scholarly journals An exact algorithm for kinodynamic planning in the plane

1990 ◽  
Author(s):  
John Canny ◽  
Ashutosh Rege ◽  
John Reif
Keyword(s):  
Exact Algorithm

Exact algorithm for generating optimal homogenous strip T-shape layouts

2013 ◽  
Vol 32 (9) ◽  
pp. 2634-2637
Author(s):  
Jun JI ◽  
Yi-ping LU ◽  
Jian-zhong ZHA ◽  
Yao-dong CUI
Keyword(s):  
Exact Algorithm

scholarly journals An exact algorithm for subgraph homeomorphism

2009 ◽  
Vol 7 (4) ◽  
pp. 464-468 ◽  
Author(s):  
Andrzej Lingas ◽  
Martin Wahlen
Keyword(s):  
Exact Algorithm

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.

An improved heuristic and an exact algorithm for the 2D strip and bin packing problem

2010 ◽  
Vol 10 (1/2/3) ◽  
pp. 217 ◽  
Author(s):  
Abdelghani Bekrar ◽  
Imed Kacem ◽  
Chengbin Chu ◽  
Cherif Sadfi
Keyword(s):  
Bin Packing ◽  
Exact Algorithm ◽  
Packing Problem ◽  
Bin Packing Problem

An exact algorithm for scheduling identical coupled tasks

2004 ◽  
Vol 59 (2) ◽  
pp. 193-203 ◽  
Author(s):  
Dino Ahr ◽  
J�zsef B�k�si ◽  
G�bor Galambos ◽  
Marcus Oswald ◽  
Gerhard Reinelt
Keyword(s):  
Exact Algorithm ◽  
Coupled Tasks

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

Towards Intelligent Road Traffic Management over Weighted Large Graphs Hybrid Meta-heuristic-Based Approach

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Traffic Management ◽  
Large Scale ◽  
Road Traffic ◽  
Optimization Technique ◽  
Shortest Paths ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Exact Algorithm ◽  
On The Road

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

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