MULTI AGV SCHEDULING PROBLEM IN AUTOMATED CONTAINER TERMINAL

With the expansion of automated container terminals (ACTs), joint scheduling among multiple types of equipment has become a critical factor affecting operational efficiency. This study investigates a joint scheduling optimization problem of quay cranes (QCs) and automated guided vehicles (AGVs) by considering AGV battery swapping strategies under spare battery constraints. With the objective of minimizing the final task completion time of AGVs, a mixed-integer programming model is formulated that simultaneously accounts for task assignment, operation sequencing, battery swapping thresholds, spare battery quantity, and mutual waiting times between AGVs and QCs. To solve this problem efficiently, a hill-climbing genetic algorithm (HC-GA) is proposed. Numerical experiments under different task scales show that HC-GA outperforms the genetic algorithm (GA), simulated annealing (SA), Q-learning, and the Q-learning-based genetic algorithm (Q-GA) in key indicators. In addition, the experimental results show that a proper configuration of AGVs can improve scheduling coordination and enhance the energy utilization efficiency of AGVs. The number of spare batteries and the threshold have significant impacts on overall system performance. When both operational efficiency and equipment utilization are considered, appropriately configuring the number of spare batteries and the threshold can effectively enhance the operational efficiency of ACTs.

Customers are generally requiring a variety of products, earlier due date, and lower price. A manufacturing process needs the efficient scheduling to meet those customer's requirements. This study proposes the novel algorithm named MJA(Minimum Job completion time and AGV time) that increases the performance of machines and AGV(Automated Guided Vehicles) in many kinds of job types. MJA optimizes the bottleneck of machines and efficiency of AGV with considering two types of dispatching at the same time. Suggested algorithm was compared with existing heuristic methods by several simulations, it performed better for reducing the time of tardiness.

자동화 컨테이너 터미널에서 안벽크레인, AGV (Automated Guided Vehicle)와 같은 하역장비의 작업은 수많은 요인에 영향을 받으며, 이로 인해 각 장비의 작업시간 예측에 있어 불확실성이 존재한다 이러한 불확실성은 AGV 배차를 어렵게 만들고 작업효율을 떨어뜨리는 주요 원인 중 하나이다. 본 논문에서는 이러한 불확실성에 대처하기 위하여 확률적 시뮬레이션 기반 AGV 배차 알고리즘을 제안한다. 제안 방안은 AGV에 작업을 할당할 때, 할당된 작업의 수행 및 이후 일정 기간 동안의 AGV의 작업에 대해 확률적 시뮬레이션을 여러 번 반복 수행하여 작업할당에 대한 평가치의 확률적 표본을 수집한다. 수집한 표본으로부터 평가의 기대치를 추정하고 이를 이용하여 대안을 평가함으로써 불확실성의 영향을 줄인다. 평가의 신뢰도를 높이기 위해서는 많은 수의 표본을 수집해야 하므로 실시간 제약 하에서 수집 가능한 확률적 표본의 수를 늘리기 위해 이벤트 기반의 고속 시뮬레이션을 디자인하였다. 시뮬레이션 실험을 통해 검증한 결과 불확실성이 있는 환경에서 제안방안의 성능이 정적인 환경을 가정하는 방안보다 뛰어남을 확인하였다. In an automated container terminal, various factors affect the operation of container handling equipment such as quay cranes and AGVs, and thus calculating the exact operation time is nearly infeasible. This uncertainty makes it difficult to dispatch AGVs well. In this paper, we propose a simulation-based AGV dispatching algorithm When dispatching an AGV to an operation, the proposed algorithm conducts multiple stochastic simulation for the succeeding AGV operations for the predetermined period to collect stochastic samples of the result of the dispatching. In the stochastic simulation, the uncertainty of crane operations is represented as a simple probability distribution and the operation time of a crane is determined according to this. A dispatching option is evaluated by the total delay time of quay cranes which is estimated by averaging the quay crane delay of each simulation In order to collect a sufficient number of samples that guarantee the credibility of the evaluation, we devised a high-speed simulator that simulates AGV operation The effectiveness of the proposed algorithm is validated by simulation experiments.

Containers have been used in past decades increasingly as one of the most important transportation tools. Containers have revolutionized cargo shipping and thus changed the world trade systematically. Container terminals as the transhipment facility play a valuable role in performance of this transportation system. Improvement of this facility has been widely considered in literatures. Automated container terminals (ACTs) have been introduced to pursue this purpose. In ACTs various transport vehicles are automated and integrated to each other. Automated guided vehicles (AGVs) are used in ACTs to handle containers between quay cranes and storage yards. Usually scheduling of the AGVs is known as the key factor to improve the performance of ACTs. This paper proposed a heuristic algorithm to schedule the AGVs concurrently with quay cranes. A genetic algorithm is proposed to optimize the simultaneous scheduling of AGVs and QCs. The results showed that proposed genetic algorithm can be used in practical implications while its running time is reasonably low.

A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time

This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises.

Automated guided vehicles (AGVs) in the U-shaped automated container terminal travel longer and more complex paths. The conflicts among AGVs are trickier. The scheduling strategy of the traditional automated container terminal is difficult to be applied to the U-shaped automated container terminal. In order to minimize the handling time of all tasks and avoid AGV conflicts simultaneously in the U-shaped automated container terminal, this paper establishes a hybrid programming model for conflict-free integrated scheduling of quay cranes, AGVs, and double-cantilever rail cranes in the unloading process. It consists of a discrete event dynamic model and a continuous time dynamic model. An improved genetic seagull optimization algorithm (GSOA) is designed. A series of numerical experiments are conducted to verify the effectiveness and the efficiency of the model and the algorithm. The results show that the proposed method can simultaneously realize the AGVs collision avoidance and multi-equipment integrated scheduling optimization in the U-shaped automated container terminal.

We herein try to schedule multiple AGVs (Automated Guided Vehicles) in real time with two-phase strategy in the flexible manufacturing workshops. This study considers the impact of running time, vehicle stopping and turning of AGVs, and deals with static workshop scheduling, real-time workshop scheduling with time-window based micro genetic algorithm. And we present a two-stage scheduling strategy for offline shortest path library generation and online optimal scheduling scheme generation. The preliminary experimental results showed the efficiency and stability of the proposed strategy and algorithm for Multiple AGVs system.

Current trend for Factory Automation is to design a variety of products to meet the changing demands of the customers. Automated material handling is an integral part of any automated manufacturing system. Automated Guided Vehicle (AGVs) is emerging as intelligent Material Handling Equipments (MHE) that is capable of adjusting to the changing scenario of manufacturing systems. AGVs are battery-powered, automated vehicles with capabilities to tag along programmed motions and orientation. AGVs selection has always been a challenging task owing to several constraints of manufacturing systems. Different MCDM (Multiple Criteria Decision Making) approaches have been employed by researchers for selection of MHE problem. This work is an attempt to choose amongst the AGVs alternatives and analyze their performance using certain MCDM approaches such as AHP, DEMATEL, TOPSIS, Fuzzy AHP, Fuzzy DEMATEL and Fuzzy TOPSIS. In the present work, data has been taken from Maniya and Bhatt [27] and the problem has been evaluated using four integrated approaches for analysis of AGV’s selection problem. It was found that Fuzzy AHP-Fuzzy TOPSIS and Fuzzy DEMATEL—Fuzzy TOPSIS technique gave better results for the instant case as they were devoid of vagueness error caused due to uncertainty in the decision making of experts. Authors have applied four approaches out of which DEMATEL-TOPSIS and Fuzzy DEMATEL-Fuzzy TOPSIS approaches has yet not been applied in AGVs selection problem, thus justifying original work and their appropriateness for the problem.

Simulation of Human Effect to the Adaptive Logistics System Used in Public Facilities

Factory automation is advanced in industrial field, and then higher speed drive and performance are desired for an AGV (automated guided vehicle). A new steering control of AGV by fuzzy control has been proposed instead of the PI control. However, it is necessary much time to investigate the rules and to adjust the scaling factors for excellent performance in the fuzzy control. Thus, this is the matter to be solved.In this paper, a new steering control of AGV based on the neural network using the back propargation method is proposed. The good steering control results by the fuzzy control is adopted for the teaching signal of neural network. At the first step, the effect of the number of learning and the learning errors on the steering control results are discussed by the computer simulation using the AGV model. Further, the ability of generalization in the turning radius and the traveling speed is also investigated. It becomes clear that the AGV can travel along the traveling course provided that the neural network learns both of the right and left turning at the maximum traveling speed and the minimum turning radius. Then, it is proved by the experiment using the AGV built as a trial that the proposed steering control method is very available.

To improve the operational efficiency of automated container terminals and the coordination between multiple operations, this article studies the integrated scheduling optimization problem of automated guided vehicles (AGVs) and yard cranes. The impact of charging constraints on AGV task allocation and scheduling is considered. With the goal of minimizing the maximum completion time of all tasks, a mixed integer programming model is proposed. A solution method based on a dynamic programming algorithm is designed, where a heuristic algorithm is used to assign tasks to the yard cranes, and the dynamic programming method is used to assign tasks to the AGVs based on the task assignment results of the yard cranes. Finally, the validity of the model and algorithm is tested by numerical experiments. Furthermore, the influence of the quantity of AGVs on the terminal operational efficiency and the impact of AGV charging strategies on AGV scheduling are analysed.

Dispatching automated guided vehicles (AGVs) is the common approach for AGVs scheduling in practice, the information about load arrivals in advance was not used to optimize the performance of the automated guided vehicles system (AGVsS). According to the characteristics of the AGVsS, the mathematical model of AGVs scheduling was established. A heuristic algorithm called Annealing Genetic Algorithm (AGA) was presented to deal with the AGVs scheduling problem,and applied the algorithm dynamically by using it repeatedly under a combined rolling optimization strategy. the performance of the proposed approach for AGVs scheduling was compared with the dispatching rules by simulation. Results showed that the approach performs significantly better than the dispatching rules and proved that it is really effective for AGVsS. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2481

This paper discusses the problem of scheduling quay cranes, the most important equipment in port terminals. A simulation model is developed for evaluating time of quay cranes. Then a dynamic scheduling model using objective programming for quay cranes is developed based on genetic algorithm approach. Finally, numerical experiments on a specific container terminal are made for propose approach. Computational results suggest that the proposed method is able to solve the problem efficiently.Keywordsquay cranecontainer terminalgenetic algorithm

The paper deals with the problem of works transport organization and control by artificial intelligence with respect to path routing for an automated guided vehicle (AGV). The presented approach is based on non-changeable path during travel along a given loop. The ordered set of stations requesting transport service was determined by fuzzy logic, while the sequence of stations in a loop was optimized by genetic algorithms. A solution for both AGV’s and semi-autonomous transport vehicles wherein the control system informs the driver about optimal route was presented. The obtained solution was verified by a computer simulation.