Rail-guided Vehicles Research Articles

Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing.

In flexible job shop scheduling problem (FJSP), the collision of bidirectional rail guided vehicles (RGVs) directly affects RGVs scheduling, and it is closely coupled with the allocation of production equipment, which directly affects the production efficiency. In this problem, taking minimizing the maximum completion time of RGVs and minimizing the maximum completion time of products as multi-objectives a dual-resource integrated scheduling model of production equipment and RGVs considering conflict-free routing problem (CFRP) is proposed. To solve the model, a multi-objective improved discrete grey wolf optimizer (MOID-GWO) is designed. Further, the performance of popular multi-objective evolutionary algorithms (MOEAs) such as NSGA-Ⅱ, SPEA2 and MOPSO are selected for comparative test. The results show that, among 42 instances of different scales designed, 37, 34 and 28 instances in MOID-GWO are superior to the comparison algorithms in metrics of generational distance (GD), inverted GD (IGD) and Spread, respectively. Moreover, in metric of Convergence and Diversity (CD), the Pareto frontier (PF) obtained by MOID-GWO is closer to the optimal solution. Finally, taking the production process of a construction machinery equipment component as an example, the validity and feasibility of the model and algorithm are verified.

A control system of rail-guided vehicle assisted by transdifferentiation strategy of lower organisms

Rail-guided vehicle is a logistics management device widely used to perform various material handling operations instead of manual labor. In processing scenarios, the dimensions of the material transfer path of a rail-guided vehicle are typically very large, which makes the optimization of the material transfer path very difficult. The transdifferentiation behavior of lower organisms was introduced into the evolutionary algorithm, and a large-scale differential evolution algorithm based on the transdifferentiation strategy was proposed, for achieving high-efficiency processing. This strategy makes it possible for some individuals with poor fitness to reach maturity again and be selected for the next iteration after losing some information and returning to their juvenile stage, which helps maintain the diversity of the population. Simulation results show that the proposed algorithm not only achieves an average 25.68% higher output rate than the comparison algorithms on the test cases but also has an excellent and stable effect distribution level on the extended problem space, which shows that the superiority of the proposed algorithm is not affected by the processing parameters. This research is expected to provide technical guidance for the processing of key components in the ship and aviation manufacturing industries. The code with a 31-page manual is available on our project homepage https://github.com/MLNST-JUST/DE-TS.

Intelligent RGV Dynamic Scheduling Virtual Simulation Technology Based on Machine Learning

With the development of workshop automation, the complexity of RGV (Rail Guided Vehicle) dynamic scheduling schemes using virtual simulation technology is increasing. For the widely valued intelligent machining systems, machine learning based optimization algorithms can effectively respond to the increasingly complex RGV dynamic intelligent scheduling. In the whole model construction, how to complete the modular design of the intelligent processing system and optimize the solution is the key problem that needs to be solved urgently at present. This paper studied the use of particle swarm optimization to design the RGV dynamic scheduling model, aiming to improve the material processing production efficiency of RGV dynamic scheduling and reduce the system failure rate. Through problem modeling, solution and simulation experiment analysis, this paper applied particle swarm optimization based on machine learning, combined with RGV structure modular design and task parameter test set samples. According to the data results, the following conclusions can be drawn from the discussion. Under the background of intelligent logistics system, the RGV dynamic scheduling model using particle swarm optimization had higher material processing production efficiency than the traditional scheduling method in all job test samples, and the average increase was 13.25%. Meanwhile, in terms of system failures, optimization algorithms were better than traditional scheduling methods, with an average reduction of 4.6%. This shows that the RGV dynamic scheduling model based on particle swarm optimization has a better practical application effect.

Order dividing optimisation in crane & shuttle-based storage and retrieval system

The crane & shuttle-based storage and retrieval system (C&SBS/RS) is the first automated warehouse technology that supports pallet picking, case picking and item picking. In the C&SBS/RS, aisle-captive cranes perform pallet picking, while tier-captive shuttles handle cases and items picking. To balance picking tasks, we propose an order dividing algorithm to fulfil required specific picking sequences. The optimisation objective is to minimise the order line picking time. Therefore, we modelled and analyzed the C&SBS/RS and considered single and dual command cycles for each resource (i.e. cranes, rail-guided vehicles, shuttles and lifters) separately according to their respective operation processes. Finally, numerical experiments were conducted to analyze impact factors and a real case to verify the power of the proposed order dividing algorithm.

Dual-rail–guided vehicle scheduling in an automated storage and retrieval system with loading and collision-avoidance constraints

PurposeThe use of multiple-capacity rail-guided vehicles (RGVs) has made automated storage and retrieval system (AS/RS) optimization more complex. The paper performs dual-RGV scheduling considering loading/unloading and collision-avoidance constraints simultaneously as these issues have only been considered separately in the previous literature.Design/methodology/approachThis paper proposes a novel model for dual-RGV scheduling with two-sided loading/unloading operations and collision-avoidance constraints. To solve the proposed problem, a hybrid harmony search algorithm (HHSA) is developed. To enhance its performance, a descent-based local search with eight move operators is introduced.FindingsA group of problem instances at different scales are optimized with the proposed algorithm and the results are compared with those of two other high-performance methods. The results demonstrate that the proposed method can efficiently solve realistically sized cases of dual multi-capacity RGV scheduling problems in AS/RSs.Originality/valueFor the first time in the research on dual multi-capacity RGV scheduling in an AS/RS, two-sided loading/unloading operations and collision avoidance constraints are simultaneously considered. Furthermore, a mathematical model for minimizing the makespan is developed and the HHSA is developed to determine solutions.

Contactless Power Transfer for Rail-Guided Vehicles With Power Equalization and Efficiency Improvement Considerations

This article proposes a contactless power transfer for rail-guided vehicles with power equalization and efficiency improvement considerations. This study is motivated since the rail-guided vehicles may encounter the power imbalance and low efficiency during the power transfer under a multireceiver environment. Therefore, the article is devoted to designing an approach of evenly dispatching power among receivers as well as developing a tracking compensation technique to search the maximum power-transmitting efficiency under different number of receiving modules. Through this proposed method, the constant power output, power equalization, and efficiency improvement are all achieved. Circuit derivation, frequency response, and resonance features are all made. A hardware circuit is also realized with different tests carried out under various scenarios. The maximum efficiency of this system is recorded to be 90.3%, supporting the practicality of the method. Experimental outcome gained from this study is beneficial for on-rail charging applications.

Dynamic Scheduling Model of Rail-Guided Vehicle (RGV) Based on Genetic Algorithms in the Context of Mobile Computing

Track guidance vehicle (RGV) is widely used in logistics warehousing and intelligent workshop, and its scheduling effectiveness will directly affect the production and operation efficiency of enterprises. In practical operation, central information system often lacks flexibility and timeliness. By contrast, mobile computing can balance the central information system and the distributed processing system, so that useful, accurate, and timely information can be provided to RGV. In order to optimize the RGV scheduling problem in uncertain environment, a genetic algorithm scheduling rule (GAM) using greedy algorithm as the genetic screening criterion is proposed in this paper. In the experiment, RGV scheduling of two-step processing in an intelligent workshop is selected as the research object. The experimental results show that the GAM model can carry out real-time dynamic programming, and the optimization efficiency is remarkable before a certain threshold.

Optimal strategy for intelligent rail guided vehicle dynamic scheduling

In an automated stereoscopic warehouse, the efficiency of the Rail Guided Vehicle (RGV) is the bottleneck. This paper proposes a foresight stepping model to optimize the intelligent RGV scheduling scheme. We incorporate the chaotic particle swarm optimization algorithm into the model and design the mechanism of multi-step processing. The machine optimization is used to compare the optimal alignment effect of the Back Propagation (BP) network algorithm and GradientBoostingDecisionTree (GBDT) algorithm. The real-life system test is performed by simulation. The simulation results show that the GBDT-foresight stepping model is superior to the traditional models in terms of complexity, reliability and accuracy.

A Study of Dynamic Scheduling Strategy of Intelligent Machining System

Rail Guided Vehicle (RGV) is used in the field of automated logistics system and automated warehouse. It has the characteristics of high speed, high reliability and low cost. The combination of RGV and Computer Number Controller (CNC) can greatly improve production efficiency. This paper designs an optimal dynamic scheduling strategy and a shortest walking path of RGV for an intelligent machining system composed of one RGV and eight CNC. Firstly, based on the shortest path algorithm and the principle of "nearest priority", this paper designs three optimal RGV dynamic scheduling models to adapt the three different cases. In these models, the optimization objective is the total number of clinkers produced by eight CNC, the decision variable is the number of clinkers produced by each CNC, and the constraints are determined by the time when each CNC issues instructions. Secondly, according to the different properties of the three models, the corresponding solutions have been given. Meanwhile, in order to describe the RGV scheduling strategy and the operational efficiency of the system, this paper uses the cost-time and average time spent for generating a clinker to measure the operational efficiency of the system. If their ratio were closer to 1, the work efficiency is higher. If the ratio were closer to 0, the work efficiency is lower. Finally, in order to test the practicability and validity about the three models, we import the known data into the three models to find how to manage the CNC and when to carry out the downloading and uploading operations by MATLAB simulation. The practicability of the optimized dynamic scheduling model and the corresponding calculation are verified.

RGV dynamic scheduling model based on kruskal algorithm

In order to solve the problems in traditional processing industry and meet the increasingly urgent demand for product processing performance, intelligent processing has begun to be widely used in industrial production. Today, the Rail Guided Vehicle (RGV) has become one of the main equipment for logistics distribution tasks in intelligent processing systems. [1] It automatically controls the direction and distance of movement according to the command. By planning the dynamic scheduling model of RGV, the output efficiency of industrial production can be greatly improved. Based on the Kruskal algorithm in the greedy algorithm, we design a set of material processing RGV dynamic scheduling simulation model through C++ programming. According to the theory of “local variable optimization and global variable optimization” in the Kruskal algorithm, the shortest path is selected every time the next target of RGV is judged, so that the whole distance traveled is the shortest, wasting shortest time and achieving the highest output.

The research on intelligent RGV dynamic scheduling based on hybrid genetic algorithm

With the rapid development of automation, mechatronics, information technology and other fields, production and processing systems are constantly moving towards unmanned and intelligent development. The RGV rail-guided vehicle system integrates various high-new technologies and is widely used in modern processing systems. Compared with the circular RGV orbit system, the linear reciprocating RGV system adopts a linear orbit, which is not easy to be blocked during operation, but its scheduling response mechanism is not perfect enough, which makes the operation efficiency of the whole system low. Therefore, how to improve the response mechanism and the operational efficiency of the RGV intelligent processing system has become an important factor in prompting the rapid development of the modern intelligent processing industry.In this paper, aiming at the intelligent linear reciprocating scheduling problem, the main influencing factors are analyzed according to the shortest distance priority response and the shortest completion time. Mathematical analysis and mathematical modeling are carried out and a rule-based genetic algorithm is designed to solve the problem. The algorithm of replacing the fixed parameters in the traditional genetic algorithm with the adaptive cross mutation probability, and improves the genetic algorithm’s tendency to fall into the local optimal solution.

Multi-facility green Weber problem

Locating facilities to satisfy the demands of customers is a strategic decision for a distribution system. In this article, we study the multi-facility green Weber problem (MF-GWP), an extension of the classical multi-facility Weber problem, that considers environmental concerns in a distribution system in the context of a planar facility location problem. In the MF-GWP, the vehicles are sent directly from the facilities to the assigned customers to satisfy their demands. Each customer has a deadline and the vehicles serving the customer must arrive at the location of the customer no later than the deadline. The MF-GWP determines the locations of p facilities on the plane, p > 1, allocations of customers to the facilities, and the speeds of the distribution vehicles so as to minimize the total amount of CO2 emission in the distribution system. We formulate this problem as a mixed integer second order cone programming (MISOCP) problem. This formulation turns out to be weak and therefore only small size instances can be solved to optimality within four hours. For larger size instances, a local search heuristic is proposed and some well-known heuristics developed for the multi-facility Weber problem, namely “location-allocation”, “transfer follow-up”, and “decomposition” are adapted for the MF-GWP. We use second order cone programming (SOCP) and the proposed MISOCP formulation as subproblems within the heuristics. We provide our computational experiments to compare the proposed solution methods in terms of solution quality and time. The results show that within a fixed computational time, even though the location-allocation heuristic is able to make more replications, the improvement heuristics considered, i.e., transfer or transfer followed by decomposition, usually find better solutions while using less number of replications. We also investigate how the total amount of CO2 emitted by distribution vehicles changes with respect to the number of facilities located. We argue that in several real life applications from different sectors including aviation and robotics, MF-GWP and its extensions or modifications can be used to reduce the CO2 emission or energy consumption. As an illustrative example, we show the applicability of the MF-GWP within an assembly line system, where the stations are fed by dedicated rail-guided vehicles.

Determination of the Pallet Quantity Using Simulation in the FMS for Aircraft Parts

This study deals with the case study on the pallet quantity determination problem for the flexible manufacturing system producing 32 different types of aircraft wing ribs which are major structures of an aircraft wings. A Korean company has constructed the WFMS (wing rib flexible manufacturing system) that is composed of several automated equipments such as the 5-axis machining centers, the RGV (rail guided vehicles)s, the AS/RS (automated storage and retrieval system), the loading/unloading stations, and so on. Pallets play a critical role in the WFMS to maintain high system utilization and continuous work flow between 5-axis machining machines and automated material handling devices. The discrete event simulation method is used to evaluate the performance of the WFMS under various pallet mix alternatives for wing rib manufacturing processes. Four performance measures including system utilization, throughput, lead-time and work in process inventory level are investigated to determine the best pallet mix alternative. The best pallet mix identified by the simulation study is adopted in setting up and operating a real Korean aircraft parts manufacturing shop. By comparing the real WFMS’s performances with those of the simulation study, we discussed the cause of performance difference observed and the necessity of developing the CPS (cyber physical system).

Belt-conveyor based efficient parallel storage system design and travel time model analysis

This paper presents a belt-conveyor based parallel storage system (PSS). Compared with the conventional AS/RS, it has advantages including more efficient utilisation of storage space, and faster storage and retrieval of products. The PSS consists of three components: the automated retrieval system (ARS), the automated storage system (ASS) and the compact storage rack (CSR). In the ARS, a vertical screw conveyor is used to facilitate the vertical movement of the unit loads, while a powered belt-conveyor is used for the horizontal dimension. Additionally, a powered conveyor system enables motion along the depth dimension, meaning each lane in the CSR is connected to several storage cells. Horizontal belt-conveyor and powered conveyor in the lane constitute cross-belt which causes the parallel process. On the other side of the rack, a unit load lift, a RGV lift, several rail-guided vehicles and a buffer rack constitute the ASS. Based on the system, we formulate separate travel-time models for ARS and ASS, under the assumption of randomised, uniformly distributed storage rack positions. Computer simulation with Matlab is used to validate the models, and optimise the automated storage system.

Indoor Autonomous Vehicle Navigation—A Feasibility Study Based on Infrared Technology

The application of autonomous vehicles has grown dramatically in recent years. Not only has the rail-guided vehicle (RGV) been used widely in traditional production lines, the automatic guided vehicle (AGV) has also been increasingly used. Positioning and path planning are two major functions of autonomous vehicles; however, there are many ways to fulfill the above requirements. The infrared remote control has been heavily and successfully used in home appliances for decades, which has encouraged us to apply this mature and cost-effective technology to an autonomous vehicle. By decoding the coded signal from the infrared light-emitting diode (LED), which is equipped on the ceiling, the autonomous vehicle can be positioned with an accuracy of less than 50 mm. On the other hand, by changing the beam pattern of infrared light from the ceiling, an invisible route can be produced on the ground. That is to say, instead of the traditional rail-guided method, these invisible paths can guide the autonomous vehicle. We have implemented a prototype of an autonomous vehicle system based on the above concept, with the aim of creating a simple and reliable approach for the navigation of an indoor autonomous vehicle.

Modelling of Rail Guided Vehicles serving an automated parts-to-picker system

An automated parts-to-picker picking system usually consists of an automated warehouse, with Automatic Storage and Retrieval Systems (AS/RS) that retrieve the Stock Keeping Units (SKUs) of the various needed products from their stocking locations, and of a picking area, with human operators or robots that pick the needed items in order to create a mixed shipping unit. The automated warehouse and the picking area are connected by an automated transportation system, which moves the SKUs from the warehouse to the picking stations and vice versa. The transportation system can be, for example, a ring rail conveyor on which various Rail Guided Vehicles (RGVs) are able to carry one SKU at a time. The present paper proposes a preliminary simulative analysis and, then, a mathematical formulation for this transportation system, useful to properly estimate the number of RGVs that are required to fulfill a certain picking throughput. In fact, it is shown that the picking throughput does not increase linearly with the number of vehicles employed, due to congestion issues.

자기부상시스템을 위한 교수-학습 최적화 알고리즘 기반의 퍼지 PID 제어기 설계

This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the conventional control methods. In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum parameters of fuzzy PID controller are selected by Teaching-Learning optimization. For the fitness function of Teaching-Learning optimization, the performance index of PID controller is used. To verify the performances of the proposed method, we use a Maglev model and compare the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

Energy-efficient rail guided vehicle routing for two-sided loading/unloading automated freight handling system

Rail-guided vehicles (RGVs) are widely employed in automated freight handling system (AFHS) to transport surging air cargo. Energy-efficient routing of such vehicles is of great interest for both financial and environmental sustainability. Given a multi-capacity RGV working on a linear track in AFHS, we consider its optimal routing under two-sided loading/unloading (TSLU) operations, in which energy consumption is minimized under conflict-avoidance and time window constraints. The energy consumption takes account of dynamics and routing-dependent gross weight of the RGV. And the conflict-avoidance constraints ensure conflict-free transport service under TSLU operations. The problem is formulated as a mixed-integer linear program, and solved by incorporating valid inequalities that exploit structural properties of the problem. The static problem model and solution approach are then integrated with a rolling-horizon approach to solve the dynamic routing problem where air cargo enters and departs from the system dynamically in time. Simulation results suggest that the proposed strategy is able to route a RGV to transport air cargo with an energy cost that is considerably lower than one of the most common heuristic methods implemented in current practice.

Design of Android-Based Remote Terminal System for RGV

This paper proposes a new method that utilized Android mobile phone to control the RGV (Rail Guided Vehicle)running through WIFI network to overcome the disadvantages of the traditional remote control can only control one-way communication for RGV in the Logistics and warehousing and realize two-way communication between the RGV and remote controller . The system is based on Android platform, and established a remote terminal system by all Android support resources. In this paper, it described the structure, function, software flow of the RGV remote terminal system and communication protocol between Android client and the RGV. The design is based on the Modbus TCP / IP protocol, to achieve a two-way communication between Android phone and the RGV.

Justification of Lever Arrangement Parameters for Friction-type Traction Gear

The paper reviews the current state and application potential of the friction-type traction gear as applied for rail-guided vehicles used in mining operations. It presents a design concept of a friction-type traction gear with a lever arrangement. A kinematic diagram of the lever arrangement is provided. Optimal geometry of the key elements of the lever arrangement is defined. The analytical model of the power load on the levers is developed. A correlation between the geometry of the key lever arrangement elements and the reduction rates was established with account for operating conditions of the traction vehicle. Design recommendations for friction-type traction gear are given based on the analysis of the obtained results.