model for a joint cargo assignment problem. The model attempts to maximize the carrier’s profit by simultaneously determining optimal ship fleet capacity setting, ship route schedules and cargo allocation scheme. With a few disparities from previous studies, we take into account two differentiated container demands: deterministic contracted basis demand received from large manufacturers and uncertain spot demand collected from the spot market. The economies of scale of ship size are incorporated to examine the scaling effect of ship capacity setting in the cargo assignment problem. Meanwhile, the schedule coordination strategy is introduced to measure the in-transit waiting time and resultant storage cost. Through two numerical studies, it is demonstrated that the proposed chance-constrained joint optimization model can characterize the impact of carrier’s risk preference on decisions of the container cargo assignment. Moreover, considering the scaling effect of large ships can alleviate the concern of cargo overload rejection and consequently help carriers make more promising ship deployment schemes.     

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.     

An integrated metro operation optimization to minimize energy consumption

Energy efficient techniques are receiving increasing attention because of rising energy prices and environmental concerns. Railways, along with other transport modes, are facing increasing pressure to provide more intelligent and efficient power management strategies.This paper presents an integrated optimization method for metro operation to minimize whole day substation energy consumption by calculating the most appropriate train trajectory (driving speed profile) and timetable configuration. A train trajectory optimization algorithm and timetable optimization algorithm are developed specifically for the study. The train operation performance is affected by a number of different systems that are closely interlinked. Therefore, an integrated optimization process is introduced to obtain the optimal results accurately and efficiently.The results show that, by using the optimal train trajectory and timetable, the substation energy consumption and load can be significantly reduced, thereby improving the system performance and stability. This also has the effect of reducing substation investment costs for new metros.     

Recent success stories on integrated optimization of railway systems

Planning and operating railway transportation systems is an extremely hard task due to the combinatorial complexity of the underlying discrete optimization problems, the technical intricacies, and the immense size of the problem instances. Because of that, however, mathematical models and optimization techniques can result in large gains for both railway customers and operators, e.g., in terms of cost reductions or service quality improvements. In the last years a large and growing group of researchers in the OR community have devoted their attention to this domain developing mathematical models and optimization approaches to tackle many of the relevant problems in the railway planning process. However, there is still a gap to bridge between theory and practice (e.g. Cacchiani et al., 2014; Borndörfer et al., 2010), with a few notable exceptions. In this paper we address three individual success stories, namely, long-term freight train routing (part I), mid-term rolling stock rotation planning (part II), and real-time train dispatching (part III). In each case, we describe real-life, successful implementations. We will discuss the individual problem setting, survey the optimization literature, and focus on particular aspects addressed by the mathematical models. We demonstrate on concrete applications how mathematical optimization can support railway planning and operations. This gives proof that mathematical optimization can support the planning of railway resources. Thus, mathematical models and optimization can lead to a greater efficiency of railway operations and will serve as a powerful and innovative tool to meet recent challenges of the railway industry.