scholarly journals Algorithms for Smooth, Safe and Quick Routing on Sensor-Equipped Grid Networks

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8188
Author(s):  
Giovanni Andreatta ◽  
Carla De Francesco ◽  
Luigi De Giovanni
Keyword(s):  
Public Transportation ◽  
Mathematical Formulation ◽  
Shortest Paths ◽  
Automated Guided Vehicles ◽  
Minimum Size ◽  
Routing Problem ◽  
Combinatorial Properties ◽  
Grid Networks ◽  
Minimum Number ◽  
Random Instances

Automation plays an important role in modern transportation and handling systems, e.g., to control the routes of aircraft and ground service equipment in airport aprons, automated guided vehicles in port terminals or in public transportation, handling robots in automated factories, drones in warehouse picking operations, etc. Information technology provides hardware and software (e.g., collision detection sensors, routing and collision avoidance logic) that contribute to safe and efficient operations, with relevant social benefits in terms of improved system performance and reduced accident rates. In this context, we address the design of efficient collision-free routes in a minimum-size routing network. We consider a grid and a set of vehicles, each moving from the bottom of the origin column to the top of the destination column. Smooth nonstop paths are required, without collisions nor deviations from shortest paths, and we investigate the minimum number of horizontal lanes allowing for such routing. The problem is known as fleet quickest routing problem on grids. We propose a mathematical formulation solved, for small instances, through standard solvers. For larger instances, we devise heuristics that, based on known combinatorial properties, define priorities, and design collision-free routes. Experiments on random instances show that our algorithms are able to quickly provide good quality solutions.

A Multi-Period Evacuation Vehicle Routing Problem Model

2014 ◽  
Vol 931-932 ◽  
pp. 578-582
Author(s):  
Sunarin Chanta ◽  
Ornurai Sangsawang
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
High Water ◽  
Mixed Integer ◽  
Routing Problem ◽  
Problem Model ◽  
Proposed Model ◽  
Evacuation Routing

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.

A new exact algorithm for the vehicle routing problem based onq-paths andk-shortest paths relaxations

1995 ◽  
Vol 61 (1) ◽  
pp. 21-43 ◽  
Author(s):  
Eleni Hadjiconstantinou ◽  
Nicos Christofides ◽  
Aristide Mingozzi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Shortest Paths ◽  
Exact Algorithm ◽  
Routing Problem

scholarly journals A Novel Parallel Ant Colony Optimization Algorithm for Warehouse Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Junqi Yu ◽  
Ruolin Li ◽  
Zengxi Feng ◽  
Anjun Zhao ◽  
Zirui Yu ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Automated Guided Vehicles ◽  
Stability And Convergence ◽  
Ant Colony Optimization Algorithm ◽  
Processing Efficiency ◽  
Minimum Number ◽  
The Stability

In order to improve the working efficiency of automated guided vehicles (AGVs) and the processing efficiency of fulfilling orders in intelligent warehouses, a novel parallel ant colony optimization algorithm for warehouse path planning is proposed. Through the interaction of pheromones among multiple subcolonies, the coevolution of multiple subcolonies is realized and the operational capability of the algorithm is improved. Then, a multiobjective function with the object of the shortest path and the minimum number of turns of the AGV is established. And the path satisfying this objective function is obtained by the proposed algorithm. In addition, the path is further smoothed by reducing the number of intermediate nodes. The results show that the stability and convergence rate of the algorithm are faster and more stable, compared to other algorithms, in generating paths for different complexity maps. The smoothing treatment of the path significantly reduces the number of turns and the path length in the AGV driving process.

Formulation of “Stability Equations” and Derivation of New Constructions with Complete Systems of Design-formulae for Variable-speed Control Mechanisms

1953 ◽  
Vol 167 (1) ◽  
pp. 319-339
Author(s):  
M. S. Frenkel
Keyword(s):  
Nuclear Physics ◽  
Characteristic Curve ◽  
Mathematical Formulation ◽  
Control Mechanisms ◽  
Variable Speed ◽  
Speed Governor ◽  
Minimum Number ◽  
Order Of Magnitude ◽  
The Stability ◽  
Stable Characteristic

Requirements for stability are formulated mathematically and, through the “transformatory operations of mathematics”, yield a series of “stability equations” of ascending order which are generally applicable, for example to control mechanisms, electronics†, nuclear physics, etc. From these stability equations, the equation of the stable characteristic curve of a governor, and the differential equations of the oscillations of a governor-engine system, are derived. It emerges that the first part of the new oscillatory equation is identical with the whole of the differential equation in the literature to date (unchanged since Maxwell 1868)‡, while the important second part, which consists of terms of the same order of magnitude as the first part and which is the only one containing the equation of the stable characteristic curve, is lacking in literature. The stability equations classify all possible constructions of variable-speed governor according to “order of stability”, which signifies important operating properties. This classification accounts for the known shortcomings of conventional types. The stability equations, combined with the mathematical formulation of practical requirements (speed-adjustment with only one actuating motion, etc.), lead to new basic types of variable-speed governor, with complete systems of design equations. In addition to determining all unknown dimensions, this set of equations is important because it derives constructions of which the complexity increases with order of stability and, furthermore, a simple construction which provides any required high order of stability with the minimum number of adjustable components.

IDENTIFYING CO-REGULATING MICRORNA GROUPS

2010 ◽  
Vol 08 (01) ◽  
pp. 99-115 ◽  
Author(s):  
JIYUAN AN ◽  
KWOK PUI CHOI ◽  
CHRISTINE A. WELLS ◽  
YI-PING PHOEBE CHEN
Keyword(s):  
Neurodegenerative Diseases ◽  
Target Genes ◽  
Signal To Noise Ratio ◽  
Statistical Tests ◽  
False Positive Rate ◽  
Target Prediction ◽  
Minimum Size ◽  
Supplementary Information ◽  
P Value ◽  
Minimum Number

Background: Current miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups. Our procedure involves a sequence of statistical tests: (1) identify genes that are highly probable miRNA targets; (2) determine for each such gene, the minimum number of miRNAs that co-regulate it with high probability; (3) find, for each such gene, the combination of the determined minimum size of miRNAs that co-regulate it with the lowest p-value; and (4) discover for each such combination of miRNAs, the group of genes that are co-regulated by these miRNAs with the lowest p-value computed based on GO term annotations of the genes. Results: Our method identifies 4, 3 and 2-term miRNA groups that co-regulate gene groups of size at least 3 in human. Our result suggests some interesting hypothesis on the functional role of several miRNAs through a "guilt by association" reasoning. For example, miR-130, miR-19 and miR-101 are known neurodegenerative diseases associated miRNAs. Our 3-term miRNA table shows that miR-130/19/101 form a co-regulating group of rank 22 (p-value =1.16 × 10-2). Since miR-144 is co-regulating with miR-130, miR-19 and miR-101 of rank 4 (p-value = 1.16 × 10-2) in our 4-term miRNA table, this suggests hsa-miR-144 may be neurodegenerative diseases related miRNA. Conclusions: This work identifies highly probable co-regulating miRNAs, which are refined from the prediction by computational tools using (1) signal-to-noise ratio to get high accurate regulating miRNAs for every gene, and (2) Gene Ontology to obtain functional related co-regulating miRNA groups. Our result has partly been supported by biological experiments. Based on prediction by TargetScanS, we found highly probable target gene groups in the Supplementary Information. This result might help biologists to find small set of miRNAs for genes of interest rather than huge amount of miRNA set. Supplementary Information:.

scholarly journals Optimal Routing and Scheduling of Charge for Electric Vehicles: A Case Study

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
J. Barco ◽  
A. Guerra ◽  
L. Muñoz ◽  
N. Quijano
Keyword(s):  
Energy Consumption ◽  
Electric Vehicles ◽  
Public Transportation ◽  
Differential Evolution Algorithm ◽  
Transportation Systems ◽  
Routing Problems ◽  
Routing Problem ◽  
Routing And Scheduling ◽  
Battery Degradation

There are increasing interests in improving public transportation systems. One of the proposed strategies for this improvement is the use of Battery Electric Vehicles (BEVs). This approach leads to a new challenge as the BEVs’ routing is exposed to the traditional routing problems of conventional vehicles, as well as the particular requirements of the electrical technologies of BEVs. Examples of BEVs’ routing problems include the autonomy, battery degradation, and charge process. This work presents a differential evolution algorithm for solving an electric vehicle routing problem (EVRP). The formulation of the EVRP to be solved is based on a scheme to coordinate the BEVs’ routing and recharge scheduling, considering operation and battery degradation costs. A model based on the longitudinal dynamics equation of motion estimates the energy consumption of each BEV. A case study, consisting of an airport shuttle service scenario, is used to illustrate the proposed methodology. For this transport service, the BEV energy consumption is estimated based on experimentally measured driving patterns.

scholarly journals Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

2021 ◽  
Vol 4 ◽  
Author(s):  
Marina Dorokhova ◽  
Christophe Ballif ◽  
Nicolas Wyrsch
Keyword(s):  
Reinforcement Learning ◽  
Electric Vehicles ◽  
Mathematical Formulation ◽  
Route Planning ◽  
Learning Approach ◽  
Training Procedure ◽  
Routing Problem ◽  
Policy Model ◽  
Model Free ◽  
Charging Stations

In the past few years, the importance of electric mobility has increased in response to growing concerns about climate change. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of electric vehicles (EVs). To mitigate the problem, the need for optimal route planning algorithms emerged. In this paper, we propose a mathematical formulation of the EV-specific routing problem in a graph-theoretical context, which incorporates the ability of EVs to recuperate energy. Furthermore, we consider a possibility to recharge on the way using intermediary charging stations. As a possible solution method, we present an off-policy model-free reinforcement learning approach that aims to generate energy feasible paths for EV from source to target. The algorithm was implemented and tested on a case study of a road network in Switzerland. The training procedure requires low computing and memory demands and is suitable for online applications. The results achieved demonstrate the algorithm’s capability to take recharging decisions and produce desired energy feasible paths.

scholarly journals Traveling salesman problem: approach to optimality

2014 ◽  
Vol 15 (2) ◽  
pp. 157-169
Author(s):  
Radosław Jadczak
Keyword(s):  
Traveling Salesman Problem ◽  
Vehicle Routing Problem ◽  
Decision Problem ◽  
Mathematical Formulation ◽  
Traveling Salesman ◽  
Chinese Postman Problem ◽  
Routing Problem ◽  
Solution Strategies ◽  
Chinese Postman ◽  
Single Vehicle

Abstract Traveling Salesman Problem (TSP) is a basic and one of the most important transportation problems in operational logistics. It is also known in the literature as a Chinese postman problem or single vehicle routing problem. TSP can be shortly described as follows. Vehicle starting from the selected city must visit a set of another cities exactly once and return to the starting city in such a way that the total distance of the route is minimized. In this paper first mathematical formulation of decision problem is presented. Then solution strategies of TSP are shown with selected algorithms as examples. In the last part of article, a computational results of selected methods are presented.

scholarly journals Solving Rich Vehicle Routing Problem Using Three Steps Heuristic

2019 ◽  
Vol 1 (1) ◽  
pp. 1-19
Author(s):  
Editor
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Optimization Problem ◽  
Maximum Load ◽  
Load Factor ◽  
Routing Problem ◽  
The Government ◽  
Hard And Soft Constraints

Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by the government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, the maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set.

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