The Container Truck Route Optimization Problem by the Hybrid PSO-ACO Algorithm

2017 ◽  
pp. 640-648
Author(s):  
Yi Liu ◽  
Mao Feng ◽  
Sabina Shahbazzade
Keyword(s):  
Optimization Problem ◽  
Route Optimization

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 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

Logistics Route Optimization Based on Improved Fish Swarm Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1761-1764
Author(s):  
Feng Kai Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Low Cost ◽  
Ant Colony ◽  
Route Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Logistics Distribution ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

In order to achieve a low cost and low exhaust pollution in logistics distribution path. In view of the shortages of existing genetic algorithm and ant colony algorithm which have the characteristics of some limitations, such as ant colony algorithm's convergence slow, easy going, the characteristics of such as genetic algorithm premature convergence in the process of path optimization, process complex, the paper proposed the improved artificial fish swarm algorithm in order to solve logistics route optimization problem. At last, through simulation experiment, the improved artificial fish swarm algorithm is proved correct and effective.

A Methodology and Modelling Technique for Taxi Path Optimization

2013 ◽  
Vol 711 ◽  
pp. 816-821
Author(s):  
Zhi Ping Hou ◽  
Feng Jin ◽  
Qin Jian Yuan ◽  
Yong Yi Li
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Routing Problem ◽  
Control Center ◽  
Material Resources ◽  
New Type

Vehicle Routing Problem (VRP) is a typical combinatorial optimization problem. A new type of bionic algorithm-ant colony algorithm is very appropriate to solve Vehicle Routing Problem because of its positive feedback, robustness, parallel computing and collaboration features. In view of the taxi route optimization problem, this article raised the issue of the control of the taxi, by using the Geographic Information System (GIS), through the establishment of the SMS platform and reasonable taxi dispatch control center, combining ant colony algorithm to find the most nearest no-load taxi from the passenger, and giving the no-load taxi the best path to the passenger. Finally this paper use Ant Colony laboratory to give the simulation. By using this way of control, taxis can avoid the no-load problem effectively, so that the human and material resources can also achieve savings.

Research and Application of Genetic Algorithm in Material Handling Route for Assembly Line of Airbag

2011 ◽  
Vol 299-300 ◽  
pp. 1217-1220
Author(s):  
Jian Ye Wan ◽  
Xin Jiang ◽  
Yun Peng Wang
Keyword(s):  
Genetic Algorithm ◽  
Material Handling ◽  
Optimization Problem ◽  
Assembly Line ◽  
Optimization Method ◽  
Route Optimization ◽  
Actual Situation ◽  
Production Logistics ◽  
Materials Handling ◽  
Route Problem

According to handling route of assembly line of airbag optimization problem, handling path planning mathematical model is established. Based on natural coding genetic algorithm, combining with JH company assembly line of airbag layout and the actual situation of material handling, using MATLAB software to realize the genetic algorithm, production logistics fields materials handling route problem in the optimization method is put forward. To solve production enterprise assembly line material handling route optimization problem, and has certain directive role.

A Route Optimization Problem in Electrical PCB Inspections: Pickup and Delivery TSP-Based Formulation

2016 ◽  
pp. 193-205
Author(s):  
Hideki Katagiri ◽  
Qingqiang Guo ◽  
Hongwei Wu ◽  
Hiroshi Hamori ◽  
Kosuke Kato
Keyword(s):  
Optimization Problem ◽  
Route Optimization ◽  
Pickup And Delivery

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

scholarly journals A Novel Multi-Objective Artificial Bee Colony Algorithm for the QoS Based Wireless Route Optimization Problem

2012 ◽  
Vol 6-7 ◽  
pp. 445-451
Author(s):  
Chang Sheng Zhang ◽  
Ming Kang Ren ◽  
Bin Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Route Optimization ◽  
Test Problems ◽  
Nsga Ii ◽  
Solution Quality ◽  
Artificial Bee Colony Optimization ◽  
Multi Objective ◽  
Bee Colony

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distributed solutions. The proposed algorithm was evaluated on a set of different scale test problems and compared with the recently proposed popular NSGA-II based multi-objective optimization algorithm. The experimental results reveal very encouraging results in terms of the solution quality and the processing time required.

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