The origin-destination shortest path problem

Includes bibliographical references (p. 34-35).Supported by the Air Force. AFOR-88-0088 Supported by the National Science Foundation. DDM-8921835 Supported by UPS.Muralidharan S. Kodialam, James B. Orlin

A network flow model for load balancing in circuit-switched multicomputers

In multicomputers that utilize circuit switching or wormhole routing, communication overhead depends largely on link contention - the variation due to distance between nodes is negligible. This has a major impact on the load balancing problem. In this case, there are some nodes with excess load (sources) and others with deficit load (sinks) and it is required to find a matching of sources to sinks that avoids contention. The problem is made complex by the hardwired routing on currently available machines: the user can control only which nodes communicate but not how the messages are routed. Network flow models of message flow in the mesh and the hypercube were developed to solve this problem. The crucial property of these models is the correspondence between minimum cost flows and correctly routed messages. To solve a given load balancing problem, a minimum cost flow algorithm is applied to the network. This permits one to determine efficiently a maximum contention free matching of sources to sinks which, in turn, tells one how much of the given imbalance can be eliminated without contention

Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot

One of the technologies in the industrial world that utilizes robots is the delivery of goods in warehouses, especially in the goods distribution process. This is very useful, especially in terms of resource efficiency and reducing human error. The existing system in this process usually uses the line follower concept on the robot's path with a camera sensor to determine the destination location. If the line and destination are not detected by the sensor or camera, the robot's navigation system will experience an error. it can happen if the sensor is dirty or the track is faded. The aim of this research is to develop a robot navigation system for efficient goods delivery in warehouses by integrating odometry and Dijkstra's algorithm for path planning. Holonomic robot is a robot that moves freely without changing direction to produce motion with high mobility. Dijkstra's algorithm is added to the holonomic robot to obtain the fastest trajectory. by calculating the distance of the node that has not been passed from the initial position, if in the calculation the algorithm finds a shorter distance it will be stored as a new route replacing the previously recorded route. the distance traversed by the djikstra algorithm is 780 mm while a distance of 1100 mm obtains the other routes. The time for using the Djikstra method is proven to be 5.3 seconds faster than the track without the Djikstra method with the same speed. Uneven track terrain can result in a shift in the robot's position so that it can affect the travel data. The conclusion is that odometry and Dijkstra's algorithm as a planning system and finding the shortest path are very efficient for warehouse robots to deliver goods than ordinary line followers without Dijkstra, both in terms of distance and travel time

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

Neoj4 and SARMIX Model for Optimizing Product Placement and Predicting the Shortest Shopping Path

Product placement of top-selling items in highly visible aisles inside supermarkets plays a crucial role in enhancing customer shopping experience. Moreover, it is important for retailers to assure that their customers can effortlessly navigate the store and locate the items they are searching for in a timely manner. The research proposes a novel and effective approach that combines two methods; the SARIMAX model for forecasting sales of each product based on historical data; by using the predicted result, placing the most demanding item in highly visible aisles. And the use of Graph Database Management Systems (GDBMS) such as Neo4j to find the shortest path for consumers to navigate throughout the store to finish the shopping as per their shopping list. By leveraging the power of data analytics and machine learning, retailers can make data-driven decisions that result in improved sales andcustomer satisfaction. Retailers investing in these technologies and strategies will likely see a significant increase in customer satisfaction and sales

Analysis with modelling and simulation of the potential benefits of automation of ground controller functions.

Aviation demand has been growing steadily for many years, but airport infrastructure capacity cannot keep pace. We have recently seen new expansion projects at Europe's main airports to increase their capacity and maintain an excellent level of service. For Barcelona to maintain the level of major European airports such as Heathrow, Paris Charles de Gaulle, Amsterdam Schiphol and others, it is not enough to simply improve the infrastructure, but the operations are even more important. This improvement can be made using the latest technologies (automation, artificial intelligence,...). The purpose of this work is to implement a simulator that automates the functions of a ground controller. These functions are to guide and avoid conflicts between the gate and runways and vice versa. At the same time, by using this simulator, human errors will disappear and taxiing time will be optimised. To carry out this work, a simulator was created from scratch using a known scenario such as Barcelona - El Prat airport and data obtained from websites such as FlightRadar24 or FlightStats. During the simulations, the code was modified each time an aircraft did not perform the desired movement, and then simulated again. Once the 24 hours of movement data had elapsed, a series of variables allowed us to analyse the behaviour of the simulator. Finally, it can be said that the programme does not generate delay, and that it substantially improves airport operations by minimising queues before the runway. It is also true that it does not produce the same improvement in departures as in arrivals. This simulator is a first step towards the automation of air traffic controllers' functions, demonstrating that it has both economic and operational benefits

The disjoint multipath challenge: multiple disjoint paths guaranteeing scalability

The multipath challenge is a research line in continuous development because of its multiple benefits, however, these benefits are overshadowed by scalability, which goes down considerably when the paths are multiple and disjoint. The disjointness aggregates an extra value to the multiple paths, but it also implies more complex mathematical operations that increase the computational cost. In fact, diverse proposals exist that try to increase scalability by limiting the number of paths obtained to the minimum possible (two-disjoint paths), which is enough for backup applications but not for other purposes. This paper presents an algorithm that solves these drawbacks by discovering multiple disjoint paths among multiple nodes in an efficient way, while keeping bounded the computational cost and ensuring scalability. The proposed algorithm has been validated thoroughly by performing a theoretical analysis, bolstered afterwards by an exhaustive experimental evaluation. The collected results are promising, our algorithm reduces the time spent to obtain the disjoint paths regarding its competitors between one and three orders of magnitude, at the cost of a slight decrease in the number of paths discovered.Comunidad de MadridJunta de Comunidades de Castilla-La Manch

Shortest path algorithms for dynamic transportation networks

Over the last decade, many interesting route planning problems can be solved by finding the shortest path in a weighted graph that represents a transportation network. Such networks are private transport networks or timetabled public transportation networks. In the shortest path problem, every network type requires different algorithms to compute one or more than one shortest path. However, routing in a public transportation network is completely different and is much more complex than routing in a private transport network, and therefore different algorithms are required. For large networks, the standard shortest path algorithms - Dijkstra's algorithm (1959) and Bellman's algorithm (1958)- are too slow. Consequently, faster algorithms have been designed to speed up the search. However, these algorithms often consider only the simplest scenario of finding an optimal route on a graph with static real edge costs. But real map routing problems are often not that simple – it is often necessary to consider time-dependent edge costs. For example, in public transportation routing, consideration of the time-dependent model of these networks is mandatory. However, there are a number of transportation applications that use informed search algorithms (where the algorithm uses heuristics that guide the search toward the destination), rather than one of the standard static shortest path algorithms. This is primarily due to shortest paths needing to be rapidly identified either because an immediate response is required. For example, the A* algorithm (Nilsson, 1971) is widely used in artificial intelligence. Heuristic information (in the form of estimated distance to the destination) is used to focus the search towards the destination node. This results in finding the shortest path faster than the standard static search algorithms. Road traffic congestion has become an increasingly significant problem in a modern society. In a dynamic traffic environment, traffic conditions are time-dependent. For instance, when travelling from home to the work, although an optimal route can be planned prior to departure based on the traffic conditions at that time, it may be necessary to adjust the route while en route because traffic conditions change all the time. In some cases, it is necessary to modify the travelling route from time to time and re-plan a new route from the current location to the destination, based on the real-time traffic information. The challenge lies in the fact that any modification to the optimal route to adapt to the dynamic environment necessitates speeding up of the search efforts. Among the algorithms suggested for the dynamic shortest path problem is the algorithm of Lifelong Planning A* algorithm (LPA*) (Koenig, Likhachev and Furcy, 2004). This algorithm has been given this name because of its ability to reuse information from previous searches. It is used to adjust a shortest path to adapt to the dynamic transportation network. Search space and fast shortest path queries can be used for finding fastest updated route on road and bus networks. Consequently, the efficient processing of both types of queries is of first-rate significance. However, most search methods focus only on one type of query and do not efficiently support the other. To address this challenge, this research presents the first novel approach; an Optimised Lifelong Planning A* (OLPA*) algorithm. The OLPA* used an appropriate data structure to improve the efficiency of the dynamic algorithms implementation making it capable of improving the search performance of the algorithm to solve the dynamic shortest path problem, which is where the traveller may have to re-compute the shortest path while travelling in a dynamic transportation environment. This research has also proposed bi-directional LPA* (BLPA*) algorithm. The proposed algorithm BLPA* used bi-directional search strategy and the main idea in this strategy is to divide the search problem into two separate problems. One search proceeds forwards from the start node, while the other search proceeds backwards from the end node. The solution requires the two search problems to meet at one middle node. The BLPA* algorithm has the same overall structure as the LPA* algorithm search, with some differences that the BLPA* contains a priority queue for each direction. This research presented another algorithm that designed to adaptively derive the shortest path to the desired destination by making use of previous search results and reducing the total execution time by using the benefits of a bi-directional search strategy . This novel algorithm has been called the bi-directional optimised Lifelong A* algorithm (BiOLPA*). It was originally proposed for road transport networks and later also applied to public transportation networks. For the road transport network, the experimental results demonstrate that the proposed incremental search approach considerably outperforms the original approach method, which recomputed the shortest path from scratch each time without utilization of the previous search results. However, for public transportation, the significant problem is that it is not possible to apply a bi-directional search backwards using estimated arrival time. This has been further investigated and a better understanding of why this technique fails has been documented. While the OLPA* algorithms give an impressive result when applied on bus network compared with original A* algorithms, and our experimental results demonstrate that the BiOLPA* algorithm on road network is significantly faster than the LPA*, OLPA* and the A* algorithms, not only in terms of number of expansion nodes but also in terms of computation time

Load Balancing in a Network using Ant Colony Optimization Technique

This thesis describes a method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between nodes; nodes carrying an excess of traffic can become congested, causing calls to be lost. In addition to calls, the network also supports a population of simple mobile agents with behaviours modelled on the trail laying abilities of ants. The ants move across the network between randomly chosen pairs of nodes; as they move they deposit simulated pheromones as a function of their distance from their source node, and the congestion encountered on their journey. They select their path at each intermediate node according the distribution of simulated pheromones at each node. Calls between nodes are routed as a function of the pheromone distributions at each intermediate node. The performance of the network is measured by the average no of hops taken to complete the calls. In this thesis ,the results of using the antbased control (ABC) are compared with those achieved by using fixed shortestpath routes,(dijkstra’s algorithm) used in network management. The ABC system is shown to result in fewer call failures than the other methods, while exhibiting many attractive features of distributed control