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Research on scheduling of the RGV system based on QPSO
Abstract
This paper presents an improved quantum behaved particle swarm optimization (QPSO) algorithm for rail guided vehicles system (RGV). First, a model of RGV system that using interleaving is proposed. Then an optimization methods based on QPSO is proposed , and a Gaussian mutation operator is defined to improve the algorithm's local convergence ability. The feasibility and effectiveness of proposed approach is illustrated by the simulation experiment.
... Literature [4] presents several algorithms for solving dynamic PDPTW model. RGV scheduling problem is one of the most important issues in material processing system [5,6]. The performance of material processing system has a decisive impact on the efficiency of the whole equipment [7]. ...
The intelligent machining system scheduling arrangement is through the RGV that can receive intelligent control of the system, so as to install raw materials and unload clinker for each CNC and coordinate the production of CNC in the whole system. This paper analyzes the order in which work orders are issued to RGV, comprehensively uses C++ and Matlab software to explore the scheduling scheme of intelligent machining system, combines time series and genetic algorithm to research and optimize the scheme, and continuously improves after mutation, and finally gives the optimal RGV scheduling strategy.
Abstract—This paper introduces a robotic system for electric energy meter detection, which is composed of AGV, RGV, 6-DOF manipulator and vision positioning. AGV is used to transport the electric energy meter from warehouse to transfer station for detection and send them back after detection. The manipulator is installed on a RGV and used to pick the electric energy meter to the detection platform with the help of RGV. All electric energy meters are placed back to their original position after detection. The manipulator end-effcector has three parallel pneumatic grippers that can grasp three meters at the same time. A camera and three barcode scanners are employed in the end-effector for positioning and reading barcode respectively. This system has been put into use in factory. I. I NTRODUCTION The electric energy meter detection is necessary before the electric energy meters are put into use. There are millions of newly produced electric energy meters waiting to be detected every year. This work is simple and boring but important and requires repeated manipulation to electric energy meters. Compared with picking and place, the detection takes much more time in this work (1)-(2). The process of the electric energy meter detection needs to carry the meters from the warehouse to the platform, detect for more than an hour or even several hours (three phase meter), and send them back to the warehouse after detection. If all these operations are done by humans, the cost and efficiency will be both beyond expectation. So we employ a robotic system that includes AGV, RGV and manipulator to complete all of these tasks. The industrial robots are mostly in the form of wheeled vehicles and manipulators. The wheeled vehicles, such as AGV and RGV, are usually used to transport materials for a long distance. The manipulators can machine surface, assemble parts, weld, spray paint and do many other complicated things. Those robots usually work together and complete all kinds of tasks coordinately, which can be called an integrated system in production. Researchers have already done much work about AGV (3)-(4), and the critical point is how to achieve automatic guide or path planning during locomotion. The automatic guide is widely used in industry and achieved by laying out magnetic things at its scheduled path. The AGV can also have
The neutron capture cross section of the neutron magic isotope
139La has been measured relative to that of 27Al
by means of the activation method. The fast neutrons were produced via
the 3H(d,n)4He reaction on Pd-300 neutron
generator. The natural high-purity La2O3 powder
was used as target material. Induced gamma activities were measured by a
high-resolution gamma-ray spectrometer with high-purity germanium (HPGe)
detector. Measurements were corrected for gamma-ray attenuations, random
coincidence (pile-up), dead time and fluctuation of neutron flux. The
new values for En=13.5±0.2, 14.1±0.2, and
14.8±0.2 MeV are found to be 1.30±0.08, 1.15±0.08
and 0.99±0.07 mb, respectively. Results were discussed and
compared with some corresponding values found in the literature.
This paper makes an exhaustive survey of various applications of Quantum inspired computational intelligence (QCI) techniques proposed till date. Definition, categorization and motivation for QCI techniques are stated clearly. Major Drawbacks and challenges are discussed. The significance of this work is that it presents an overview on applications of QCI in solving various problems in engineering, which will be very much useful for researchers on Quantum computing in exploring this upcoming and young discipline.
In pickup and delivery problems vehicles have to transport loads from origins to destinations without transshipment at intermediate locations. In this paper, we discuss several characteristics that distinguish them from standard vehicle routing problems and present a survey of the problem types and solution methods found in the literature.
We present DRIVE (Dynamic Routing of Independent VEhicles), a planning module to be incorporated in a decision support system for the direct transportation at Van Gend & Loos BV. Van Gend & Loos BV is the largest company providing road transportation in the Benelux with about 1400 vehicles transporting 160,000 packages from thousands of senders to tens of thousands of addressees per day. The heart of DRIVE is a branch-and-price algorithm. Approximation and incomplete optimization techniques as well as a sophisticated column management scheme have been employed to create the right balance between solution speed and solution quality. DRIVE has been tested by simulating a dynamic planning environment with real-life data and has produced very encouraging results. April 1996 Revision July 1996 1 Introduction Van Gend & Loos BV 1 is the largest company providing road transportation in the Benelux, which is the region consisting of Belgium, The Netherlands, and Luxembourg, 1 Van Gend & ...
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function. A method based on PSO was researched to solve the discrete Vehicle Routing Problem (VRP). The VRP was changed into a quasi-continuous problem by designing a new real coding. Constrained terms in VRP were processed by the penalty function. This proposed algorithm was applied to illustrate its higher searching efficiency in comparison with standard genetic algorithm and double populations genetic algorithm for VRP. Simulation results of several VRP examples demonstrated the effectiveness of this algorithm.
To apply Particle Swarm Optimization (PSO) algorithm oriented to continuous optimization problems in solving Traveling Salesman Problem (TSP), a new coding method based on the priority was put forward to transform the TSP from combinatorial optimization to continuous optimization. And the easy operation and high efficiency of the PSO were reserved. Then an innovative approach based on k-center algorithm was proposed with purpose of preventing PSO algorithm from local optimum. In this approach, the dissimilarity matrix was constructed by simple matching coefficient. Based on this, the k-center algorithm was used to execute cluster analysis on the particle swarm, which made particles exchange information with each other and search the best solution in a larger solution space than ever, so as to prevent the algorithm from local optimum. Finally, some TSPLIB examples were used to verify the effectiveness of the improved PSO in the TSP.
The particle swarm optimization algorithm combining with the ideal of the genetic algorithm, the ant colony algorithm and the simulated annealing algorithm are presented to solve the well known traveling salesman problem. Compared with the standard GA and simulated annealing algorithm, all hybrid particle swarm optimization algorithms are proved effective. Especially the hybrid particle swarm optimization algorithm with across strategy and mutation strategy is simple and effective. It can easily be modified for any combinatorial problem for which there is no good specialized algorithm up to now.
An automated Guided Vehicle (AGV) System with a limited number of vehicles cannot perform all of the transportation requests on demand. When a capacitated AGV system is used, then it is possible for the material handling system to become the bottleneck. There are many factors that affect the efficiency of an AGV system including how vehicles are routed and schedules. This paper presents a new dynamic approach to AGV routing. Simulation is used to study the effectiveness of this approach.
Automated Guided Vehicles (or AGVs for short) are now widely used in automated material handling systems. Much research has been done on developing path layout and algorithms for the optimal routing and scheduling of AGVs. However, with the drastically increased number of AGVs in some recent applications (e.g. in the order of a hundred in container terminals), algorithms that can achieve a high degree of concurrency of AGV moves are urgently needed. This paper presents a bi-directional path layout and an algorithm for routing AGVs. To route the vehicles without conflicts and to minimize the space requirement of the layout, critical conditions for certain key parameters of the path and vehicle are derived. We further show theoretically that a high degree of concurrency of AGV moves can be achieved, although the routing decision takes only a constant amount of time for each vehicle. The routing efficiency is analysed in terms of the distance traversed and the time requirement for AGVs to complete all pickup and drop-off (or P/D for short) jobs. Results of this study could form a basis for more complicated path layouts and routing algorithms.
Autonomous vehicle storage and retrieval systems utilize rail-guided vehicles moving in rectilinear paths within and between the aisles of unit load storage racks. Vertical vehicle movement is provided by lifts installed at fixed locations along the rack periphery. As an alternative to traditional automated storage and retrieval systems, autonomous vehicle systems enable users to match vehicle fleet size and the number of lifts to the level of transactions demand in a storage system. Analytical conceptualizing tools based on the features of autonomous vehicle systems are proposed for modelling expected performance as a function of key system attributes including storage capacity, rack configuration and fleet size. The models are demonstrated for a sample problem and compared with analytical conceptualizing tools used for automated storage and retrieval systems.
Travel time models are developed for automated storage/retrieval (AS/R) machines. The S/r machine travels simultaneously horizontally and vertically as it moves along a storage aisle. For randomized storage conditions expected travel times are determined for both single and dual command cycles. Alternative input/output (I/O) locations are considered. Additionally, various dwell point strategies for the storage/retrieval machine are examined. (Author)
This study addresses the issue of vehicle route planning in an AGV system. The two salient approaches to route planning static and dynamic are briefly discussed. The main thrust of the present work is development and testing of a proposed semi-dynamic time window constrained routeing strategy. Reserved time windows are placed on nodes indicating sequential crossing of nodes by the respective vehicles. Free time windows represent empty time slots available for vehicles to cross the nodes. Similarly, time windows representing the direction of traffic flow are placed on the bidirectional arcs. Based on these time windows, Dijkstra's algorithm is applied to find the least congested fastest routes for vehicles. A simulation study of a bidirectional flow path is then reported. The aim of the simulation experiment is to evaluate the system performance under the proposed routeing strategy vis-a-vis static routeing strategy. The results show that the proposed routeing strategy helps in reducing vehicle blocking time and improving the throughput potential of the system.
This paper presents an efficient algorithm for finding conflict-free shortest-time routes for automated guided vehicles moving in a bidirectional flow path network. The proposed algorithm is based on Dijkstra's shortest-path method. It maintains, for each node, a list of time windows reserved by scheduled vehicles and a list of free time windows available for vehicles to be scheduled. We introduce the concept of time window graph in which the node set represents the free time windows and the arc set represents the reachability between the free time windows. Then the algorithm routes the vehicles through the free time windows of the time window graph instead of the physical nodes of the flow path network. It requires O(v4n2) computations in the worst case, where v is the number of vehicles and n is the number of nodes.
This paper presents a new optimization algorithm – MCPSO, multi-swarm cooperative particle swarm optimizer, inspired by the phenomenon of symbiosis in natural ecosystems. MCPSO is based on a master–slave model, in which a population consists of one master swarm and several slave swarms. The slave swarms execute a single PSO or its variants independently to maintain the diversity of particles, while the master swarm evolves based on its own knowledge and also the knowledge of the slave swarms. According to the co-evolutionary relationship between master swarm and slave swarms, two versions of MCPSO are proposed, namely the competitive version of MCPSO (COM-MCPSO) and the collaborative version of MCPSO (COL-MCPSO), where the master swarm enhances its particles based on an antagonistic scenario or a synergistic scenario, respectively. In the simulation studies, several benchmark functions are performed, and the performances of the proposed algorithms are compared with the standard PSO (SPSO) and its variants to demonstrate the superiority of MCPSO.
The Probabilistic Traveling Salesman Problem (PTSP) is a variation of the classic Traveling Salesman Problem (TSP) and one of the most significant stochastic routing problems. In the PTSP, only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a new hybrid algorithmic nature inspired approach based on Particle Swarm Optimization (PSO), Greedy Randomized Adaptive Search Procedure (GRASP) and Expanding Neighborhood Search (ENS) Strategy is proposed for the solution of the PTSP. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm, the classic PSO and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in 13 out of 20 cases the proposed algorithm gives a new best solution.
This paper presents a review on design and control of automated guided vehicle systems. We address most key related issues including guide-path design, determining vehicle requirements, vehicle scheduling, idle-vehicle positioning, battery management, vehicle routing and deadlock resolution. We discuss and classify important models and results from key publications in literature, including often-neglected areas, such as idle-vehicle positioning and battery management. In addition, we propose a new dispatching-rule classification, a framework for design and control of automated guided vehicle systems, and suggest some fruitful research directions.
Based on the quantum-behaved particle swarm optimization (QPSO) algorithm, we formulate the philosophy of QPSO and introduce a so-called mainstream thought of the population to evaluate the search scope of a particle and thus propose a novel parameter control method of QPSO. After that, we test the revised QPSO algorithm on several benchmark functions and the experiment results show its superiority.
In this paper we present particle swarm optimization with Gaussian mutation combining the idea of the particle swarm with concepts from evolutionary algorithms. This method combines the traditional velocity and position update rules with the ideas of Gaussian mutation. This model is tested and compared with the standard PSO and standard GA. The comparative experiments have been conducted on unimodal functions and multimodal functions. PSO with Gaussian mutation is able to obtain a result superior to GA. We also apply the PSO with Gaussian mutation to a gene network. Consequently, it has succeeded in acquiring better results than those by GA and PSO alone.
The particle swarm algorithm adjusts the trajectories of a
population of “particles” through a problem space on the
basis of information about each particle's previous best performance and
the best previous performance of its neighbors. Previous versions of the
particle swarm have operated in continuous space, where trajectories are
defined as changes in position on some number of dimensions. The paper
reports a reworking of the algorithm to operate on discrete binary
variables. In the binary version, trajectories are changes in the
probability that a coordinate will take on a zero or one value.
Examples, applications, and issues are discussed