Crude oil refinery scheduling: addressing a real-world multiobjective problem through genetic programming and dominance-based approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Cristiane S. Pereira and Douglas M. Dias and 
                 Marley M. B. R. Vellasco and Francisco Henrique F. Viana and 
                 Luis Marti",
  title =        "Crude oil refinery scheduling: addressing a real-world
                 multiobjective problem through genetic programming and
                 dominance-based approaches",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  year =         "2018",
  editor =       "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and 
                 Shigeru Obayashi and Bogdan Filipic and 
                 Thomas Bartz-Beielstein and Grant Dick and 
                 Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and 
                 Pablo Valledor Pellicer and Manuel Lopez-Ibanez and 
                 Daniel R. Tauritz and Pietro S. Oliveto and 
                 Thomas Weise and Borys Wrobel and Ales Zamuda and 
                 Anne Auger and Julien Bect and Dimo Brockhoff and 
                 Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and 
                 Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and 
                 Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and 
                 Richard Duro and Joshua Auerbach and 
                 Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and 
                 Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and 
                 Francisco {Chavez de la O} and Ozgur Akman and 
                 Khulood Alyahya and Juergen Branke and Kevin Doherty and 
                 Jonathan Fieldsend and Giuseppe Carlo Marano and 
                 Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and 
                 Stefan Wagner and Michael Affenzeller and 
                 Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and 
                 Riyad Alshammari and Tokunbo Makanju and 
                 Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and 
                 John R. Woodward and Shin Yoo and John McCall and 
                 Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and 
                 Masaya Nakata and Anthony Stein and 
                 Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and 
                 Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and William {La Cava} and 
                 Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and 
                 Ivanoe {De Falco} and Antonio {Della Cioppa} and 
                 Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and 
                 Giovanni Iacca and Ahmed Hallawa and Anil Yaman and 
                 Alma Rahat and Handing Wang and Yaochu Jin and 
                 David Walker and Richard Everson and Akira Oyama and 
                 Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and 
                 Pramudita Satria Palar",
  isbn13 =       "978-1-4503-5764-7",
  pages =        "1821--1828",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205651.3208291",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "his study presents the crude oil scheduling problem
                 with four objectives divided in two different levels of
                 importance. It comes from a real refinery where the
                 scheduling starts on the offloading of ships,
                 encompasses terminal and refinery tanks, a crude
                 pipeline, and finishes on the output streams of the
                 crude distillation units. We propose a new approach for
                 the Quantum-Inspired Grammar-based Linear Genetic
                 Programming (QIGLGP) evolutionary algorithm to handle
                 the multiple objectives of the problem using the
                 non-dominance concept. The modifications are
                 concentrated on the population updating and sorting
                 steps of QIGLGP. We tackle difference of importance
                 among the objectives using the principle of violation
                 of constraints. The problem constraints define if an
                 instruction will or not be executed but do not affect
                 the violation equation of the objectives. The
                 individuals which have objective values under a
                 pre-defined upper limit are better ranked. Results from
                 five scenarios showed that the proposed model was able
                 to significantly increase the percentage of runs with
                 acceptable solutions, achieving success ratio of
                 100percent in 3 cases and over 70percent in 2 other
                 ones. They also show that the Pareto front of these
                 accepted runs contains a set of non-dominated solutions
                 that could be analysed by the decision maker for his a
                 posteriori decision.",
  notes =        "Also known as \cite{3208291} GECCO-2018 A
                 Recombination of the 27th International Conference on
                 Genetic Algorithms (ICGA-2018) and the 23rd Annual
                 Genetic Programming Conference (GP-2018)",

Genetic Programming entries for Cristiane Salgado Pereira Douglas Mota Dias Marley Maria Bernardes Rebuzzi Vellasco Francisco Henrique F Viana Luis Marti