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

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

A GRASP-based metaheuristic for the Berth Allocation Problem and the Quay Crane Assignment Problem by managing vessel cargo holds

Container terminals are open systems that generally serve as a transshipment zone between vessels and land vehicles. These terminals carry out a large number of planning and scheduling tasks. In this paper, we consider the problem of scheduling a number of incoming vessels by assigning a berthing position, a berthing time, and a number of Quay Cranes to each vessel. This problem is known as the Berth Allocation Problem and the Quay Crane Assignment Problem. Holds of vessels are also managed in order to obtain a more realistic approach. Our aim is to minimize the total waiting time elapsed to serve all these vessels. In this paper, we deal with the above problems and propose an innovative metaheuristic approach. The results are compared against other allocation methods.This work has been partially supported by the research projects TIN2010-20976-C02-01 (Ministerio de Ciencia e Innovacion, Spain) the fellowship program FPU (AP2010-4405), and also with the collaboration of the maritime container terminal MSC (Mediterranean Shipping Company S.A.).Rodríguez Molins, M.; Salido Gregorio, MA.; Barber Sanchís, F. (2014). 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. https://doi.org/10.1007/s10489-013-0462-4S273290402Ayvaz D, Topcuoglu H, Gurgen F (2012) Performance evaluation of evolutionary heuristics in dynamic environments. Appl Intell 37(1):130–144Bierwirth C, Meisel F (2010) A survey of berth allocation and quay crane scheduling problems in container terminals. Eur J Oper Res 202(3):615–627Cheong C, Tan K, Liu D (2009) Solving the berth allocation problem with service priority via multi-objective optimization. In: IEEE symposium on computational intelligence in scheduling, 2009, CI-sched ’09, pp 95–102Christiansen M, Fagerholt K, Ronen D (2004) Ship routing and scheduling: status and perspectives. Transp Sci 38(1):1–18Consultants DS (2010) Global container terminal operators annual review and forecast. Annual ReportCordeau J, Laporte G, Legato P, Moccia L (2005) Models and tabu search heuristics for the berth-allocation problem. Transp Sci 39(4):526–538Daganzo C (1989) The crane scheduling problem. Transp Res, Part B, Methodol 23(3):159–175Feo T, Resende M (1995) Greedy randomized adaptive search procedures. J Glob Optim 6(2):109–133Festa P, Resende MG (2009) An annotated bibliography of grasp–part ii: applications. Int Trans Oper Res 16(2):131–172Giallombardo G, Moccia L, Salani M, Vacca I (2010) Modeling and solving the tactical berth allocation problem. Transp Res, Part B, Methodol 44(2):232–245Guan Y, Cheung R (2004) The berth allocation problem: models and solution methods. OR Spektrum 26(1):75–92Henesey L (2006) Overview of transshipment operations and simulation. In: MedTrade conference, Malta, April 2006, pp 6–7Imai A, Nagaiwa K, Tat C (1997) Efficient planning of berth allocation for container terminals in Asia. J Adv Transp 31(1):75–94Imai A, Chen H, Nishimura E, Papadimitriou S (2008) The simultaneous berth and quay crane allocation problem. Transp Res, Part E, Logist Transp Rev 44(5):900–920Kim K, Günther H (2006) Container terminals and cargo systems. Springer, BerlinKim KH, Park YM (2004) A crane scheduling method for port container terminals. Eur J Oper Res 156(3):752–768Lai KK, Shih K (1992) A study of container berth allocation. J Adv Transp 26(1):45–60Lambrechts O, Demeulemeester E, Herroelen W (2008) Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. J Sched 11(2):121–136Lee D, Wang H, Miao L (2008) Quay crane scheduling with non-interference constraints in port container terminals. Transp Res, Part E, Logist Transp Rev 44(1):124–135Lee DH, Chen JH, Cao JX (2010) The continuous berth allocation problem: a greedy randomized adaptive search solution. Transp Res, Part E, Logist Transp Rev 46(6):1017–1029Liang C, Huang Y, Yang Y (2009) A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning. Comput Ind Eng 56(3):1021–1028Lim A (1998) The berth planning problem. Oper Res Lett 22(2–3):105–110Liu J, Wan YW, Wang L (2006) Quay crane scheduling at container terminals to minimize the maximum relative tardiness of vessel departures. Nav Res Logist 53(1):60–74Meisel F, Bierwirth C (2009) Heuristics for the integration of crane productivity in the berth allocation problem. Transp Res, Part E, Logist Transp Rev 45(1):196–209Mohi-Eldin E, Mohamed E (2010) The impact of the financial crisis on container terminals (a global perspectives on market behavior). In: Proceedings of 26th international conference for seaports & maritime transportPark Y, Kim K (2003) A scheduling method for berth and quay cranes. OR Spektrum 25(1):1–23Peterkofsky R, Daganzo C (1990) A branch and bound solution method for the crane scheduling problem. Transp Res, Part B, Methodol 24(3):159–172Rodríguez-Molins M, Salido MA, Barber F (2010) Domain-dependent planning heuristics for locating containers in maritime terminals. In: Proceedings of the 23rd international conference on industrial engineering and other applications of applied intelligent systems. LNCS, vol 6096. Springer, Berlin, pp 742–751Rodriguez-Molins M, Salido M, Barber F (2012) Intelligent planning for allocating containers in maritime terminals. Expert Syst Appl 39(1):978–989Salido M, Sapena O, Barber F (2009) An artificial intelligence planning tool for the container stacking problem. In: Proceedings of the 14th IEEE international conference on emerging technologies and factory automation, pp 532–535Salido MA, Rodriguez-Molins M, Barber F (2012) A decision support system for managing combinatorial problems in container terminals. Knowl-Based Syst 29:63–74Stahlbock R, VoßS (2008) Operations research at container terminals: a literature update. OR Spektrum 30(1):1–52Steenken D, VoßS, Stahlbock R (2004) Container terminal operation and operations research-a classification and literature review. OR Spektrum 26(1):3–49Szlapczynski R, Szlapczynska J (2012) On evolutionary computing in multi-ship trajectory planning. Appl Intell 37:155–174Theofanis S, Boile M, Golias M (2009) Container terminal berth planning. Transp Res Rec 2100:22–28ValenciaPort F (2009) Automation and simulation methodologies for assessing and improving the capacity, performance and service level of port container terminals. Ministerio de Fomento (P19/08), SpainVis I, De Koster R (2003) Transshipment of containers at a container terminal: an overview. Eur J Oper Res 147:1–1

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). 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Containerbrückeneinsatzplanung in Seehafencontainerterminals - Entwurf und experimentelle Analyse von Lösungsverfahren für das Container Sequencing Problem

Durch den Einsatz immer größerer Containerschiffe zum Transport containerisierter Güter über den Seeweg gewinnt die Produktivität der zur Be- und Entladung der Containerschiffe eingesetzten Containerbrücken in Seehafencontainerterminals immer mehr an Bedeutung. Einen erheblichen Einfluss auf die Produktivität der Containerbrücken hat die Containerbrückeneinsatzplanung. Gegenstand dieser Arbeit ist das im Rahmen der Containerbrückeneinsatzplanung auftretende Container Sequencing Problem, welches hier erstmals unter Berücksichtigung von Ladelukendeckeln und Rehandlecontainern verschiedener Containerkategorien untersucht wird. Die Problemstellung wird als ganzzahliges lineares Optimierungsmodell formuliert. Zur Lösung des Problems werden verschiedene heuristische Verfahren vorgeschlagen. Deren Leistungsfähigkeit wird anhand numerischer Experimente analysiert