Development of deterministic collision-avoidance algorithms for routing automated guided vehicles

A manufacturing job spends a small portion of its total flow time being processed on machines, and during the remaining time, either it is in a queue or being transported from one work center to another. In a fully automated material-handling environment, automated guided vehicles (AGV) perform the function of transporting the jobs between workstations, and high operational costs are involved in these material-handling activities. Consequently, the AGV route schedule dictates subsequent work-center scheduling. For an AGV job transportation schedule to be effective, the issue of collisions amongst AGV during travel needs to be addressed. Such collisions cause stalemate situations that potentially disrupt the flow of materials in the job shop, adding to the non-value time of job processing, and thus, increase the material handling and inventory holding costs. The current research goal was to develop a methodology that could effectively and efficiently derive optimal AGV routes for a given set of transportation requests, considering the issue of collisions amongst AGV during travel. As part of the solution approach in the proposed work, an integer linear program was formulated in Phase I with the capability of optimally predicting the AGV routes for a deterministic set of transportation requests. Collision avoidance constraints were developed in this model. The model was programmed using OPL / Visual Basic, and the program feasibility were experimentally analyzed for different problem domain specifications. Due to the complexity and combinatorial nature of the formulation in Phase I, computationally it was expected to be NP-Hard. Hence, to improve the computation prediction capability (estimation of upper bounds), it was required that in Phase II, heuristics be developed to relax the computational complexity of the original problem. In Phase III, experimental techniques were used to compute the lower and upper bounds of the original problem. The performances of the different heuristics were compared using experimental analysis

Route planning of automated guided vehicles for container logistics

Automated guided vehicles (AGVs) are widely used in container terminals for the movement of material from shipping to the yard area and vice versa. Research in this area is directed toward the development of a path layout design and routing algorithms for container movement. The problem is to design a path layout and a routing algorithm that will route the AGVs along the bi-directional path so that the distance traveled will be minimized. This thesis presents a bi-directional path flow layout and a routing algorithm that guarantee conflict-free, shortest time routes for AGVs. Based on the path layout, a routing algorithm and sufficient, but necessary conditions, mathematical relationships are developed among certain key parameters of vehicle and path. A high degree of concurrency is achieved in the vehicle movement. The routing efficiency is analyzed in terms of the distance traveled and the time required for AGVs to complete all pickup and drop-off jobs. Numerical results are presented to compare performance of the proposed model. The research provides the foundation for a bi-directional path layout design and routing algorithms that will aid the designer to develop complicated path layouts

Automatic Routing System for Intelligent Warehouses

Automation of logistic processes is essential to improve productivity and reduce costs. In this context, intelligent warehouses are becoming a key to logistic systems thanks to their ability of optimizing transportation tasks and, consequently, reducing costs. This paper initially presents briefly routing systems applied on intelligent warehouses. Then, we present the approach used to develop our router system. This router system is able to solve traffic jams and collisions, generate conflict-free and optimized paths before sending the final paths to the robotic forklifts. It also verifies the progress of all tasks. When a problem occurs, the router system can change the task priorities, routes, etc. in order to avoid new conflicts. In the routing simulations, each vehicle executes its tasks starting from a predefined initial pose, moving to the desired position. Our algorithm is based on Dijkstra's shortest path and the time window approaches and it was implemented in C language. Computer simulation tests were used to validate the algorithm efficiency under different working conditions. Several simulations were carried out using the Player/Stage Simulator to test the algorithms. Thanks to the simulations, we could solve many faults and refine the algorithms before embedding them in real robots.Comment: 2010 IEEE International Conference on Robotics and Automation, International workshop on Robotics and Intelligent Transportation System, Full Day Workshop, May 7th 2010, Anchorage, Alaska. Organizers,Christian Laugier (INRIA, France), Ming Lin (University of North Carolina, USA), Philippe Martinet IFMA and LASMEA, France),Urbano Nunes (ISR, Portugal

An adaptive routing approach for personal rapid transit

Personal Rapid Transit (PRT) is a public transportation mode, in which small automated vehicles transport passengers on demand. Central control of the vehicles leads to interesting possibilities for optimized routings. The complexity of the involved routing problems together with the fact that routing algorithms for PRT essentially have to run in real-time often leads to the choice of fast greedy approaches. The most common routing approach is arguably a sequential one, where upcoming requests are greedily served in a quickest way without interfering with previously routed vehicles. The simplicity of this approach stems from the fact that a chosen route is never changed later. This is as well the main drawback of it, potentially leading to large detours. It is natural to ask how much one could gain by using a more adaptive routing strategy. This question is the main motivation of this article. In this paper, we first suggest a simple mathematical model for PRT, and then introduce a new adaptive routing algorithm that repeatedly uses solutions to an LP as a guide to route vehicles. Our routing approach incorporates new requests in the LP as soon as they appear, and reoptimizes the routing of all currently used vehicles, contrary to sequential routing. We provide preliminary computational results that give first evidence of the potential gains of an adaptive routing strategy, as used in our algorithm.National Science Foundation (U.S.) (Grants CCF-1115849 and CCF-0829878)United States. Office of Naval Research (Grants N00014-11-1-0053 and N00014-09-1-0326

Famas-NewCon: A generator program for stacking in the reference case

The FAMAS-Newcon project aims at developing new logistic control structures for a containerterminal capable of handling jumbo container ships within 24 hours. In one of the subprojectsstacking aspects is studied. This report describes a generator model in which on the basis of a global description arrival and departure moments of individual containers are generated for a medium term (15 weeks). The global description consists of a specification of the modal split in terms of containers handled by jumbo's, deepsea, shortsea, rail, barge and trucks, next a specification of the number and type of containers transported by an individual jumbo and deepsea and finally a specification of the dwell time. The output of the generator program is a file with the following container information: arrival and departure times,import/export modality and the import/export ship in case that is a deepsea or jumbo. This file serves as the input of a stacking program which is described in a sequel report. The advantage of this construction is that several stacking strategies can be compared with the same arrival and departures of containers.generator program;stacking;logistic control structures

Facility Layout Linked with Scheduling Problem by Genetic Algorithm and Tabu Search

In this paper, we propose a method to solve simultaneously facility layout problem and scheduling problem. About a initial random layout planning, the production scheduling and the transportation scheduling of AGV are obtained by using priority rules. From the obtained transportation scheduling, the critical transportation and the closeness rating are obtained. Facility layout is renewed by the combined procedure of genetic algorithm and tabu search in order to reduce the material handling cost. By using this renewed facility layout, the production scheduling and the transportation scheduling of AGV are also revised until no further improvement is possible

Cooperation of Production, Product Handling and TransferScheduling for Semiconductor Fabrication

Various kind of productions are made in semiconductor fabrications, where it employs the production system with multiprocesses and multiple Automated Guided Vehicles(AGVs) for transportation. It is difficult to optimize planning of production and transportation simultaneously because of the complicated flow of semifinished products. This paper describes the formulations of production scheduling, transportation routing and sequence planning of material handling system, and algorithm for simultaneous optimization of plannings by using solution space reduction and simulated annealing method. In this paper, all production system is decomposed to the production scheduling problem, transportation routing problem by AGVs and sequence planning of material handling system with managing stockers and buffers. Production scheduling problem and transportation routing problem are solved by the optimization algorithm using the decomposition routing problem. Sequence planning of material handling robot problem is solved by the algorithm using simulated annealing method

Parallel Simulation of AGVs in Container Port Operations

Abstract We describe parallel simulations of an Automated Guided Vehicle (AGV) system for the container handling at a port. The AGV system is modelled with a time-driven approach and executed on efficient simulation engines implemented by using Cilk, a multi-threaded parallel programming language developed at MIT. The speedup results of the AGV simulation over sequential versions are documented. We also present congestion control schemes of our AGV routing system

Performance evaluation of flexible manufacturing systems under uncertain and dynamic situations

The present era demands the efficient modelling of any manufacturing system to enable it to cope with unforeseen situations on the shop floor. One of the complex issues affecting the performance of manufacturing systems is the scheduling of part types. In this paper, the authors have attempted to overcome the impact of uncertainties such as machine breakdowns, deadlocks, etc., by inserting slack that can absorb these disruptions without affecting the other scheduled activities. The impact of the flexibilities in this scenario is also investigated. The objective functions have been formulated in such a manner that a better trade-off between the uncertainties and flexibilities can be established. Consideration of automated guided vehicles (AGVs) in this scenario helps in the loading or unloading of part types in a better manner. In the recent past, a comprehensive literature survey revealed the supremacy of random search algorithms in evaluating the performance of these types of dynamic manufacturing system. The authors have used a metaheuristic known as the quick convergence simulated annealing (QCSA) algorithm, and employed it to resolve the dynamic manufacturing scenario. The metaheuristic encompasses a Cauchy distribution function as a probability function that helps in escaping the local minima in a better manner. Various machine breakdown scenarios are generated. A ‘heuristic gap’ is measured, and it indicates the effectiveness of the performance of the proposed methodology with the varying problem complexities. Statistical validation is also carried out, which helps in authenticating the effectiveness of the proposed approach. The efficacy of the proposed approach is also compared with deterministic priority rules