A dynamic truck dispatching problem in marine container terminal

In this paper, a dynamic truck dispatching problem of a marine container terminal is described and discussed. In this problem, a few containers, encoded as work instructions, need to be transferred between yard blocks and vessels by a fleet of trucks. Both the yard blocks and the quay are equipped with cranes to support loading/unloading operations. In order to service more vessels, any unnecessary idle time between quay crane (QC) operations need to be minimised to speed up the container transfer process. Due to the unpredictable port situations that can affect routing plans and the short calculation time allowed to generate one, static solution methods are not suitable for this problem. In this paper, we introduce a new mathematical model that minimises both the QC makespan and the truck travelling time. Three dynamic heuristics are proposed and a genetic algorithm hyperheuristic (GAHH) under development is also described. Experiment results show promising capabilities the GAHH may offer

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

Loading and unloading operations in container terminals

Department of Logistics, Faculty of BusinessAuthor name used in this publication: George L. Vairaktarakis2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

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

Adaptive autotuning mathematical approaches for integrated optimization of automated container terminal

With the development of automated container terminals (ACTs), reducing the loading and unloading time of operation and improving the working efficiency and service level have become the key point. Taking into account the actual operation mode of loading and unloading in ACTs, a mixed integer programming model is adopted in this study to minimize the loading and unloading time of ships, which can optimize the integrated scheduling of the gantry cranes (QCs), automated guided vehicles (AGVs), and automated rail-mounted gantries (ARMGs) in automated terminals. Various basic metaheuristic and improved hybrid algorithms were developed to optimize the model, proving the effectiveness of the model to obtain an optimized scheduling scheme by numerical experiments and comparing the different performances of algorithms. The results show that the hybrid GA-PSO algorithm with adaptive autotuning approaches by fuzzy control is superior to other algorithms in terms of solution time and quality, which can effectively solve the problem of integrated scheduling of automated container terminals to improve efficiency.info:eu-repo/semantics/publishedVersio