scholarly journals Multi-Objective Shortest Path Model for Optimal Route between Commercial Cities on America

2019 ◽  
pp. 1394-1403
Author(s):  
Mohammed S. Ibrahim
Keyword(s):  
Shortest Path ◽  
Transportation Network ◽  
Optimal Path ◽  
Path Model ◽  
Multi Objective ◽  
Proposed Model ◽  
Shortest Distance ◽  
Lexicographic Method ◽  
Package Program ◽  
Us Cities

The traditional shortest path problem is mainly concerned with identifying the associated paths in the transportation network that represent the shortest distance between the source and the destination in the transportation network by finding either cost or distance. As for the problem of research under study it is to find the shortest optimal path of multi-objective (cost, distance and time) at the same time has been clarified through the application of a proposed practical model of the problem of multi-objective shortest path to solve the problem of the most important 25 commercial US cities by travel in the car or plane. The proposed model was also solved using the lexicographic method through package program Win-QSB 2.0 for operational research applications.

Towards a Multi Objective Path Optimisation for Car Navigation

2011 ◽  
Vol 65 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Ching-Sheng Chiu ◽  
Chris Rizos
Keyword(s):  
Selection Criteria ◽  
Empirical Studies ◽  
Route Selection ◽  
Multi Objective ◽  
Optimal Paths ◽  
Proposed Model ◽  
Shortest Distance ◽  
Shortest Path Algorithms ◽  
Car Navigation System ◽  
Car Navigation

In a car navigation system the conventional information used to guide drivers in selecting their driving routes typically considers only one criterion, usually the Shortest Distance Path (SDP). However, drivers may apply multiple criteria to decide their driving routes. In this paper, possible route selection criteria together with a Multi Objective Path Optimisation (MOPO) model and algorithms for solving the MOPO problem are proposed. Three types of decision criteria were used to present the characteristics of the proposed model. They relate to the cumulative SDP, passed intersections (Least Node Path – LNP) and number of turns (Minimum Turn Path – MTP). A two-step technique which incorporates shortest path algorithms for solving the MOPO problem was tested. To demonstrate the advantage that the MOPO model provides drivers to assist in route selection, several empirical studies were conducted using two real road networks with different roadway types. With the aid of a Geographic Information System (GIS), drivers can easily and quickly obtain the optimal paths of the MOPO problem, despite the fact that these paths are highly complex and difficult to solve manually.

Research on Optimal Vehicle Path in Intelligent Transportation

2014 ◽  
Vol 644-650 ◽  
pp. 2615-2618
Author(s):  
Wen Ming Yu
Keyword(s):  
Data Storage ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Rapid Development ◽  
Optimal Path ◽  
Intelligent Transportation ◽  
Proposed Model ◽  
Planning Algorithm ◽  
Single Vehicle ◽  
Vehicle Path

The rapid development of economy and science and technology is quite obvious, which makes cars becoming more and more widely into the general family life. With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, there is the basic organizational framework for intelligent transportation, intelligent transportation network proposed model and its data storage structure, and the important influence on optimal path trajectory intelligent transportation planning. This paper analyzes intersection road network in the distribution based on computer graphics language, type and grade roads. The dynamic mathematical model of single vehicle is used vehicle planning algorithm based on period, effectively avoided the traffic road, reduce vehicle travel cost, improve the real-time effect and accuracy of the vehicle dynamic path. We hope the results and researches could combine with reality in order to reduce traffic congestion.

scholarly journals IMPLEMENTASI ALGORITMA DIJKSTRA UNTUK MENENTUKAN JALUR TERPENDEK DALAM DISTRIBUSI BARANG

2021 ◽  
Vol 2 (2) ◽  
pp. 194-203
Author(s):  
Ikhsan Baharudin ◽  
Ahmad Jaka Purwanto ◽  
Teguh Rahayu Budiman ◽  
Muchammad Fauzi
Keyword(s):  
Shortest Path ◽  
Manufacturing Industry ◽  
Optimal Path ◽  
Transportation Costs ◽  
Project Work ◽  
Shortest Route ◽  
Regular Customer ◽  
Shortest Distance ◽  
The Cost ◽  
A Company

PT. X is a company domiciled in Gedebage, Bandung which is engaged in the manufacturing industry by producing precision parts using CNC machines. PT. X is a sub-contracting company that usually serves project work from other companies. PT. Y and PT. Z is a regular customer who often works with PT. X. So that PT. X often sends finished products directly to PT. Y who is domiciled on Jl. Gatot Subroto, Bandung and also PT. Z who is domiciled on Jl. Pajajaran, Bandung. To minimize the cost of distribution of goods, PT. X must determine an adequate path taking into account the optimization of transportation costs. One of the variables that affect transportation costs is distance. It is assumed that the optimal path for transportation costs is the shortest distance using the Dijkstra method. This test uses data from Google Maps to find out the distance to each destination, making it easier to get the shortest path. Obtained the shortest path from PT. X to PT.Y is 12.3 Km via West Java Police then Carefour, while the shortest route is from PT. Y to PT. Z is 10.7 Km via Simpang Lima then Vie Hotel Westhoff. So that the optimal total mileage for distributing goods is 23 Km.

scholarly journals Shortest Path Algorithm in Dynamic Restricted Area Based on Unidirectional Road Network Model

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 203
Author(s):  
Haitao Wei ◽  
Shusheng Zhang ◽  
Xiaohui He
Keyword(s):  
Network Model ◽  
Shortest Path ◽  
Road Network ◽  
Transportation Network ◽  
Optimal Path ◽  
Network Routing ◽  
Navigation Systems ◽  
Search Performance ◽  
Statistical Parameters ◽  
The Real

Accurate and fast path calculation is essential for applications such as vehicle navigation systems and transportation network routing. Although many shortest path algorithms for restricted search areas have been developed in the past ten years to speed up the efficiency of path query, the performance including the practicability still needs to be improved. To settle this problem, this paper proposes a new method of calculating statistical parameters based on a unidirectional road network model that is more in line with the real world and a path planning algorithm for dynamically restricted search areas that constructs virtual boundaries at a lower confidence level. We conducted a detailed experiment on the proposed algorithm with the real road network in Zhengzhou. As the experiment shows, compared with the existing algorithms, the proposed algorithm improves the search performance significantly in the condition of optimal path under the premise of ensuring the optimal path solution.

scholarly journals Optimal Route Computation for Public Transport with Minimum Travelling Time & Travel Cost: A Case Study of Pokhara City

2019 ◽  
Vol 1 (1) ◽  
pp. 79-86
Author(s):  
R. Thapa ◽  
J.K. Shrestha
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Public Transportation ◽  
Transportation Network ◽  
Optimal Path ◽  
Travel Cost ◽  
Transit Network ◽  
Final Destination ◽  
Travelling Time

In road networks, it is imperative to discover a shortest way to reach the final destination. When an individual is new to a place, lots of time is wasted in finding the destination. With the advancement of technology, various navigation applications have been developed for guiding private vehicles, but few are designed for public transportation. This study is solely concentrated on finding the possible shortest path in terms of minimum time and cost to reach specific destination for an individual. It requires an appropriate algorithm to search the shortest path. With the implementation of Dijkstra’s algorithm, the shortest path with respect to minimum travel time and travel cost was computed. Public transportation network of Pokhara city was taken for the case study of this research. The results of this analysis indicated that when the “time” impedance was used by the algorithm, it generated the shortest path between the origin and destination along with the path to be followed. This study formulates a framework for generating itinerary for passengers in a transit network that allows the user to find the optimal path with minimum travel time and cost.

Fuzzy Constrained Shortest Path Problem for Location-Based Online Services

2021 ◽  
Vol 29 (02) ◽  
pp. 231-248
Author(s):  
Ali Abbaszadeh Sori ◽  
Ali Ebrahimnejad ◽  
Homayun Motameni ◽  
Jose Luis Verdegay
Keyword(s):  
Shortest Path ◽  
Spatial Information ◽  
Optimal Path ◽  
Weather Conditions ◽  
Online Services ◽  
Constrained Shortest Path ◽  
Proposed Model ◽  
Precise Prediction ◽  
Fuzzy Cost ◽  
Predetermined Time

One of the important issues under discussion connected with traffic on the roads is improving transportation. In this regard, spatial information, including the shortest path, is of particular importance due to the reduction of economic and environmental costs. Here, the constrained shortest path (CSP) problem which has an important application in location-based online services is considered. The aim of this problem is to find a path with the lowest cost where the traversal time of the path does not exceed from a predetermined time bound. Since precise prediction of cost and time of the paths is not possible due to traffic and weather conditions, this paper discusses the CSP problems with fuzzy cost and fuzzy time. After formulating the CSP problem an efficient algorithm for finding the constrained optimal path is designed. The application of the proposed model is presented on a location-based online service called Snap.

scholarly journals Neural network for optimization of routing in communication networks

2006 ◽  
Vol 19 (2) ◽  
pp. 317-329 ◽  
Author(s):  
Nenad Kojic ◽  
Irini Reljin ◽  
Branimir Reljin
Keyword(s):  
Neural Network ◽  
Communication Networks ◽  
Shortest Path ◽  
Routing Algorithm ◽  
Uncertain Data ◽  
Optimal Path ◽  
Traffic Conditions ◽  
Proposed Model ◽  
Computational Speed ◽  
Neural Network Algorithm

The efficient neural network algorithm for optimization of routing in communication networks is suggested. As it was known from literature different optimization and ill-defined problems may be resolved using appropriately designed neural networks, due to their high computational speed and the possibility of working with uncertain data. Under some assumptions the routing in packet-switched communication networks may be considered as optimization problem, more precisely, as a shortest-path problem. The Hopfield-type neural network is a very efficient tool for solving such problems. The suggested routing algorithm is designed to find the optimal path, meaning, the shortest path (if possible), but taking into account the traffic conditions: the incoming traffic flow, routers occupancy, and link capacities, avoiding the packet loss due to the input buffer overflow. The applicability of the proposed model is demonstrated through computer simulations in different traffic conditions and for different full-connected networks with both symmetrical and non-symmetrical links.

scholarly journals Mehar Approach for Finding Shortest Path in Supply Chain Network

2021 ◽  
Vol 13 (7) ◽  
pp. 4016
Author(s):  
Tanveen Kaur Bhatia ◽  
Amit Kumar ◽  
Srimantoorao S. Appadoo ◽  
Yuvraj Gajpal ◽  
Mahesh Kumar Sharma
Keyword(s):  
Shortest Path ◽  
Fuzzy Numbers ◽  
New Approach ◽  
Shortest Path Problems ◽  
Shortest Distance ◽  
Lexicographic Method ◽  
Different Types ◽  
The Cost ◽  
Pythagorean Fuzzy Numbers ◽  
Interval Valued

The aim of each company/industry is to provide a final product to customers at the minimum possible cost, as well as to protect the environment from degradation. Ensuring the shortest travel distance between involved locations plays an important role in achieving the company’s/industry’s objective as (i) the cost of a final product can be minimized by minimizing the total distance travelled (ii) finding the shortest distance between involved locations will require less fuel than the longest distance between involved locations. This will eventually result in lesser degradation of the environment. Hence, in the last few years, various algorithms have been proposed to solve different types of shortest path problems. A recently proposed algorithm for solving interval-valued Pythagorean fuzzy shortest path problems requires excessive computational efforts. Hence, to reduce the computational efforts, in this paper, firstly, an alternative lexicographic method is proposed for comparing interval-valued Pythagorean fuzzy numbers. Then, using the proposed lexicographic comparing method, a new approach (named as Mehar approach) is proposed to solve interval-valued Pythagorean fuzzy shortest path problems. Furthermore, the superiority of the proposed lexicographic comparing method, as well as the proposed Mehar approach, is discussed.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

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