The berth allocation and quay crane assignment problem with crane travel and setup times

This is an open access article under the CC BY-NC-ND licenseIn this paper, we propose a new approach for including quay crane travel and setup times in the berth allocation and quay crane assignment problem. We first develop a new mixed integer linear programming model (MILP) for the problem without setups (BACASP), in which berthing positions and times are considered as continuous variables. Several groups of valid inequalities are also set forth. Then, for the BACASP with crane travel and setup times, which we denote as BACASP-S, we propose two MILPs: the first is based on the previous BACASP formulation and the second on routing formulations. Due to the complexity of the BACASP-S, we also propose a genetic algorithm and an exact approach which combines various MILPs with the genetic algorithm. All methods and valid inequalities are computationally tested over two different sets of randomly generated instances. According to the results, the models and algorithms can optimally solve, in less than one hour, BACASP-S instances of up to 40 vessels within a quay one kilometer long and a time horizon of one week. Additionally, extensive experiments were conducted on a new large set of instances to assess the effect of various BACASP-S input parameters on the computation effort required to solve the problem. Ceteris paribus, the computational effort required seems to increase with decreasing number of cranes, while vessel processing times and crane setup times seem not to affect it.Ministerio de Ciencia e Innovación RTI2018-094940-B-I00Fondo Europeo de Desarrollo Regional PID2021 - 122344NB-I00Generalitat Valenciana CIGE/2022/057Agencia Estatal de Investigación (AEI) PID2020-114594GB-C2

Models and algorithms for berth allocation problems in port terminals

Seaports play a key role in maritime commerce and the global market economy. Goods of different kinds are carried in specialized vessels whose handling requires ad hoc port facilities. Port terminals comprise the quays, infrastructures, and services dedicated to handling the inbound and outbound cargo carried on vessels. Increasing seaborne trade and ever-greater competition between port terminals to attract more traffic have prompted new studies aimed at improving their quality of service while reducing costs. Most terminals implement operational planning to achieve more efficient usage of resources, and this poses new combinatorial optimization problems which have attracted increasing attention from the Operations Research community. One of the most important problems confronted at the quayside is the efficient allocation of quay space to the vessels calling at the terminal over time, also known as the Berth Allocation Problem. A closely related problem arising in terminals that specialize in container handling concerns the efficient assignment of quay cranes to vessels, which, together with quay space planning, leads to the Berth Allocation and Quay Crane Assignment Problem. These problems are known to be especially hard to solve, and therefore require designing methods capable of attaining good solutions in reasonable computation times. This thesis studies different variants of these problems considering well-known and new real-world aspects, such as terminals with multiple quays or irregular layouts. Mathematical programming and metaheuristics techniques are extensively used to devise tailored solution methods. In particular, new integer linear models and heuristic algorithms are developed to deal with problem instances of a broad range of sizes representing real situations. These methods are evaluated and compared with other state-of-the-art proposals through various computational experiments on different benchmark sets of instances. The results obtained show that the integer models proposed lead to optimal solutions on small instances in short computation times, while the heuristic algorithms obtain good solutions to both small and large instances. Therefore, this study proves to be an effective contribution to the efforts aimed at improving port efficiency and provides useful insights to better tackle similar combinatorial optimization problems

Genetic algorithm for integrated model of berth allocation problem and quay crane scheduling with noncrossing safety and distance constraint

Berth Allocation and Quay Crane Scheduling are the most important part of container terminal operations since berth and quay cranes are an interface of ocean-side and landside in any port container terminal operation. Their operations significantly influence the efficiency of port container terminals and need to be solved simultaneously. Based on the situation, this study focuses on an integrated model of Continuous Berth Allocation Problem and Quay Crane Scheduling Problem. A comprehensive analysis of safety distance for vessel and non-crossing constraint for quay crane is provided. There are two integrated model involved. For the first integrated model, non-crossing constraints are added wherein quay cranes cannot cross over each other since they are on the same track. The second integrated model is focused on the safety distance between vessels while berthing at the terminal and at the same time, quay crane remains not to cross each other. These two constraints were selected to ensure a realistic model based on the real situation at the port. The objective of this model is to minimise the processing time of vessels. A vessel's processing time is measured between arrival and departure including the waiting time to be berthed and servicing time. A new algorithm is developed to obtain the good solution. Genetic Algorithm is chosen as a method based on flexibility and can apply to any problems. There are three layers of algorithm that provide a wider search to the solution space for vessel list, berth list, and hold list developed in this study. The new Genetic Algorithm produced a better solution than the previous research, where the objective function decreases 5 to 12 percent. Numerical experiments were conducted and the results show that both integrated models are able to minimize the processing time of vessels and can solve problem quickly even involving a large number of vessels. Studies have found that the safety distance set as 5 percent of vessel length gives the best solution. By adding safety distance to the integrated model with non-crossing constraint, the result indicates no improvement in the model objective function due to increasing distance between vessels. The objective function increases in the range of 0.4 to 8.6 percent. However, the safety distance constraint is important for safety and realistic model based on the port’s real situation

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

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

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

Allocation Of Thesis Supervisor Using Genetic Algorithm

Allocation of thesis supervisor is a way to determine the students’ thesis supervisor, so the students get the appropriate supervisor and the number of students guided by each supervisor does not exceed the maximum capacity. Allocation of thesis supervisor is not an easy taskbecause each supervisor has a capacity of the students that have to be guided and the supervisors major should be in accordance with the student's major in project.Allocation of thesis supervisor is an optimization problem that minimizes the number of mismatches between supervisor’s major and student’s project, and the number of supervisors whose guidance exceeds the maximum capacity.One of the methods that can be used to solve this problem is genetic algorithm.Genetic algorithm can solve optimization problems well on complex or even difficult mathematical models.The crossover operator is one of the operator that determines the success of the genetic algorithm.The commonly used crossover operator is the one cut point crossover operator, while after previous studies the position crossover operator is a crossover operator that developed to resolve the scheduling issues.Therefore, this research compares crossover position operator and the crossover one cut point operator to solve the problem of the thesis supervisor allocation. According to the results of the experiments, the genetic algorithm using crossover one cut point operators get better results in allocation of thesis supervisor rather than genetic algorithm with a position crossover operator