An experimental analysis of evolutionary algorithms for the three-objective oil derivatives distribution problem

Scheduling oil derivatives distribution by multi-product pipelines is an important problem faced by the petroleum industry. Some researchers deal with it as a discrete problem where batches of products flow in a network. Minimizing delivery time is a usual objective handled by engineers when dealing...

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Bibliographic Details
Published in:2014 IEEE Congress on Evolutionary Computation (CEC) pp. 1982 - 1989
Main Authors: Souza, Thatiana C. N., Goldbarg, Elizabeth F. G., Goldbarg, Marco C.
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2014
Subjects:
Biological cells
distribution network
Electricity
Evolutionary computation
evolutionary multi-objective algorithm
Indexes
MOEA/D
NSGA-II
oil derivatives
Sociology
Statistics
Vectors
ISSN:1089-778X
Online Access:Get full text
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Summary:Scheduling oil derivatives distribution by multi-product pipelines is an important problem faced by the petroleum industry. Some researchers deal with it as a discrete problem where batches of products flow in a network. Minimizing delivery time is a usual objective handled by engineers when dealing with this problem. Nevertheless, other important costs may also be considered such as losses due to interfaces between fluids and electrical energy. Losses due to interfaces occur when different products sent consecutively contaminate each other. The price paid for electrical energy varies during the day, so it is important also to try to minimize this cost. In this paper, these three objectives, i.e. delivery time, interface losses and electricity cost, are minimized simultaneously. Two hybridizations of transgenetic algorithms with well-known multi-objective evolutionary algorithms are proposed. One is derived from the NSGA-II framework, named NSTA, and the other is derived from the MOEA/D framework, named MOTA/D. To analyze the performance of the proposed algorithms, they are compared with their classical counterparts and applied to thirty random instances. It is also the first time MOEA/D is applied to the investigated problem. Statistical tests indicate that the MOTA/D generated better approximation sets than the other algorithms. Therefore, the MOTA/D encourages further researches in the hybridization of transgenetic algorithms and evolutionary multi-objective frameworks, specifically those based on decomposition.
ISSN:1089-778X
DOI:10.1109/CEC.2014.6900598