Multiple Objective Path Optimization for Time Dependent Objective Functions

2002 ◽  
pp. 127-142 ◽  
Author(s):  
Michael M. Kostreva ◽  
Laura Lancaster
Keyword(s):  
Path Optimization ◽  
Time Dependent ◽  
Multiple Objective ◽  
Objective Functions

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

scholarly journals A Fissile Ripple Spreading Algorithm to Solve Time-Dependent Vehicle Routing Problem via Coevolutionary Path Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Wen Xu ◽  
JiaJun Li
Keyword(s):  
Vehicle Routing ◽  
Shortest Path ◽  
Traffic Congestion ◽  
Path Optimization ◽  
Time Dependent ◽  
Optimization Approach ◽  
Routing Problem ◽  
Shortest Path Problems ◽  
Travelling Time ◽  
Logistics Delivery

The time-dependent vehicle routing problems have lately received great attention for logistics companies due to their crucial roles in reducing the time and economic costs, as well as fuel consumption and carbon emissions. However, the dynamic routing environment and traffic congestions have made it challenging to make the actual travelling trajectory optimal during the delivery process. To overcome this challenge, this study proposed an unconventional path optimization approach, fissile ripple spreading algorithm (FRSA), which is based on the advanced structure of coevolutionary path optimization (CEPO). The objective of the proposed model is to minimize the travelling time and path length of the vehicle, which are the popular indicators in path optimization. Some significant factors usually ignored in other research are considered in this study, such as congestion evolution, routing environment dynamics, signal control, and the complicated correlation between delivery sequence and the shortest path. The effectiveness of the proposed approach was demonstrated well in two sets of simulated experiments. The results prove that the proposed FRSA can scientifically find out the optimal delivery trajectory in a single run via global research, effectively avoid traffic congestion, and decrease the total delivery costs. This finding paves a new way to explore a promising methodology for addressing the delivery sequence and the shortest path problems at the same time. This study can provide theoretical support for the practical application in logistics delivery.

scholarly journals Optimization of energy performance with renewable energy project sizing using multiple objective functions

2019 ◽  
Vol 5 ◽  
pp. 898-908 ◽  
Author(s):  
E.O.B. Ogedengbe ◽  
P.A. Aderoju ◽  
D.C. Nkwaze ◽  
J.B. Aruwajoye ◽  
M.B. Shitta
Keyword(s):  
Renewable Energy ◽  
Energy Performance ◽  
Multiple Objective ◽  
Objective Functions

ENHANCED INTERACTIVE SATISFYING OPTIMIZATION APPROACH TO MULTIPLE OBJECTIVE OPTIMIZATION WITH PREEMPTIVE PRIORITIES

2006 ◽  
Vol 05 (01) ◽  
pp. 47-63 ◽  
Author(s):  
CHAOFANG HU ◽  
SHAOYUAN LI
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Previous Method ◽  
Optimization Models ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Optimization Approach ◽  
Fuzzy Relations ◽  
Objective Functions ◽  
Preemptive Priority

This paper proposes an enhanced interactive satisfying optimization method based on goal programming for the multiple objective optimization problem with preemptive priorities. Based on the previous method, the approach presented makes the higher priority achieve the higher satisfying degree. For three fuzzy relations of the objective functions, the corresponding optimization models are proposed. Not only can satisfying results for all the objectives be acquired, but the preemptive priority requirement can also be simultaneously actualized. The balance between optimization and priorities is realized. We demonstrate the power of this proposed method by illustrative examples.

scholarly journals A self-reconfiguring architecture supporting multiple objective functions in genetic algorithms

2009 ◽  
Author(s):  
Charalampos Effraimidis ◽  
Kyprianos Papadimitriou ◽  
Apostolos Dollas ◽  
Ioannis Papaefstathiou
Keyword(s):  
Genetic Algorithms ◽  
Multiple Objective ◽  
Objective Functions
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