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

A combined Mixed Integer Programming model of seaside operations arising in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely the Berth Allocation Problem, the Quay Crane Assignment Problem, and the Quay Crane Scheduling problem, which have to be solved to carry out these seaside operations in container ports. Each one of these problems is complex to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence the need to solve them in a combined form. The problem is formulated as a mixed-integer programming model with the objective being to minimise the tardiness of vessels. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX

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

A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the Bee Colony Optimisation algorithm

AbstractBerth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method

An evolutionary approach to a combined mixed integer programming model of seaside operations as arise in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integer programming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times

Models and Solutions Algorithms for Improving Operations in Marine Transportation

International seaborne trade rose significantly during the past decades. This created the need to improve efficiency of liner shipping services and marine container terminal operations to meet the growing demand. The objective of this dissertation is to develop simulation and mathematical models that may enhance operations of liner shipping services and marine container terminals, taking into account the main goals of liner shipping companies (e.g., reduce fuel consumption and vessel emissions, ensure on-time arrival to each port of call, provide vessel scheduling strategies that capture sailing time variability, consider variable port handling times, increase profit, etc.) and terminal operators (e.g., decrease turnaround time of vessels, improve terminal productivity without significant capital investments, reduce possible vessel delays and associated penalties, ensure fast recovery in case of natural and man-made disasters, make the terminal competitive, maximize revenues, etc.). This dissertation proposes and models two alternatives for improving operations of marine container terminals: 1) a floaterm concept and 2) a new contractual agreement between terminal operators. The main difference between floaterm and conventional marine container terminals is that in the former case some of import and/or transshipment containers are handled by off-shore quay cranes and placed on container barges, which are further towed by push boats to assigned feeder vessels or floating yard. According to the new collaborative agreement, a dedicated marine container terminal operator can divert some of its vessels for the service at a multi-user terminal during specific time windows. Another part of dissertation focuses on enhancing operations of liner shipping services by introducing the following: 1) a new collaborative agreement between a liner shipping company and terminal operators and 2) a new framework for modeling uncertainty in liner shipping. A new collaborative mechanism assumes that each terminal operator is able to offer a set of handling rates to a liner shipping company, which may result in a substantial total route service cost reduction. The suggested framework for modeling uncertainty is expected to assist liner shipping companies in designing robust vessel schedules

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

An evolutionary approach to solving a new integrated quay crane assignment and quay crane scheduling mathematical model

This paper puts forward an integrated optimisation model that combines two distinct problems arising in container terminals, namely the Quay Crane Assignment Problem, and the Quay Crane Scheduling Problem. The model is of the mixed-integer programming type with the objective being to minimise the tardiness of vessels. Although exact solutions can be found to the problem using Branch-and-Cut, for instance, they are costly in time when instances are of realistic sizes. To overcome the computational burden of large scale instances, an adapted Genetic Algorithm, is used. Small to medium size instances of the combined model have been solved with both the Genetic Algorithm and the CPLEX implementation of Branch-and-Cut. Larger size instances, however, could only be solved approximately in acceptable times with the Genetic Algorithm. Computational results are included and discussed

Combined quay crane assignment and quay crane scheduling with crane inter-vessel movement and non-interference constraints

Integrated models of the quay crane assignment problem (QCAP) and the quay crane scheduling problem (QCSP) exist. However, they have shortcomings in that some do not allow movement of quay cranes between vessels, others do not take into account precedence relationships between tasks, and yet others do not avoid interference between quay cranes. Here, an integrated and comprehensive optimization model that combines the two distinct QCAP and QCSP problems which deals with the issues raised is put forward. The model is of the mixed-integer programming type with the objective being to minimize the difference between tardiness cost and earliness income based on finishing time and requested departure time for a vessel. Because of the extent of the model and the potential for even small problems to lead to large instances, exact methods can be prohibitive in computational time. For this reason an adapted genetic algorithm (GA) is implemented to cope with this computational burden. Experimental results obtained with branch-and-cut as implemented in CPLEX and GA for small to large-scale problem instances are presented. The paper also includes a review of the relevant literature