Berth allocation planning in Seville inland port by simulation and optimisation

We study the problems associated with allocating berths for containerships in the port of Seville. It is the only inland port in Spain and it is located on the Guadalquivir River. This paper addresses the berth allocation planning problems using simulation and optimisation with Arena software. We propose a mathematical model and develop a heuristic procedure based on genetic algorithm to solve non-linear problems. Allocation planning aims to minimise the total service time for each ship and considers a first-come-first-served allocation strategy. We conduct a large amount of computational experiments which show that the proposed model improves the current berth management strategy

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

A modified sailfish optimizer to solve dynamic berth allocation problem in conventional container terminal

During the past two decades, there has been an increase on maritime freight traffic particularly in container flow. Thus, the Berth Allocation Problem (BAP) can be considered among the primary optimization problems encountered in port terminals. In this paper, we address the Dynamic Berth Allocation Problem (DBAP) in a conventional layout terminal which differs from the popular discrete layout terminal in that each berth can serve multiple vessels simultaneously if their total length is equal or less than the berth length. Then, a Modified Sailfish Optimizer (MSFO) meta-heuristic based on hunting sailfish behavior is developed as an alternative for solving this problem. Finally, computational experiments and comparisons are presented to show the efficiency of our method against other methods presented in the literature in one hand. We also discuss the productivity of a container terminal based on different scenarios which can happen

Systematic Review of Literature on Dry Port Concept Evolution

Dry port plays an important role in supply chain management and mitigates seaport problems. The aim of this paper is to review the dry port concept over the different phases. Today there are different types of dry ports, different interpretations on the dry port life cycle, and different relations with seaport. We will provide a clear vision on the concept development and the advantages that can be added to the seaport and transportation flow. Then, the study will show the evolution of the research community interest on the concept. In the first step, we will briefly present all the challenges faced by seaports today. Next, we will undertake a systematic literature review in order to provide a global vision able to answer questions concerning dry port concept, types, research evolution. Finally, we will present some research topics that open for us at the dry port seaport system

Alocação de navios em portos marítimos : aplicação de metaheurísticas

Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e EmpresarialA alocação de navios nos cais marítimos é uma das principais tarefas do processo de gestão de um porto marítimo. Por essa razão, o número de estudos relacionados com o problema de alocação de navios aumentou drasticamente nos últimos anos. A resolução deste problema permite ao porto marítimo melhorar a eficiência de utilização do seu cais, a satisfação dos clientes e aumentar o rendimento do próprio porto, conduzindo assim a um aumento da sua competitividade. Tendo em conta que o problema de alocação de navios do tipo contínuo é considerado um problema NP-difícil, foram tidas em consideração três heurísticas construtivas que, dada uma sequência de inserção, inserem os navios no diagrama tempo-espaço que representa graficamente qualquer solução admissível do problema de alocação de navios. Duas dessas heurísticas, Upper Allocation e Intermedium Allocation, foram desenvolvidas com base na tradicional heurística Bottom-Left. Apesar das heurísticas construtivas desenvolvidas permitirem obter rapidamente uma solução admissível para o problema de alocação de navios e, em particular, a heurística Upper Allocation melhorar os resultados obtidos pela heurística tradicional Bottom-Left, estas não possuem parâmetros aleatórios que permitam obter diferentes soluções. Posto isto, foram desenvolvidas duas metaheurísticas, a Squeaky Wheel Optimization e a Tabu Search with Full Memory, cujo principal objetivo consiste em obter melhores soluções através da construção de diferentes sequências de inserção. Com base nos resultados computacionais obtidos, concluiu-se que a metaheurística Tabu Search with Full Memory apresentou melhores resultados do que a metaheurística Squeaky Wheel Optimization, quer para instâncias de pequena e média dimensão quer para instâncias de maior dimensão.The allocation of ships to maritime quays is considered one of the main tasks in the management process of a seaport. As a result, the number of studies related to the berth allocation problem has therefore drastically increased. Solving this problem allows the seaport to improve the efficiency of its quay utilisation, improve customer satisfaction and increase the income of the port itself, thus leading to an increase on its competitiveness. Bearing in mind that the berth allocation problem is considered NP-hard, three constructive heuristics were considered with the aim of, given an insertion sequence, inserting the ships into the time-space diagram that graphically represents any feasible solution to the berth allocation problem. Two of the heuristics, Upper Allocation and Intermedium Allocation, were developed based on the traditional Bottom-Left heuristic. Although the constructive heuristics developed make it possible to quickly obtain a feasible solution to the berth allocation problem and, in particular, the Upper Allocation heuristic improves the results obtained by the traditional Bottom-Left heuristic, they do not have random parameters that allow different solutions to be obtained. That said, two metaheuristics were developed, Squeaky Wheel Optimisation and Tabu Search with Full Memory, whose main objective is to obtain better solutions by constructing different insertion sequences. Based on the computational results, it was concluded that the Tabu Search with Full Memory metaheuristic obtained better results than the Squeaky Wheel Optimisation metaheuristic, both for small and medium instances and for larger instances.info:eu-repo/semantics/publishedVersio

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

Kerjasama Pemanfaatan Prasarana dan Sarana Terminal Dalam Upaya Mengurangi Waktu Pelayanan Kapal Di Terminal Peti Kemas

Berth allocation problem merupakan permasalahan yang kompleks karena adanya faktor ketidakpastian (uncertainty) yang menyebabkan kedatangan kapal di pelabuhan sulit untuk diprediksi dan seringkali terlambat dari jadwal yang telah ditentukan. Keterlambatan kedatangan kapal mengakibatkan sumber daya yang sudah dipersiapkan menjadi menganggur. Operator terminal harus menyusun jadwal ulang untuk mengalokasikan kapal yang mengalami keterlambatan. Apabila sumber daya tidak tersedia maka kapal harus menunggu (antri) sampai dermaga tersedia. Berth allocation tidak semata-mata hanya mengalokasikan kapal ke dermaga, tetapi juga mengalokasikan sumber daya lainnya seperti crane, yard, RTG, dan alat transportasi. Untuk pelabuhan yang memiliki lebih dari satu terminal yang dioperasikan oleh operator berbeda dimana setiap terminal menerapkan sistem windows slot, setiap terminal memiliki potensi pada saat yang sama di satu terminal terjadi kekurangan (shortage) dan terminal lain terjadi kelebihan (surplus) sumber daya. Oleh karena itu dibutuhkan strategi untuk menghadapi kondisi tersebut. Salah satu strategi yang diusulkan adalah dengan melakukan kerja sama atau kolaborasi. Pada kondisi eksisting shipping lines yang memiliki windows slot di satu terminal hanya bisa berthing dan bongkar muat menggunakan sumber daya yang dimiliki terminal tersebut. Apabila seluruh dermaga dan sumber daya di terminal tersebut sedang digunakan, maka kapal yang datang harus menunggu dan antri sampai dermaga tersedia, meskipun di terminal lain terdapat dermaga yang tidak digunakan, demikian juga sebaliknya. Strategi kolaborasi memungkinkan setiap kapal bisa berthing di setiap terminal meskipun kapal tersebut memiliki windows di terminal yang berbeda. Dalam penelitian ini dikembangkan model simultaneous berth allocation problem dengan strategi kolaborasi. Karena sistem yang dimodelkan relatif kompleks dan mengandung unsur ketidakpastian maka dalam studi ini digunakan permodelan discrete event simulation. Beberapa skenario diusulkan dan dipilih skenario terbaik yang terbaik. Skenario ditentukan berdasarkan kombinasi empat faktor, yaitu service order, berth-yard, crane dan strategy, dimana setiap faktor memiliki 2 level. Dengan menggunakan konsep full factorial design (2k factorial design) dihasilkan sebanyak 16 skenario. Skenario pertama merupakan kondisi eksisting yang dijadikan sebagai baseline untuk menentukan skenario terbaik yang ditentukan berdasarkan dua respon, yaitu waktu (waiting time, handling time, turnaround time) dan jumlah kapal yang menunggu. Berdasarkan hasil simulasi diperoleh skenario terbaik dengan kombinasi service order secara menggunakan sistem prioritas, berth-yard secara independent, alokasi crane secara fixed, dan strategi yang digunakan adalah kolaborasi. Hasil simulasi menunjukkan bahwa kolaborasi dapat menciptakan keseimbangan operasi di terminal dengan load tinggi dan terminal dengan load rendah. Waiting time dan turnaround time di terminal dengan load tinggi menjadi lebih pendek, sedangkan di terminal dengan load rendah menjadi lebih panjang. Strategi kolaborasi dapat mengurangi jumlah kapal menunggu hingga 43.82 % per tahun, menurunkan waiting time sebesar 46.82%, dan menurunkan turnaround time sebesar 10.60% per kapal per kedatangan. Kolaborasi menimbulkan terjadinya shifting kapal dan container dari terminal load tinggi ke terminal load rendah. Pergeseran kapal dan container menyebabkan terjadinya perubahan performa finansial bagi kedua terminal. Untuk menghindarkan terjadinya kerugian bagi salah satu pihak, maka dibuat skema profit sharing atau profit redistribution. ================================================================================================================== Berth allocation problem is a complex problem because of the uncertainty factor that causes the arrival of the ship in the port is difficult to predict and often the arrival of the ship is late from the schedule.The ship's delays result in the resources already allocated for the vessel cannot be utilized. If the ship comes out of schedule, the terminal operator should re-schedule the ship, so the ship must wait until the berth is available. Berth allocation does not solely allocate ships to berth, but also allocates other resources such as cranes, yards, RTG and transportation. For ports that have more than one terminal operated by different operators and each terminal implements a windows system, each terminal has the potential at the same time in one terminal to have a shortage of resources and another terminal overload (surplus). Strategy is needed to deal with the condition. One of the proposed strategies is to collaborate between terminals. In the existing condition of shipping lines that have windows in one terminal can only berthing, loading and unloading using resources in the terminal. If all the resources at the terminal are in use, the arriving vessel will have to wait and queue until the berth is available, even in other terminals there are unused docks, and vice versa. The collaboration strategy allows each ship to berthing in every terminal even though it has windows in different terminals. The allocation of berth, crane and yard is an interrelated process so that the allocation cannot be done partially or gradually (multiphase). Partial and multiphase solutions are generally accomplished by completing the berth allocation in the first phase, and continued with the crane or yard allocation in the next phase. Multiphase solutions have drawbacks because they do not always result in optimal completion. The allocation of berth, crane and yard is an interrelated process so that the allocation cannot be done partially or gradually (multiphase). Partial and multiphase solutions are generally accomplished by completing the berth allocation in the first phase, and continued with the crane or yard allocation in the next phase. Multiphase solutions have drawbacks because they do not always result in optimal completion. The optimal crane allocation in the second phase can change the optimal berth allocation in the first phase. This research develops simultaneous berth allocation problem model with collaboration strategy. Because the modeled system is relatively complex and contains uncertainty factor, this study uses discrete event simulation model. In this simulation, 16 scenarios were obtained using the full factorial design concept (2k factorial design) from a combination of four factors: service order, berth-yard, crane and strategy, each factor has two levels. The first scenario is an existing condition that is used as a baseline to determine the best scenario. The best scenario is determined based on two responses, namely time (waiting time, handling time, turnaround time) and the number of ships waiting. Simulation results show that collaboration can create a balance of operations in terminals with high load and terminals with low load. Waiting time and turnaround time in terminals with high load becomes shorter, while in terminals with low load becomes longer. The collaboration strategy can reduce the number of ships waiting up to 43.82% per year, while the waiting time is reduced by 46.82%. Turnaround time decreased by 10.60% per ship per arrival. Collaboration creates unavoidable consequences of shifting ships and containers from high load terminals to low load terminals. Shifting vessels and containers leads to changes in financial performance for both terminals. In this research also created profit sharing scheme or profit redistribution to avoid losses for either party