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 rolling horizon approach for the integrated multi-quays berth allocation and crane assignment problem for bulk ports

In this paper, an efficient rolling horizon-based heuristic is presented to solve the integrated berth allocation and crane assignment problem in bulk ports. We were guided by a real case study of a multi-terminal port, owned by our Moroccan industrial partner, under several restrictions as high tides and installation’s availability. First, we proposed a mixed integer programming model for the problem. Then, we investigated a strategy to dissipate the congestion within the presented rolling horizon. A variety of experiments were conducted, and the obtained results show that the proposed methods were efficient from a practical point of view

Optimising discrete dynamic berth allocations in seaports using a Levy Flight based meta-heuristic

Seaports play a vital role in our everyday life: they handle 90% of our world trade goods. Improving seaports' efficiency means improving the efficiency of sending and receiving our goods. In seaports, one of the most important and most expensive operations is how to allocate vessels to berths. In this paper, we solve this problem by proposing a new meta-heuristic, which combines the nature-inspired Levy Flight random walk with local search, while taking into account tidal windows. With our algorithm, we meet the following goals: (i) to minimise the cost of all vessels while staying in the port, and (ii) to schedule available berths for the arriving vessels taking into account a multi-tidal planning horizon. In comparison with the state-of-the-art exact method using commercial solver and a competitive heuristic, the computational results prove our approach guarantees feasibility of solutions for all the problem instances and is able to find good solutions in a short amount of time, especially for large-scale instances. We also compare our results to an existing state-of-the-art Particle Swarm Optimisation and our work produces significantly better performances on all the test instances

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Funding Information: Funding: This research was funded by the National Natural Science Foundation of China under Grant U1964201 and Grant U21B6001, the Major Scientific and Technological Special Project of Hei-longjiang Province under Grant 2021ZX05A01, the Heilongjiang Natural Science Foundation under Grant LH2019F020, and the Major Scientific and Technological Research Project of Ningbo under Grant 2021Z040. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.publishersversionpublishe

Berth Scheduling at Seaports: Meta-Heuristics and Simulation

This research aims to develop realistic solutions to enhance the efficiency of port operations. By conducting a comprehensive literature review on logistic problems at seaports, some important gaps have been identified for the first time. The following contributions are made in order to close some of the existing gaps. Firstly, this thesis identifies important realistic features which have not been well-studied in current academic research of berth planning. This thesis then aims to solve a discrete dynamic Berth allocation problem (BAP) while taking tidal constraints into account. As an important feature when dealing with realistic scheduling, changing tides have not been well-considered in BAPs. To the best of our knowledge, there is no existing work using meta-heuristics to tackle the BAP with multiple tides that can provide feasible solutions for all the test cases. We propose one single-point meta-heuristic and one population-based meta-heuristic. With our algorithms, we meet the following goals: (i) to minimise the cost of all vessels while staying in the port, and (ii) to schedule available berths for the arriving vessels taking into account a multi-tidal planning horizon. Comprehensive experiments are conducted in order to analyse the strengths and weaknesses of the algorithms and compare with both exact and approximate methods. Furthermore, lacking tools for examining existing algorithms for different optimisation problems and simulating real-world scenarios is identified as another gap in this study. This thesis develops a discrete-event simulation framework. The framework is able to generate test cases for different problems and provide visualisations. With this framework, contributions include assessing the performance of different algorithms for optimisation problems and benchmarking optimisation problems

Simulation of In Bag Fertilizer Loading Process In Port of Petrokimia Gresik to Increase Loading Rate

Pelabuhan Petrokimia Gresik is one of the element to support the goverment regarding to maintain stability of fertilizer loading speed. The issue that occurs at Pelabuhan Petrokimia Gresik is the low speed of loading of fertilizer in the bag for the past 4 years. The low speed of loading fertilizer in bag tends to affect its cost. This study aims to simulate the process of loading fertilizer in bag at the Port of Petrokimia Gresik in order to find out the factors which influence the speed deterioration of fertilizer bagging process and applicable alternative scenarios to improve the process of loading fertilizer in bag. Simulations were executed using discrete simulation methods. The results show that loading speed downturn are influenced by significant factors as follows: high loading preparation time at the dock, which consumes 29,35% of loading activity; the high loading time at the dock, which reaches 23,15% of loading activity; also the extensive duration of truck commute from the warehouse to the pier which utilizes 22,95% of loading activity. This study evaluate 7 alternative improvement scenarios. According to ROI perspective, it is preeminent to additionally put field internal control personnel at each shift when loading fertilizer in bag, which can increase the loading speed to 668,29 tons / day, of investment (ROI) of 68% will be obtained within 1 year and the payback period will approximately result in 0,60 years. The best scenario in terms of loading speed is to add internal control personnel and apply a new method of loading fertilizer using a sling bag with an increase in loading rate to 804.17 tons / day, following ROI value of 23% within one year and a PP value of 0.81 year

Berth scheduling problem considering traffic limitations in the navigation channel

In view of the trend of upsizing ships, the physical limitations of natural waterways,huge expenses, and unsustainable environmental impact of channel widening, this paper aims toprovide a cost-efficient but applicable solution to improve the operational performance of containerterminals that are enduring inefficiency caused by channel traffic limitations. We propose a novelberth scheduling problem considering the traffic limitations in the navigation channel, which appearsin many cases including insufficient channel width, bad weather, poor visibility, channel accidents,maintenance dredging of the navigation channel, large vessels passing through the channel, andso on. To optimally utilize the berth and improve the service quality for customers, we proposea mixed-integer linear programming model to formulate the berth scheduling problem under theone-way ship traffic rule in the navigation channel. Furthermore, we develop a more generalizedmodel which can cope with hybrid traffic in the navigation channel including one-way traffic,two-way traffic, and temporary closure of the navigation channel. For large-scale problems, a hybridsimulated annealing algorithm, which employs a problem-specific heuristic, is presented to reducethe computational time. Computational experiments are performed to evaluate the effectiveness andpracticability of the proposed method

Disruption Response Support For Inland Waterway Transportation

Motivated by the critical role of the inland waterways in the United States\u27 transportation system, this dissertation research focuses on pre- and post- disruption response support when the inland waterway navigation system is disrupted by a natural or manmade event. Following a comprehensive literature review, four research contributions are achieved. The first research contribution formulates and solves a cargo prioritization and terminal allocation problem (CPTAP) that minimizes total value loss of the disrupted barge cargoes on the inland waterway transportation system. It is tailored for maritime transportation stakeholders whose disaster response plans seek to mitigate negative economic and societal impacts. A genetic algorithm (GA)-based heuristic is developed and tested to solve realistically-sized instances of CPTAP. The second research contribution develops and examines a tabu search (TS) heuristic as an improved solution approach to CPTAP. Different from GA\u27s population search approach, the TS heuristic uses the local search to find improved solutions to CPTAP in less computation time. The third research contribution assesses cargo value decreasing rates (CVDRs) through a Value-focused Thinking based methodology. The CVDR is a vital parameter to the general cargo prioritization modeling as well as specifically for the CPTAP model for inland waterways developed here. The fourth research contribution develops a multi-attribute decision model based on the Analytic Hierarchy Process that integrates tangible and intangible factors in prioritizing cargo after an inland waterway disruption. This contribution allows for consideration of subjective, qualitative attributes in addition to the pure quantitative CPTAP approach explored in the first two research contributions

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 /