An exact algorithm for the integrated planning of berth allocation and quay crane assignment

In this paper we study the simultaneous optimization of berth allocation and quay crane assignment in seaport container terminals. We propose a model based on an exponential number of variables that is solved via column generation. An exact branch-and-price algorithm is implemented to produce optimal integer solutions to the problem. In particular, we present several accelerating techniques for the master and the pricing problem that can be generalized to other branch-and-price schemes. Computational results show that the proposed approach outperforms commercial solvers. Furthermore, the developed algorithm allows for a comparative analysis between the hierarchical and the integrated solution approach that confirms the added value of integration in terms of cost reduction and efficient use of resources. To the best of our knowledge, this is the first exact branch-and-price algorithm for both the berth allocation problem and the berth allocation problem with quay crane assignment

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. 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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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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

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

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

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

An evolutionary approach to a combined mixed integer programming model of seaside operations as arise in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integer programming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times

Integrated Berth Allocation and Quay Crane Assignment Problem: Set partitioning models and computational results

Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated Berth Allocation and Quay Crane Assignment Problem in seaport container terminals. We will extend the current state-of-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the effects of different discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models significantly improve the benchmark solutions of the current state-of-art optimal approaches

A combined Mixed Integer Programming model of seaside operations arising in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely the Berth Allocation Problem, the Quay Crane Assignment Problem, and the Quay Crane Scheduling problem, which have to be solved to carry out these seaside operations in container ports. Each one of these problems is complex to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence the need to solve them in a combined form. The problem is formulated as a mixed-integer programming model with the objective being to minimise the tardiness of vessels. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX