Abstract
According to previous research studies, automated quayside cranes (AQCs) and automated guided vehicles (AGVs) in automated container terminals have a high potential synergy. In this paper, a collaborative scheduling model for AQCs and AGVs is established and the capacity limitation of the transfer platform on AQCs is considered in the model. The minimum total energy consumption of automated quayside cranes (AQCs) and Automatic Guided Vehicles (AGVs) is taken as the objective function. A two-stage taboo search algorithm is adopted to solve the problem of collaborative scheduling optimization. This algorithm integrates AQC scheduling and AGV scheduling. The optimal solution to the model is obtained by feedback from the two-stage taboo search process. Finally, the Qingdao Port is taken as an example of a data experiment. Ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the two-stage taboo search algorithm since it can find near-optimal solutions, precisely and accurately.
Highlights
Introduction and Literature ReviewWith the deepening of economic globalization, the status of automated container terminals is becoming increasingly prominent
According to the characteristics of the model, a two-stage taboo search algorithm is designed to solve the cooperative scheduling problem of automated quayside cranes (AQCs) and automated guided vehicles (AGVs) in an automated container terminal. is algorithm integrates the decision on two stages for AQC scheduling and AGV scheduling. e first stage is the scheduling of the AQCs. e taboo search algorithm is used to search the loading and unloading sequences according to the different requirements of a ship in different time periods. e second stage is AGV dispatching
The problem of the cooperative scheduling of the AQC and the AGV is studied under the synchronized loading and unloading conditions of a single ship in an automated container terminal. e main conclusions of this paper include three aspects
Summary
With the deepening of economic globalization, the status of automated container terminals is becoming increasingly prominent. Xin et al [16, 17] proposed a hybrid model predictive control (MPC) to optimize the operation performance of container terminals and balance the efficiency and energy consumption of transport equipment. In 2016, Mei et al [18] proposed an overall planning model to optimize the scheduling of container terminal yard cranes and minimize the total energy consumption of tire-type gantry crane. In 2016, Yu et al [24] proposed a hybrid genetic taboo search algorithm for the flexible job-shop scheduling problem, considering defects such as the premature convergence of the genetic algorithm and strong initial solution dependence of the taboo search algorithm He used the method of dividing target multiplication to guide the evolution of the algorithm, established a multiobjective optimization model, and implemented it with MATLAB simulation.
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