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

Content Provider	IEEE Xplore Digital Library
Author	Souza, T.C.N. Goldbarg, E.F.G. Goldbarg, M.C.
Copyright Year	2014
Description	Author affiliation: Fed. Univ. of Rio Grande do Norte, Natal, Brazil (Souza, T.C.N.) \|\| Dept. of Inf. & Appl. Math., Fed. Univ. of Rio Grande do Norte, Natal, Brazil (Goldbarg, E.F.G.; Goldbarg, M.C.)
Abstract	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.
Starting Page	1982
Ending Page	1989
File Size	1413976
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900598
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological cells Sociology Statistics Vectors Evolutionary computation Electricity Indexes NSGA-II distribution network oil derivatives evolutionary multi-objective algorithm MOEA/D
Content Type	Text
Resource Type	Article

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