scholarly journals Network Mobility Route Optimization Problem Statement

2007 ◽  
Author(s):  
C. Ng ◽  
P. Thubert ◽  
M. Watari ◽  
F. Zhao
Keyword(s):  
Optimization Problem ◽  
Route Optimization ◽  
Network Mobility ◽  
Problem Statement

Spatially Enabled Pipeline Route Optimization Model

2004 ◽  
Author(s):  
S. Raza Wasi ◽  
J. Darren Bender
Keyword(s):  
Decision Making ◽  
Optimization Problem ◽  
Environmental Design ◽  
Route Optimization ◽  
Design Criteria ◽  
Least Cost Path ◽  
Ahp Model ◽  
Spatial Decision Making ◽  
Pipeline Route ◽  
Analytical Tools

An interesting, potentially useful, and fully replicable application of a spatially enabled decision model is presented for pipeline route optimization. This paper models the pipeline route optimization problem as a function of engineering and environmental design criteria. The engineering requirements mostly deal with capital, operational and maintenance costs, whereas environmental considerations ensure preservation of nature, natural resources and social integration. Typically, pipelines are routed in straight lines, to the extent possible, to minimize the capital construction costs. In contrast, longer pipelines and relatively higher costs may occur when environmental and social considerations are part of the design criteria. Similarly, much longer pipelines are less attractive in terms of capital costs and the environmental hazard associated with longer construction area. The pipeline route optimization problem is potentially a complex decision that is most often undertaken in an unstructured, qualitative fashion based on human experience and judgement. However, quantitative methods such as spatial analytical techniques, particularly the least-cost path algorithms, have greatly facilitated automation of the pipeline routing process. In the past several interesting studies have been conducted using quantitative spatial analytical tools for finding the best pipeline route or using non-spatial decision making tools to evaluate several alternates derived through conventional route reconnaissance methods. Most of these studies (that the authors are familiar with) have concentrated on integrating multiple sources of spatial data and performing quantitative least-cost path analysis or have attempted to make use of non-spatial decision making tools to select the best route. In this paper, the authors present a new framework that incorporates quantitative spatial analytical tools with an Analytical Hierarchical Process (AHP) model to provide a loosely integrated but efficient spatial Decision Support System (DSS). Specifically, the goal is to introduce a fully replicable spatial DSS that processes both quantitative and qualitative information, balances between lowest-cost and lowest-impact routes. The model presented in this paper is implemented in a four step process: first, integration of multiple source data that provide basis for engineering and environmental design criteria; second, creation of several alternate routes; third, building a comprehensive decision matrix using spatial analysis techniques; and fourth, testing the alternative and opinions of the stakeholder groups on imperatives of AHP model to simplify the route optimization decision. The final output of the model is then used to carry out sensitivity analysis, quantify the risk, generate “several what and if scenarios” and test stability of the route optimization decision.

scholarly journals Wireless Sensor Network Route Optimization Based on Improved Ant Colony-Genetic

2015 ◽  
Vol 11 (9) ◽  
pp. 4 ◽  
Author(s):  
Wei Liu ◽  
Yongfeng Cui ◽  
Zhongyuan Zhao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Problem ◽  
Ant Colony ◽  
Route Optimization ◽  
Wireless Sensor ◽  
Multi Objective Optimization ◽  
Mutation Operation

The objective of this paper is focuses on route optimization, for a given wireless sensor network. We detail the significance of route optimization problem and the corresponding mathematical model. After analyzing the complex multi-objective optimization problem, Ant Colony Optimization (ACO) algorithm was introduced to search the best route. Inspired by Genetic Algorithm (GA), we embed two operations into ACO to refine it. First, every ant after achieving sink will be regarded as an individual such as that in GA. The crossover operation will be applied and then, the generated new ants will replace the weaker parents. Second, we designed a mutation operation for ants selecting next nodes to visit. Experimental results demonstrate that the proposed combination algorithm has significant enhancements than both GA and ACO. The lifetime of WSN can be extended and the coverage speed can be accelerated.

A secure and efficient batch binding update scheme for route optimization of nested NEtwork MObility (NEMO) in VANETs

2013 ◽  
Vol 36 (1) ◽  
pp. 284-292 ◽  
Author(s):  
Lo-Yao Yeh ◽  
Chun-Chuan Yang ◽  
Jee-Gong Chang ◽  
Yi-Lang Tsai
Keyword(s):  
Route Optimization ◽  
Network Mobility ◽  
Binding Update ◽  
Nested Network

A Membrane-Inspired Algorithm to Solve the Shipping Route Optimization Problem

2015 ◽  
Vol 12 (7) ◽  
pp. 1373-1377
Author(s):  
Tao Luo ◽  
Xiaoli Qiang ◽  
Yajun She ◽  
Zongyuan Yang
Keyword(s):  
Optimization Problem ◽  
Route Optimization
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