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Abstract Marine container terminals play a significant role for international trade networks and global market. To cope with the rapid and steady growth of the seaborne trade market, marine container terminal operators must address the operational challenges with appropriate analytical methods to meet the needs of the market. The berth allocation and scheduling problem is one of the important decisions faced by operators during operations planning. The optimization of a berth schedule is strongly associated with the allocation of spatial and temporal resources. An optimal and robust berth schedule remarkably improves the productivity and competitiveness of a seaport. A significant number of berth allocation and scheduling studies have been conducted over the last years. Thus, there is an existing need for a comprehensive and critical literature survey to analyze the state-of-the-art research progress, developing tendencies, current shortcomings, and potential future research directions. Therefore, this study thoroughly selected scientific manuscripts dedicated to the berth allocation and scheduling problem. The identified studies were categorized based on spatial attributes, including discrete, continuous, and hybrid berth allocation and scheduling problems. A detailed review was performed for the identified study categories. A representative mathematical formulation for each category was presented along with a detailed summary of various considerations and characteristics of every study. A specific emphasis was given to the solution methods adopted. The current research shortcomings and important research needs were outlined based on the review of the state-of-the-art. This study was conducted with the expectation of assisting the scientific community and relevant stakeholders with berth allocation and scheduling. Graphical Abstract Graphical Abstract

PurposeBuilding on the resource-based view (RBV) perspective of common resources, the objective of this paper is to empirically examine the impact of container terminals' common resources (i.e. government support and terminal resources) on resource bundling strategies and subsequent effect on service performance.Design/methodology/approachUsing cross-sectional survey data collected from a sample of 216 respondents of Indonesia's container terminals, this study used structural equation modeling (SEM) to test the hypothesised relationships between common resources, resource bundling strategies and service performance.FindingsGovernment support and terminal resources (personnel and physical), both as sources of common resources when bundled effectively, are found to have positive and significant effect on terminal service performance. The resource bundling strategies fully mediate the relationship between container terminals' common resources and service performance.Practical implicationsThe study introduces the notion of common resources to container terminal managers in contrast to the valuable, rare, inimitable and non-substitutable (VRIN) types. It is recommended that appropriate resource bundling strategies can turn the common resources into VRIN resources that can be used to obtain desired service performance.Originality/valueRBV theorists suggest that resources that are VRIN types can be the source of competitive advantage. However, the resources can also be common, basic and valuable, a fact that is rarely investigated in the literature. These common resources can be bundled judiciously with other pre-existing resources to create VRIN resources. This research enriches the RBV by empirically validating that VRIN resources are embedded within various common resources bundling strategies.

Purpose Because container terminals (CTs) are broker organizations, their interest has complex, various internal and external factors, the purpose of this study is to scrutiny and structure the factors affecting the interests of the CT. Design/methodology/approach In terms of purpose, this study is a developmental study that, are identified the factors related to the interests of the CT through studying the library and interviewing experts and then the degree of influence of each factor on each other by using the interview with experts of CT in Bushehr province are determined. Then, the fuzzy DEMATEL method is used to calculate the feedback loop of the indicators, and then the matrix obtained Fcmapper software and the fuzzy cognitive map (FCM) is drawn. Findings According to the FCM analysis, three important factors are centrality identified in terms of attracting capital, quality of service and efficiency. In addition, there are three factors, attracting capital, operator performance and law have high outdegree. Originality/value This research has identified 34 effective indicators of CT interests and evaluated their relationship with FCM. In the available researches, all these indicators measurement has not been evaluated together. Furthermore, fuzzy DEMATEL has been used to evaluate self-loops. Another contribution of this research is the introduction of a strategic route as a roadmap for CT managers in Bushehr province to decide on the interests of a CT.

This paper treats a berth allocation problem (BAP) in dedicated container terminals where feeder ships and container vessels are jointly served. When assigning quay space and a service time to each calling ship particular focus is put on the container exchange between feeder ships and mother vessels, so that the weighted number of containers delivered by feeder missing their intended mother vessel (and vice versa) does not exceed a given upper bound. The resulting BAP is formalized, complexity proofs are provided, and suited optimization procedures are presented and tested.

In the berth-allocation problem (BAP) the aim is to optimally schedule and assign ships to berthing areas along a quay. The objective is the minimization of the total (weighted) service time for all ships, defined as the time elapsed between the arrival in the harbor and the completion of handling. Two versions of the BAP are considered: the discrete case and the continuous case. The discrete case works with a finite set of berthing points. In the continuous case ships can berth anywhere along the quay. Two formulations and a tabu search heuristic are presented for the discrete case. Only small instances can be solved optimally. For these sizes the heuristic always yields an optimal solution. For larger sizes it is always better than a truncated branch-and-bound applied to an exact formulation. A heuristic is also developed for the continuous case. Computational comparisons are performed with the first heuristic and with a simple constructive procedure.

This paper focuses on the application of two approaches: Business Process Redesign (BPR) and Discrete Event Simulation (DES) in an attempt to enhance the basic logistics processes in inland container terminals. The goal of the paper is to identify the weaknesses and potential problems in the execution of current logistics processes, as well as to redesign such identified weaknesses and problems. The main contribution of the paper is to show how DES and BPR can be used in restructuring of container logistics processes in inland container terminals. Its original feature is a novelty, a holistic approach in rationalization of container logistics processes in inland intermodal terminals, creating models of the current (AS-IS) and improved (TO-BE) states. Models show that DES and BPR represent an easily accessible and effective tool in the analysis of terminal processes in container terminals. Therefore, it can be efficiently used for monitoring the current situation, identification of weak spots in a particular system and for creating any necessary preconditions for improvement of business processes.

Yang, X.; Zhao, N.; Bian, Z.; Chai, J., and Mi, C., 2015. An intelligent storage determining method for inbound containers in container terminals. In this paper, the container Stacking Position Determination Problem (SPDP) for inbound containers in the process of vessel loading and unloading activities is discussed, specifically focusing on the storage space allocation problem (SSAP) in container terminals. The process of positioning inbound containers is investigated and analyzed based on an advanced handling strategy, namely assigning quay cranes in their full utilization with the aim of “Full In & Full Out” that is proposed for internal trucks, where intense loading and unloading activities are considered at the same time. A multi-objective integer programming model (MOIPM) is then put forward based on the idea of real-time strategic planning. For the solution, a genetic algorithm (GA) is employed to address the formulated problem. In addition to the effectiveness and reliability of the proposed model, a number of experimental results also verify the robustness of the GA algorithm. The application of the proposed method is able to improve operational efficiency in container terminals and reduce handling costs as well.

Bian, Z.; Yang, Y.; Mi, W., and Mi, C., 2015. Dispatching electric AGVs in automated container terminals with long travel distance. Electric AGV's are a promising horizontal transporter for automated container terminals (ACT), however, its application in ACTs has been held back since the performance of batteries was unsatisfactory. Following the development of battery technology in recent years, the major obstacle impeding the application of AGVs has turned to the management defects, especially in automated container terminals with long travelling distance. In light of this, an event-driven approach for dispatching electric AGVs in ACT's is proposed in this paper. An event-driven assignment model considering battery capacity is raised, in which empty AGVs are always deemed as available for assignment. Moreover, the performance of algorithms for assignment is compared by numerical experiments.

In this paper, a Data Envelopment Analysis approach is used to assess the efficiency of cargo-handling operations at a container terminal and study the factors influencing it. The aim is to provide recommendations that would allow a container terminal to reach high technical efficiency scores, in order to increase its inner productivity. A three-stage methodological framework is proposed to analyze the operational conditions of 152 containerships, along a 6-month period, in a Mexican Container Terminal. In the first stage, a cluster analysis is performed in order to group containerships with homogeneous operational characteristics into clusters. Then, an input-oriented Variable Returns to Scale model is used to compute efficiency scores in each cluster. Scale efficiency, returns to scale, and average efficiency of containerships are reported. In the second stage, two regression models are estimated to relate the efficiency scores obtained to a number of exogenous variables. In the third stage, a decision tree is constructed to gain insights about the efficiency of the cargo-handling operations at container terminals. The results indicate that for every additional hour spent in cargo-handling operations of a containership, the probability of providing an efficient service decreases by 9.58%. Furthermore, using more quay cranes than needed might decrease the probability of providing an efficient service by 99.93%.

The containerized trade market is growing rapidly with the uprising of the Far East. Container ports worldwide should be responsive by developing tools to handle these massive volumes of containers in order to retain their level of competitiveness. One of the areas in a container terminal that is highly affected by the increase in the demand of containers is the stack. The stack is used to temporarily store containers upon further transport to their destination. This study is concerned with scheduling sea and landside storages and retrievals in a stack with two cooperating automated stacking cranes working in a single block. We present a mathematical model to minimize the makespan for both cranes. Both an algorithm to derive a lower bound for the makespan and a simulated-annealing based heuristic are proposed to efficiently solve the problem. Numerical experiments show that the solutions of the heuristic method are within 2% of the lower bound for large instances.