Integrated intelligent techniques for remarshaling and berthing in maritime terminals

[EN] Maritime container terminals are facilities where cargo containers are transshipped between ships or between ships and land vehicles (trucks or trains). These terminals involve a large number of complex and combinatorial problems. Two important problems are the container stacking problem and the berth allocation problem. Both problems are generally managed and solved independently but there exist a relationship that must be taken into account to optimize the whole process. The terminal operator normally demands all containers bound for an incoming vessel to be ready in the terminal before its arrival. Similarly, customers (i.e., vessel owners) expect prompt berthing of their vessels upon arrival. This is particularly important for vessels from priority customers who may have been guaranteed berth-on-arrival service in their contract with the terminal operator. To this end, both problems must be interrelated. In this paper, a set of artificial intelligence based-techniques for solving both problems is presented. We develop a planning technique for solving the container stacking problem and a set of optimized allocation algorithms for solving the berth allocation problem independently. Finally we have developed an architecture to solve both problems in an integrated way. Thus, an algorithm for solving the berth allocation problem generates an optimized order of vessels to be served meanwhile our container stacking problem heuristics calculate the minimum number of reshuffles needed to allocate the containers in the appropriate place for the obtained ordering of vessels. Thus combined optimal solutions can be calculated and the terminal operator could decide which solution is more appropriate in each case. These techniques will minimize disruptions and facilitate planning in container terminals. © 2011 Elsevier Ltd. All rights reserved.This work has been partially supported by the research projects TIN2007-67943-C02-01 (Min. de Educacion y Ciencia, Spain-FEDER), and P19/08 (Min. de Fomento, Spain-FEDER), as well as with the collaboration of the maritime container terminal MSC (Mediterranean Shipping Company S.A.).Salido Gregorio, MA.; Rodríguez Molins, M.; Barber Sanchís, F. (2011). Integrated intelligent techniques for remarshaling and berthing in maritime terminals. ADVANCED ENGINEERING INFORMATICS. 25(3):435-451. https://doi.org/10.1016/j.aei.2010.10.001S43545125

Robust scheduling for Berth Allocation and Quay Crane Assignment Problem

[EN] Decision makers must face the dynamism and uncertainty of real-world environments when they need to solve the scheduling problems. Different incidences or breakdowns, for example, initial data could change or some resources could become unavailable, may eventually cause the infeasibility of the obtained schedule. To overcome this issue, a robust model and a proactive approach are presented for scheduling problems without any previous knowledge about incidences. This paper is based on proportionally distributing operational buffers among the tasks. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems. The dynamism and uncertainty are managed by assessing the robustness of the schedules. The robustness is introduced by means of operational buffer times to absorb those unknown incidences or breakdowns. Therefore, this problem becomes a multiobjective combinatorial optimization problem that aims to minimize the total service time, to maximize the buffer times, and to minimize the standard deviation of the buffer times. To this end, a mathematical model and a new hybrid multiobjective metaheuristic is presented and compared with two well-known multiobjective genetic algorithms: NSGAII and SPEA2+.This work has been partially supported by by the Spanish Government under research project MINECO TIN2013-46511-C2-1-P, the project PIRSES-GA-2011-294931 (FP7-PEOPLE-2011-IRSES), and the predoctoral FPU fellowship (AP2010-4405).Rodríguez Molins, M.; Salido Gregorio, MA.; Barber Sanchís, F. (2014). Robust scheduling for Berth Allocation and Quay Crane Assignment Problem. Mathematical Problems in Engineering. 2014(1):1-17. https://doi.org/10.1155/2014/834927S11720141Imai, A., Chen, H. C., Nishimura, E., & Papadimitriou, S. (2008). The simultaneous berth and quay crane allocation problem. Transportation Research Part E: Logistics and Transportation Review, 44(5), 900-920. doi:10.1016/j.tre.2007.03.003Hu, Q.-M., Hu, Z.-H., & Du, Y. (2014). Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Computers & Industrial Engineering, 70, 1-10. doi:10.1016/j.cie.2014.01.003Salido, M. A., Rodriguez-Molins, M., & Barber, F. (2011). Integrated intelligent techniques for remarshaling and berthing in maritime terminals. Advanced Engineering Informatics, 25(3), 435-451. doi:10.1016/j.aei.2010.10.001Rodriguez-Molins, M., Salido, M. A., & Barber, F. (2013). A GRASP-based metaheuristic for the Berth Allocation Problem and the Quay Crane Assignment Problem by managing vessel cargo holds. Applied Intelligence, 40(2), 273-290. doi:10.1007/s10489-013-0462-4Stahlbock, R., & Voß, S. (2007). Operations research at container terminals: a literature update. OR Spectrum, 30(1), 1-52. doi:10.1007/s00291-007-0100-9Lim, A. (1998). The berth planning problem. Operations Research Letters, 22(2-3), 105-110. doi:10.1016/s0167-6377(98)00010-8Bierwirth, C., & Meisel, F. (2010). A survey of berth allocation and quay crane scheduling problems in container terminals. European Journal of Operational Research, 202(3), 615-627. doi:10.1016/j.ejor.2009.05.031Kim, K. H., & Moon, K. C. (2003). Berth scheduling by simulated annealing. Transportation Research Part B: Methodological, 37(6), 541-560. doi:10.1016/s0191-2615(02)00027-9Giallombardo, G., Moccia, L., Salani, M., & Vacca, I. (2010). Modeling and solving the Tactical Berth Allocation Problem. Transportation Research Part B: Methodological, 44(2), 232-245. doi:10.1016/j.trb.2009.07.003Liang, C., Guo, J., & Yang, Y. (2009). Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning. Journal of Intelligent Manufacturing, 22(3), 471-479. doi:10.1007/s10845-009-0304-8Diabat, A., & Theodorou, E. (2014). An Integrated Quay Crane Assignment and Scheduling Problem. Computers & Industrial Engineering, 73, 115-123. doi:10.1016/j.cie.2013.12.012Park, Y.-M., & Kim, K. H. (2003). A scheduling method for Berth and Quay cranes. OR Spectrum, 25(1), 1-23. doi:10.1007/s00291-002-0109-zZhang, C., Zheng, L., Zhang, Z., Shi, L., & Armstrong, A. J. (2010). The allocation of berths and quay cranes by using a sub-gradient optimization technique. Computers & Industrial Engineering, 58(1), 40-50. doi:10.1016/j.cie.2009.08.002Lambrechts, O., Demeulemeester, E., & Herroelen, W. (2007). Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. Journal of Scheduling, 11(2), 121-136. doi:10.1007/s10951-007-0021-0Hendriks, M., Laumanns, M., Lefeber, E., & Udding, J. T. (2010). Robust cyclic berth planning of container vessels. OR Spectrum, 32(3), 501-517. doi:10.1007/s00291-010-0198-zHan, X., Lu, Z., & Xi, L. (2010). A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time. European Journal of Operational Research, 207(3), 1327-1340. doi:10.1016/j.ejor.2010.07.018Xu, Y., Chen, Q., & Quan, X. (2011). Robust berth scheduling with uncertain vessel delay and handling time. Annals of Operations Research, 192(1), 123-140. doi:10.1007/s10479-010-0820-0Zhen, L., & Chang, D.-F. (2012). A bi-objective model for robust berth allocation scheduling. Computers & Industrial Engineering, 63(1), 262-273. doi:10.1016/j.cie.2012.03.003Blum, C., Puchinger, J., Raidl, G. R., & Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, 11(6), 4135-4151. doi:10.1016/j.asoc.2011.02.032Ehrgott, M., & Gandibleux, X. (2008). Hybrid Metaheuristics for Multi-objective Combinatorial Optimization. Studies in Computational Intelligence, 221-259. doi:10.1007/978-3-540-78295-7_8Hanafi, R., & Kozan, E. (2014). A hybrid constructive heuristic and simulated annealing for railway crew scheduling. Computers & Industrial Engineering, 70, 11-19. doi:10.1016/j.cie.2014.01.002Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Kim, M., Hiroyasu, T., Miki, M., & Watanabe, S. (2004). SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2. Parallel Problem Solving from Nature - PPSN VIII, 742-751. doi:10.1007/978-3-540-30217-9_75Rodriguez-Molins, M., Ingolotti, L., Barber, F., Salido, M. A., Sierra, M. R., & Puente, J. (2014). A genetic algorithm for robust berth allocation and quay crane assignment. Progress in Artificial Intelligence, 2(4), 177-192. doi:10.1007/s13748-014-0056-3Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32-49. doi:10.1016/j.swevo.2011.03.001Bandyopadhyay, S., Saha, S., Maulik, U., & Deb, K. (2008). A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA. IEEE Transactions on Evolutionary Computation, 12(3), 269-283. doi:10.1109/tevc.2007.900837While, L., Bradstreet, L., & Barone, L. (2012). A Fast Way of Calculating Exact Hypervolumes. IEEE Transactions on Evolutionary Computation, 16(1), 86-95. doi:10.1109/tevc.2010.207729

Optimisation of berth and quay crane allocation in port container terminals

U radu su istraženi operativni logistički problemi koji se javljaju prilikom obavljanja tehnoloških procesa u obalnom podsustavu lučkih kontejnerskih terminala. Cilj istraživanja je strukturirati optimizacijski model u svrhu potpore taktičkom planiranju na lučkim kontejnerskim terminalima do 1 milijun kontejnera godišnje koji su tipični za šire regionalno okruženje. U užem smislu optimizacijski model rješava problem kod različitih tlocrtnih konfiguracija lučkih pristana gdje su dva pristana postavljena prostorno neovisno. Kao rezultat istraživanja razvijen je integrirani model optimizacije kod neovisno postavljenih pristana za kompletan obalni podsustav koji objedinjuje tri tipska problema: problem dodjele veza, problem raspodjele dizalica i problem redoslijeda prekrcajnih operacija, a predstavljen je optimizacijskim procesom u tri faze: inicijalizacije, raspoređivanja i usklađivanja. Tipično obilježje procesa je izbor optimalnog scenarija prekrcaja koji se dobije kao rezultat optimizacijske funkcije definirane ukupnim najkraćim vremenom boravka brodova u luci i optimalnog iskorištenja kapaciteta prihvatnih i prekrcajnih resursa. Kao rezultat optimizacije za svaki brod se odabiru tri najbolja scenarija prekrcaja, ovisno o rasporedu tereta na brodu i specifičnim zahtjevima brodara. Za razvoj modela korištene su metode operacijskih istraživanja: metode linearnog i cjelobrojnog programiranja te metoda asignacije. Model je testiran na temelju simuliranih dolazaka brodova na uzorku od 100 brodova raspoređenim u grupe po 10, 15 i 20 brodova sukladno očekivanom vremenskom horizontu planiranja. Dobiveni rezultati pokazuju primjenljivost modela za rješavanje taktičko-operativnih problema u obalnom podsustavu ciljanih lučkih kontejnerskih terminala.The present paper addresses the tactical logistical problems in the seaside subsystem of port container terminals. The research goal is to structure optimization model to support tactical planning in maritime container terminals with annual capacity below 1 million TEU per year, which are typical for wider regional environment. In the narrow sense, the optimization model solves the problem with different basin layouts where two quays are placed independently. As a result of research, an integrated optimization model has been developed for the seaside subsystem that combines three typical logistical problems: Berth Allocation Problem, Quay Crane Assignment Problem and Quay Crane Scheduling Problem. The model is represented by the optimization process to takes up in three stages: initialization, allocation and synchronization. A typical feature of the process is the selection of the optimal handling scenario which is obtained as a result of the optimization function defined by the minimum total service time of ships in port and the optimum utilization of the quay cranes capacity. As a result of the optimization, for each ship the best suite handling scenario is selected among three pre-defined options, depending on the stowage location onboard and depending on specific requirements of shippers. The methods of operations research has been used in the developing models, that is, methods of linear and integer programming and the assignment method. The model is tested on a sample of 100 virtually generated vessels with simulated stochastic times of arrivals and cargo handling demand. Vessels are arranged in groups of 10, 15 and 20 in accordance with the expected time horizon planning. The results show the applicability of the model for solving tactical operational problems in the seaside subsystem of targeted port container terminals

Optimisation of berth and quay crane allocation in port container terminals

U radu su istraženi operativni logistički problemi koji se javljaju prilikom obavljanja tehnoloških procesa u obalnom podsustavu lučkih kontejnerskih terminala. Cilj istraživanja je strukturirati optimizacijski model u svrhu potpore taktičkom planiranju na lučkim kontejnerskim terminalima do 1 milijun kontejnera godišnje koji su tipični za šire regionalno okruženje. U užem smislu optimizacijski model rješava problem kod različitih tlocrtnih konfiguracija lučkih pristana gdje su dva pristana postavljena prostorno neovisno. Kao rezultat istraživanja razvijen je integrirani model optimizacije kod neovisno postavljenih pristana za kompletan obalni podsustav koji objedinjuje tri tipska problema: problem dodjele veza, problem raspodjele dizalica i problem redoslijeda prekrcajnih operacija, a predstavljen je optimizacijskim procesom u tri faze: inicijalizacije, raspoređivanja i usklađivanja. Tipično obilježje procesa je izbor optimalnog scenarija prekrcaja koji se dobije kao rezultat optimizacijske funkcije definirane ukupnim najkraćim vremenom boravka brodova u luci i optimalnog iskorištenja kapaciteta prihvatnih i prekrcajnih resursa. Kao rezultat optimizacije za svaki brod se odabiru tri najbolja scenarija prekrcaja, ovisno o rasporedu tereta na brodu i specifičnim zahtjevima brodara. Za razvoj modela korištene su metode operacijskih istraživanja: metode linearnog i cjelobrojnog programiranja te metoda asignacije. Model je testiran na temelju simuliranih dolazaka brodova na uzorku od 100 brodova raspoređenim u grupe po 10, 15 i 20 brodova sukladno očekivanom vremenskom horizontu planiranja. Dobiveni rezultati pokazuju primjenljivost modela za rješavanje taktičko-operativnih problema u obalnom podsustavu ciljanih lučkih kontejnerskih terminala.The present paper addresses the tactical logistical problems in the seaside subsystem of port container terminals. The research goal is to structure optimization model to support tactical planning in maritime container terminals with annual capacity below 1 million TEU per year, which are typical for wider regional environment. In the narrow sense, the optimization model solves the problem with different basin layouts where two quays are placed independently. As a result of research, an integrated optimization model has been developed for the seaside subsystem that combines three typical logistical problems: Berth Allocation Problem, Quay Crane Assignment Problem and Quay Crane Scheduling Problem. The model is represented by the optimization process to takes up in three stages: initialization, allocation and synchronization. A typical feature of the process is the selection of the optimal handling scenario which is obtained as a result of the optimization function defined by the minimum total service time of ships in port and the optimum utilization of the quay cranes capacity. As a result of the optimization, for each ship the best suite handling scenario is selected among three pre-defined options, depending on the stowage location onboard and depending on specific requirements of shippers. The methods of operations research has been used in the developing models, that is, methods of linear and integer programming and the assignment method. The model is tested on a sample of 100 virtually generated vessels with simulated stochastic times of arrivals and cargo handling demand. Vessels are arranged in groups of 10, 15 and 20 in accordance with the expected time horizon planning. The results show the applicability of the model for solving tactical operational problems in the seaside subsystem of targeted port container terminals

Optimisation of berth and quay crane allocation in port container terminals

U radu su istraženi operativni logistički problemi koji se javljaju prilikom obavljanja tehnoloških procesa u obalnom podsustavu lučkih kontejnerskih terminala. Cilj istraživanja je strukturirati optimizacijski model u svrhu potpore taktičkom planiranju na lučkim kontejnerskim terminalima do 1 milijun kontejnera godišnje koji su tipični za šire regionalno okruženje. U užem smislu optimizacijski model rješava problem kod različitih tlocrtnih konfiguracija lučkih pristana gdje su dva pristana postavljena prostorno neovisno. Kao rezultat istraživanja razvijen je integrirani model optimizacije kod neovisno postavljenih pristana za kompletan obalni podsustav koji objedinjuje tri tipska problema: problem dodjele veza, problem raspodjele dizalica i problem redoslijeda prekrcajnih operacija, a predstavljen je optimizacijskim procesom u tri faze: inicijalizacije, raspoređivanja i usklađivanja. Tipično obilježje procesa je izbor optimalnog scenarija prekrcaja koji se dobije kao rezultat optimizacijske funkcije definirane ukupnim najkraćim vremenom boravka brodova u luci i optimalnog iskorištenja kapaciteta prihvatnih i prekrcajnih resursa. Kao rezultat optimizacije za svaki brod se odabiru tri najbolja scenarija prekrcaja, ovisno o rasporedu tereta na brodu i specifičnim zahtjevima brodara. Za razvoj modela korištene su metode operacijskih istraživanja: metode linearnog i cjelobrojnog programiranja te metoda asignacije. Model je testiran na temelju simuliranih dolazaka brodova na uzorku od 100 brodova raspoređenim u grupe po 10, 15 i 20 brodova sukladno očekivanom vremenskom horizontu planiranja. Dobiveni rezultati pokazuju primjenljivost modela za rješavanje taktičko-operativnih problema u obalnom podsustavu ciljanih lučkih kontejnerskih terminala.The present paper addresses the tactical logistical problems in the seaside subsystem of port container terminals. The research goal is to structure optimization model to support tactical planning in maritime container terminals with annual capacity below 1 million TEU per year, which are typical for wider regional environment. In the narrow sense, the optimization model solves the problem with different basin layouts where two quays are placed independently. As a result of research, an integrated optimization model has been developed for the seaside subsystem that combines three typical logistical problems: Berth Allocation Problem, Quay Crane Assignment Problem and Quay Crane Scheduling Problem. The model is represented by the optimization process to takes up in three stages: initialization, allocation and synchronization. A typical feature of the process is the selection of the optimal handling scenario which is obtained as a result of the optimization function defined by the minimum total service time of ships in port and the optimum utilization of the quay cranes capacity. As a result of the optimization, for each ship the best suite handling scenario is selected among three pre-defined options, depending on the stowage location onboard and depending on specific requirements of shippers. The methods of operations research has been used in the developing models, that is, methods of linear and integer programming and the assignment method. The model is tested on a sample of 100 virtually generated vessels with simulated stochastic times of arrivals and cargo handling demand. Vessels are arranged in groups of 10, 15 and 20 in accordance with the expected time horizon planning. The results show the applicability of the model for solving tactical operational problems in the seaside subsystem of targeted port container terminals