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

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

Multiship Crane Sequencing with Yard Congestion Constraints

Crane sequencing in container terminals determines the order of ship discharging and loading jobs that quay cranes (QCs) perform, so that the duration of a vessel's stay is minimized. The ship's load profile, berthing time, number of available bays, and QCs are considered. More important, clearance and yard congestion constraints need to be included, which, respectively, ensure that a minimum distance between adjacent QCs is observed and yard storage blocks are not overly accessed at any point in time. In sequencing for a single ship, a mixed-integer programming (MIP) model is proposed, and a heuristic approach based on the model is developed that produces good solutions. The model is then reformulated as a generalized set covering problem and solved exactly by branch and price (B&P). For multiship sequencing, the yard congestion constraints are relaxed in the spirit of Lagrangian relaxation, so that the problem decomposes by vessel into smaller subproblems solved by B&P. An efficient primal heuristic is also designed. Computational experiments reveal that large-scale problems can be solved in a 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

Integrated Berth Allocation and Quay Crane Assignment Problem: Set partitioning models and computational results

Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated Berth Allocation and Quay Crane Assignment Problem in seaport container terminals. We will extend the current state-of-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the effects of different discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models significantly improve the benchmark solutions of the current state-of-art optimal approaches

Berth Allocation Problem with Quay Crane Assignment for Container Terminals Based on Rolling-Horizon Strategy

In order to solve the large-scale integral dynamic scheduling of continuous berths and quay cranes problem, a method based on rolling-horizon strategy is proposed. A multiobjective optimization model that is established minimizes the total penalty costs considering vessels’ deviations to their preferred berthing positions, delayed times for berthing comparing to their estimated arrival times, and delayed times for departure comparing to their estimated departure times. Then, the scheduling process was divided into a set of continual scheduling interval according to the dynamic arrival sequences. Meanwhile, rolling-horizon strategies for setting rolling and frozen windows and the parameter updating strategy are designed. The input parameters of the model in the next rolling window are updated according to the optimal results of each time window which have been obtained. The model is solved by choosing appropriate rolling and freezing window lengths that represents the numbers of adjacent vessels in the sequence of calling vessels. The holistic optimal solution is obtained by gradually rolling and combining the results of each window. Finally, a case study indicated that the rolling schedule can solve large-scale scheduling problems, and the efficiency of the proposed approach relates to the size of rolling window, freeze ship quantity, and rolling frequency

Containership Load Planning with Crane Operations

Since the start of the containerization revolution in 1950's, not only the TEU capacity of the vessels has been increasing constantly, but also the number of fully cellular container ships has expanded substantially. Because of the tense competition among ports in recent years, improving the operational efficiency of ports has become an important issue in containership operations. Arrangement of containers both within the container terminal and on the containership play an important role in determining the berthing time. The berthing time of a containership is mainly composed of the unloading and loading time of containers. Containers in a containership are stored in stacks, making a container directly accessible only if it is on the top of one stack. The task of determining a good container arrangement to minimize the number of re-handlings while maintaining the ship's stability over several ports is called stowage planning, which is an everyday problem solved by ship planners. The horizontal distribution of the containers over the bays affects crane utilization and overall ship berthing time. In order to increase the terminal productivity and reduce the turnaround time, the stowage planning must conform to the berth design. Given the configuration of berths and cranes at each visiting port, the stowage planning must take into account the utilization of quay cranes as well as the reduction of unnecessary shifts to minimize the total time at all ports over the voyage. This dissertation introduces an optimization model to solve the stowage planning problem with crane utilization considerations. The optimization model covers a wide range of operational and structural constraints for containership load planning. In order to solve real-size problems, a meta-heuristic approach based on genetic algorithms is designed and implemented which embeds a crane split approximation routine. The genetic encoding is ultra-compact and represents grouping, sorting and assignment strategies that might be applied to form the stowage pattern. The evaluation procedure accounts for technical specification of the cranes as well as the crane split. Numerical results show that timely solution for ultra large size containerships can be obtained under different scenarios

Port Terminal Appointment Scheduling Problem

El constante aumento del transporte marítimo de los últimos años ha llevado a los operadores de terminales marítimas a investigar nuevas soluciones que aumenten su rendimiento. Un ejemplo actual es la resolución publicada por la Agencia del Petróleo de Brasil en julio de 2022, en la que destaca la importancia de contar con una metodología de programación de citas estructurada para organizar las operaciones buque-tierra.Esta investigación tiene tres objetivos principales: el primero de ellos es abordar el problema de programar citas desde diferentes perspectivas para ayudar al proceso de diseño de tales soluciones. El segundo es facilitar modelos de programación de citas que puedan ayudar a las terminales en sus procesos de optimización. El tercero es estudiar diferentes planteamientos sobre el valor de la información, diferentes plazos de programación, coordinación entre los equipos operativos y de programación, diferentes perfiles de congestión de la agenda, niveles de incertidumbre y normas de programación de atraque, entre otros.El reto consiste en encontrar un plan de cita optimizado que permita maximizar las ganancias de las terminales, considerando particularidades de cada solicitud de operación, incertidumbres en plazos de llegada y en procesamiento de los buques, los costes y ganancias vinculados a los contratos y la norma de secuenciación de atraques que utilizan los equipos operativos.Por un lado, existirán contribuciones en la parte de gestión a través de ideas que pueden impulsar el rendimiento de las terminales. Por otro lado, existirán aportaciones académicas a través de propuestas de modelos de programación de citas que incorporan la aleatoriedad en parámetros y consideran las llegadas como variables endógenas, conforme a diferentes perfiles de solapamiento de la agenda. Por tanto, se propondrán varias heurísticas, que abordarán los problemas de programación de citas aleatorios, enteros y no lineales (SINP, por sus siglas en inglés). Tienen en cuenta las solicitudes de los clientes, los acuerdos contractuales, las distribuciones de plazos de retraso / procesamiento y la norma predefinida de secuencia de atraques como insumos. En función de lo rentable que sean las operaciones, se define qué buques se aceptan o rechazan para operar, así como la fecha de la cita que se espera que se produzca.Debido a cuestiones de dimensionalidad, se propone una metodología de descomposición llamada ¿Cluster First, Schedule Second¿ (Primero agrupar, luego programar) con el fin de reducir el plazo de resolución. El problema principal se descompone en otros más pequeños que se resuelven de manera secuencial mediante la aproximación de la media muestral, de manera que la programación de cada grupo afecta a los siguientes. Los resultados de los modelos de optimización también se evalúan en un entorno de simulación de acontecimientos discreto que reproduce varias restricciones presentes en terminales congestionadas.Por último, se propondrá un conjunto de diez preguntas de investigación que guiarán todo el proceso de experimentación utilizado para probar diferentes temas sobre el problema de programación de citas de terminales portuarias. Entre las conclusiones, cabe destacar que los resultados muestran que las terminales especialmente congestionadas pueden lograr mejoras significativas en beneficios con medidas como las que se presentarán. También, se puede estudiar dar incentivos a los clientes para obtener más información por adelantado sobre la operación, así como aumentar la flexibilidad en la disponibilidad de días. Responder a los clientes de forma estadística dio mejores resultados, puesto que la terminal puede tomar la decisión con toda la información. En caso de que los clientes valoren respuestas dinámicas, una sugerencia podría ser ofrecerles un servicio superior para reducir el impacto general. En términos de normas de atraque, el método FIFO presentó buenos resultados en el caso de terminales con agendas congestionadas, mientras que la norma por programación fue mejor en situaciones con poco solapamiento. En el caso de llegadas al mismo tiempo, se recomienda priorizar en función de las desviaciones más pequeñas. Además, un resultado sorprendente es que las incertidumbres en las llegadas pueden, en algunos casos, ser beneficiosas, pero aceptar ventanas de tiempo en lugar de una fecha programada no lo es.<br /