349 research outputs found
Review on integrated scheduling of quay crane and yard truck
With the development of port shipping trade, the increasing container throughput has brought pressure to port operation. Research literatures on quay crane scheduling, yard truck scheduling and integrated scheduling of quay crane and yard truck are reviewed in turn. Combined with the current research, the future research direction of integrated scheduling of quay crane and yard truck is proposed
Sequence-Based Simulation-Optimization Framework With Application to Port Operations at Multimodal Container Terminals
It is evident in previous works that operations research and mathematical algorithms can provide optimal or near-optimal solutions, whereas simulation models can aid in predicting and studying the behavior of systems over time and monitor performance under stochastic and uncertain circumstances. Given the intensive computational effort that simulation optimization methods impose, especially for large and complex systems like container terminals, a favorable approach is to reduce the search space to decrease the amount of computation.
A maritime port can consist of multiple terminals with specific functionalities and specialized equipment. A container terminal is one of several facilities in a port that involves numerous resources and entities. It is also where containers are stored and transported, making the container terminal a complex system. Problems such as berth allocation, quay and yard crane scheduling and assignment, storage yard layout configuration, container re-handling, customs and security, and risk analysis become particularly challenging.
Discrete-event simulation (DES) models are typically developed for complex and stochastic systems such as container terminals to study their behavior under different scenarios and circumstances. Simulation-optimization methods have emerged as an approach to find optimal values for input variables that maximize certain output metric(s) of the simulation. Various traditional and nontraditional approaches of simulation-optimization continue to be used to aid in decision making.
In this dissertation, a novel framework for simulation-optimization is developed, implemented, and validated to study the influence of using a sequence (ordering) of decision variables (resource levels) for simulation-based optimization in resource allocation problems. This approach aims to reduce the computational effort of optimizing large simulations by breaking the simulation-optimization problem into stages.
Since container terminals are complex stochastic systems consisting of different areas with detailed and critical functions that may affect the output, a platform that accurately simulates such a system can be of significant analytical benefit. To implement and validate the developed framework, a large-scale complex container terminal discrete-event simulation model was developed and validated based on a real system and then used as a testing platform for various hypothesized algorithms studied in this work
Simulation-optimization models for the dynamic berth allocation problem
Container terminals are designed to
provide support for the continuous changes in
container ships. The most common schemes used
for dock management are based on discrete and
continuous locations. In view of the steadily
growing trend in increasing container ship size,
more flexible berth allocation planning is
mandatory. The consideration of continuous
location in the container terminal is a good
option. This paper addresses the berth allocation
problem with continuous dock, which is called
dynamic berth allocation problem (DBAP). We
propose a mathematical model and develop a
heuristic procedure, based on a genetic
algorithm, to solve the corresponding mixed
integer problem. Allocation planning aims to
minimise distances travelled by the forklifts and
the quay crane, for container loading and
unloading operations for each ship, according to
the quay crane scheduling. Simulations are
undertaken using Arena software, and
experimental analysis is carried out for the most
important container terminal in Spain
A Simulation-Based Optimization Approach for Integrated Port Resource Allocation Problem
Todays, due to the rapid increase in shipping volumes, the container terminals are faced with the challenge to cope with these increasing demands. To handle this challenge, it is crucial to use flexible and efficient optimization approach in order to decrease operating cost. In this paper, a simulation-based optimization approach is proposed to construct a near-optimal berth allocation plan integrated with a plan for tug assignment and for resolution of the quay crane re-allocation problem. The research challenges involve dealing with the uncertainty in arrival times of vessels as well as tidal variations. The effectiveness of the proposed evolutionary algorithm is tested on RAJAEE Port as a real case. According to the simulation result, it can be concluded that the objective function value is affected significantly by the arrival disruptions. The result also demonstrates the effectiveness of the proposed simulation-based optimization approach. </span
An estimation of distribution algorithm for combinatorial optimization problems
This paper considers solving more than one combinatorial problem considered some of the most difficult to solve in the combinatorial optimization field, such as the job shop scheduling problem (JSSP), the vehicle routing problem with time windows (VRPTW), and the quay crane scheduling problem (QCSP). A hybrid metaheuristic algorithm that integrates the Mallows model and the Moth-flame algorithm solves these problems. Through an exponential function, the Mallows model emulates the solution space distribution for the problems; meanwhile, the Moth-flame algorithm is in charge of determining how to produce the offspring by a geometric function that helps identify the new solutions. The proposed metaheuristic, called HEDAMMF (Hybrid Estimation of Distribution Algorithm with Mallows model and Moth-Flame algorithm), improves the performance of recent algorithms. Although knowing the algebra of permutations is required to understand the proposed metaheuristic, utilizing the HEDAMMF is justified because certain problems are fixed differently under different circumstances. These problems do not share the same objective function (fitness) and/or the same constraints. Therefore, it is not possible to use a single model problem. The aforementioned approach is able to outperform recent algorithms under different metrics for these three combinatorial problems. Finally, it is possible to conclude that the hybrid metaheuristics have a better performance, or equal in effectiveness than recent algorithms
Barge Prioritization, Assignment, and Scheduling During Inland Waterway Disruption Responses
Inland waterways face natural and man-made disruptions that may affect navigation and infrastructure operations leading to barge traffic disruptions and economic losses. This dissertation investigates inland waterway disruption responses to intelligently redirect disrupted barges to inland terminals and prioritize offloading while minimizing total cargo value loss. This problem is known in the literature as the cargo prioritization and terminal allocation problem (CPTAP). A previous study formulated the CPTAP as a non-linear integer programming (NLIP) model solved with a genetic algorithm (GA) approach. This dissertation contributes three new and improved approaches to solve the CPTAP.
The first approach is a decomposition based sequential heuristic (DBSH) that reduces the time to obtain a response solution by decomposing the CPTAP into separate cargo prioritization, assignment, and scheduling subproblems. The DBSH integrates the Analytic Hierarchy Process and linear programming to prioritize cargo and allocate barges to terminals. Our findings show that compared to the GA approach, the DBSH is more suited to solve large sized decision problems resulting in similar or reduced cargo value loss and drastically improved computational time.
The second approach formulates CPTAP as a mixed integer linear programming (MILP) model improved through the addition of valid inequalities (MILP\u27). Due to the complexity of the NLIP, the GA results were validated only for small size instances. This dissertation fills this gap by using the lower bounds of the MILP\u27 model to validate the quality of all prior GA solutions. In addition, a comparison of the MILP\u27 and GA solutions for several real world scenarios show that the MILP\u27 formulation outperforms the NLIP model solved with the GA approach by reducing the total cargo value loss objective.
The third approach reformulates the MILP model via Dantzig-Wolfe decomposition and develops an exact method based on branch-and-price technique to solve the model. Previous approaches obtained optimal solutions for instances of the CPTAP that consist of up to five terminals and nine barges. The main contribution of this new approach is the ability to obtain optimal solutions of larger CPTAP instances involving up to ten terminals and thirty barges in reasonable computational time
Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals
Tesis por compendioDespite the continuous evolution in computers and information technology, real-world
combinatorial optimization problems are NP-problems, in particular in the domain of
planning and scheduling. Thus, although exact techniques from the Operations Research
(OR) field, such as Linear Programming, could be applied to solve optimization problems,
they are difficult to apply in real-world scenarios since they usually require too much computational
time, i.e: an optimized solution is required at an affordable computational time.
Furthermore, decision makers often face different and typically opposing goals, then resulting
multi-objective optimization problems. Therefore, approximate techniques from
the Artificial Intelligence (AI) field are commonly used to solve the real world problems.
The AI techniques provide richer and more flexible representations of real-world (Gomes
2000), and they are widely used to solve these type of problems. AI heuristic techniques
do not guarantee the optimal solution, but they provide near-optimal solutions in a reasonable
time. These techniques are divided into two broad classes of algorithms: constructive
and local search methods (Aarts and Lenstra 2003). They can guide their search processes
by means of heuristics or metaheuristics depending on how they escape from local optima
(Blum and Roli 2003). Regarding multi-objective optimization problems, the use of AI
techniques becomes paramount due to their complexity (Coello Coello 2006).
Nowadays, the point of view for planning and scheduling tasks has changed. Due to
the fact that real world is uncertain, imprecise and non-deterministic, there might be unknown
information, breakdowns, incidences or changes, which become the initial plans
or schedules invalid. Thus, there is a new trend to cope these aspects in the optimization
techniques, and to seek robust solutions (schedules) (Lambrechts, Demeulemeester, and
Herroelen 2008).
In this way, these optimization problems become harder since a new objective function
(robustness measure) must be taken into account during the solution search. Therefore,
the robustness concept is being studied and a general robustness measure has been developed
for any scheduling problem (such as Job Shop Problem, Open Shop Problem,
Railway Scheduling or Vehicle Routing Problem). To this end, in this thesis, some techniques
have been developed to improve the search of optimized and robust solutions in
planning and scheduling problems. These techniques offer assistance to decision makers
to help in planning and scheduling tasks, determine the consequences of changes, provide
support in the resolution of incidents, provide alternative plans, etc.
As a case study to evaluate the behaviour of the techniques developed, this thesis focuses
on problems related to container terminals. Container terminals generally serve
as a transshipment zone between ships and land vehicles (trains or trucks). In (Henesey
2006a), it is shown how this transshipment market has grown rapidly. Container terminals
are open systems with three distinguishable areas: the berth area, the storage yard,
and the terminal receipt and delivery gate area. Each one presents different planning and
scheduling problems to be optimized (Stahlbock and Voß 2008). For example, berth allocation,
quay crane assignment, stowage planning, and quay crane scheduling must be
managed in the berthing area; the container stacking problem, yard crane scheduling, and
horizontal transport operations must be carried out in the yard area; and the hinterland
operations must be solved in the landside area.
Furthermore, dynamism is also present in container terminals. The tasks of the container
terminals take place in an environment susceptible of breakdowns or incidences. For
instance, a Quay Crane engine stopped working and needs to be revised, delaying this
task one or two hours. Thereby, the robustness concept can be included in the scheduling
techniques to take into consideration some incidences and return a set of robust schedules.
In this thesis, we have developed a new domain-dependent planner to obtain more effi-
cient solutions in the generic problem of reshuffles of containers. Planning heuristics and
optimization criteria developed have been evaluated on realistic problems and they are
applicable to the general problem of reshuffling in blocks world scenarios.
Additionally, we have developed a scheduling model, using constructive metaheuristic
techniques on a complex problem that combines sequences of scenarios with different
types of resources (Berth Allocation, Quay Crane Assignment, and Container Stacking
problems). These problems are usually solved separately and their integration allows
more optimized solutions.
Moreover, in order to address the impact and changes that arise in dynamic real-world
environments, a robustness model has been developed for scheduling tasks. This model
has been applied to metaheuristic schemes, which are based on genetic algorithms. The
extension of such schemes, incorporating the robustness model developed, allows us to
evaluate and obtain more robust solutions. This approach, combined with the classical
optimality criterion in scheduling problems, allows us to obtain, in an efficient in way,
optimized solution able to withstand a greater degree of incidents that occur in dynamic
scenarios. Thus, a proactive approach is applied to the problem that arises with the presence
of incidences and changes that occur in typical scheduling problems of a dynamic real world.Rodríguez Molins, M. (2015). Optimization and Robustness in Planning and Scheduling Problems. Application to Container Terminals [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48545TESISCompendi
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