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With increasing container-shipping traffic, congestion at transshipment hub ports happens from time to time incurring longer-than-expected waiting time for vessels and loss of transshipment connections. This situation is even worse for feeder companies, due to their relatively lower berthing priority. It is essential to design the feeder vessel routes and schedules in response to hub port congestion while ensuring efficient transshipment connection with their connecting long-haul services. In this paper, we study the vessel routing and transshipment coordination problem for a feeder liner company where only limited choices of berthing time slots are available at the hub port. We proposed an arc-based mixed integer linear programming model and a set partitioning model with the objective of minimizing the total operating and transshipment connection cost. Two solution approaches are developed (1) a branch-&-price exact algorithm that can solve instances with up to 30 ports to optimality within reasonable computational time; and (2) a column generation heuristic method that yields high-quality near-optimal solutions very efficiently without having to implement a full-scale branch-&-bound search. Computational experiments based on a real-world shipping network and randomly generated test instances confirm the effectiveness and efficiency of the solution approaches. It is also demonstrated that, by integrating the hub port calling time decision with feeder vessel routing, transshipment coordination can be significantly enhanced even under hub port congestion situations. • Investigate feeder vessel routing and transshipment coordination for shipping companies. • Propose a set partitioning formulation and a branch-&-price exact algorithm. • Design a column generation based heuristic approach. • Conduct real-world case studies based on APL's container shipping networks. • Container transshipment at the hub port can be significantly improved. [ABSTRACT FROM AUTHOR]

In container terminals, operators normally form an initial plan for berth allocation before the arrival of incoming vessels. However, during the real operations, unforeseen incidents (e.g. delays, equipment breakdowns) happen frequently, which hinder the implementation of initial plans. In response to disruptions, terminal operators usually employ an experience-based strategy for berth allocation recovery. However, this practice may not work well in challenging situations, especially when extensive transshipment operations need to be conducted, and the berthing for mother and feeder vessels should be coordinated. This paper studies the disruption recovery problem for berth allocation in transshipment terminals, and develops decision support tools for terminal operators. A mixed integer linear programming model is established to adjust original berthing plan with the objective of minimizing recovery cost caused by deviations from the original plan. Quay crane assignment decision is also taken into account during disruption recovery. Transshipment connections between correlated feeder and mother vessels are also considered specifically in order to reduce the missing of transshipment connection opportunities. Squeaky Wheel Optimization heuristic is devised to find near-optimal solutions under large problem scales. Extensive computational experiments validate the effectiveness and the efficiency of designed method, and the results show practical benefit from incorporating transshipment consideration when rescheduling berth allocation. [ABSTRACT FROM AUTHOR]

In this paper, we study a barge and tugboat joint scheduling problem arising from a river–sea intermodal transport system, where non-self-propelled barges travel with the help of tugboats between an inland river bulk port and sea transshipment platforms to transport cargo. Due to limited channels and the sequential scheduling among ships, transit inefficiency and resource under-utilization often occur, thus it is essential to design barge timetables and tugboat paths. This paper jointly schedules barges and tugboats in a river–sea transshipment port with a tidally influenced channel, and focuses on barge round trips with multiple legs. We construct a state-space–time network from the perspective of tugboats, in which the state implies whether a barge is served by a tugboat. The problem is formulated as an integer programming model based on the network to minimize time taken for bulk cargo transshipment. A tailored variable neighborhood search algorithm is designed and its efficiency, effectiveness, and stability are demonstrated by numerical experiments, which are based on real data from the Boffa port. It is demonstrated that, by joint scheduling barges and tugboats, transfer efficiency can be significantly improved under limited tugboat resources and shorter tidal windows. The results also provide managerial insights on schedule and valuable conclusions about the resource allocation in the port. • Introduce an integrated barge-tugboat scheduling problem for river–sea transport. • Develop a state-space-time network and an integer programming model. • Design a variable neighborhood search algorithm. • Verify the performance of the proposed algorithm via computational experiments. [ABSTRACT FROM AUTHOR]

With the trend towards mega-vessels and shipping alliance, the importance of transshipment activities keeps increasing. In transshipment yards, a “yard template” is often used to stack containers in dedicated areas (sub-blocks) pre-reserved for their own destination vessels. At short-term planning level, the yard template is given, but the containers going to a specific vessel still have high flexibility to be allocated among many pre-reserved sub-blocks. The amount of containers allocated to each sub-block, i.e. “container allocation”, not only affects the traffic congestion, but more importantly determines the number of yard cranes (YCs) required in each block. The limited YCs have to switch blocks to fit the needs of container allocation in different periods, i.e. “YC deployment”. This study integrated these two closely related problems and formulated a MIP model. Since the model has a nice block-diagonal structure, column generation under Dentzig Wolfe decomposition was proposed to get lower bounds. A novel branch-and-price (B&P) method was proposed to find near-optimal solutions. To reduce the searching tree size, our B&P method branched on YC paths during the planning horizon, instead of branching on decision variables directly. Numerical experiments under both small and large scale problems showed that our B&P method could efficiently solve the integrated planning problem. The results also showed that YC movements could be reduced effectively without sacrificing operational efficiency or using more yard cranes. [ABSTRACT FROM AUTHOR]

Quay-side berthing congestion is an emerging challenging issue that arises in busy container transshipment terminals and calls for effective management of terminal operations. This paper tackles the berthing congestion problem by introducing a proactive management strategy from the terminal’s perspective that adjusts the calling schedule of feeder vessels in such a way that the quay-side workload distribution in the temporal dimension can be balanced. Such a schedule template design problem is considered simultaneously with another two tactical level decision problems, berth template design (i.e., determining preferred berthing positions for vessels) and yard template design (i.e., allocating storage yard space to transshipment flows). This highly integrated problem is formulated as a set covering model. Heuristic methods based on column generation are developed to obtain near-optimal solutions in an efficient way. Computational experiments on real-world sized test instances demonstrate the efficiency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: The feeder vessel management problem consists of designing preferred berthing positions and service time for cyclically visiting feeders, and allocating storage yard space to the transshipment flows between mother vessels and feeders. We consider the above three tactical decision problems simultaneously for a container transshipment terminal with an eye toward the quayside congestion and the housekeeping cost of container movements. The integrated problem is formulated as a mixed integer programming model and solved by a memetic heuristic approach. A comprehensive computational experiment is conducted to show the effectiveness of the heuristic and the improvement upon real-world terminal operations. [Copyright &y& Elsevier]

Abstract: This paper presents an integer programming model for the terminal and yard allocation problem in a large container transshipment hub with multiple terminals. The model integrates two decisions: terminal allocation for vessels and yard allocation for transshipment container movements within a terminal as well as between terminals. The objective function aims to minimize the total inter-terminal and intra-terminal handling costs generated by transshipment flows. To solve the problem, we develop a 2-level heuristic algorithm to obtain high quality solutions in an efficient way. Computational experiments show the effectiveness of the proposed approach. [Copyright &y& Elsevier]

• The train schedule template and transshipment plan of intermodal containers are jointly optimized. • Various time window constraints related to the transshipment operation are considered. • The model is enhanced by valid inequalities and relaxations. • A critical value of handling capacity could be effectively identified. • Increasing the unit storage cost is in favor of a better transfer plan. • The overestimate of the indirect-transshipment penalty should be avoided. This paper investigates the transshipment operations between vessels and trains in seaport rail terminals. In order to get a more effective transshipment plan, the schedule plan of trains and the transshipment plan of containers among vessels, yards, and trains are determined simultaneously. To this end, a mixed integer programming (MIP) model is proposed to integrate the two sub-problems with the objective of minimizing the total operation cost, in which the service time window and unloading time requirement of trains are considered. It is demonstrated that some variables in the model can be relaxed without influencing the optimal result. Moreover, a set of valid inequalities are introduced to tighten the constraints. The result experiments show that the enhanced model can be solved more effectively. Then, extensive experiments have been conducted to analyze the influence of several factors, including the handling capacity, the yard capacity, and the value of unit cost. The results report that the handling capacity shows a significant effect on the performance of the transfer plan, and increasing the storage cost of import containers leads to a more effective transshipment plan. [ABSTRACT FROM AUTHOR]