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

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

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 model to determine ratios between quay, yard and intra-terminal transfer equipment in an integrated container handling system

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottle-necks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research