eprintid: 14200
rev_number: 6
eprint_status: archive
userid: 5
dir: disk0/00/01/42/00
datestamp: 2016-12-22 15:22:02
lastmod: 2021-08-27 17:28:19
status_changed: 2016-12-22 15:22:02
type: article
metadata_visibility: show
item_issues_count: 2
creators_name: Sun, M.
creators_name: Stam, A.
creators_name: Steuer, R.E.
creators_id: 1602
title: Interactive multiple objective programming using Tchebycheff programs and artificial neural networks
ispublished: pub
divisions: prog_ins
abstract: A new interactive multiple objective programming procedure is developed that combines the strengths of the interactive weighted Tchebycheff procedure (Steuer and Choo. Mathematical Programming 1983;26(1):326–44.) and the interactive FFANN procedure (Sun, Stam and Steuer. Management Science 1996;42(6):835–49.). In this new procedure, nondominated solutions are generated by solving augmented weighted Tchebycheff programs (Steuer. Multiple criteria optimization: theory, computation and application. New York: Wiley, 1986.). The decision maker indicates preference information by assigning “values” to or by making pairwise comparisons among these solutions. The revealed preference information is then used to train a feed-forward artificial neural network. The trained feed-forward artificial neural network is used to screen new solutions for presentation to the decision maker on the next iteration. The computational experiments, comparing the current procedure with the interactive weighted Tchebycheff procedure and the interactive FFANN procedure, produced encouraging results.
date: 2000
date_type: published
publisher: Elsevier
id_number: 10.1016/S0305-0548(99)00108-2
creators_browse_id: 1588
full_text_status: none
publication: Computers & Operations Research
volume: 27
number: 7-8
pagerange: 601-620
refereed: TRUE
issn: 03050548
coversheets_dirty: FALSE
fp7_type: info:eu-repo/semantics/article
citation:   Sun, M., Stam, A. <https://pure.iiasa.ac.at/view/iiasa/1588.html>, & Steuer, R.E.  (2000).  Interactive multiple objective programming using Tchebycheff programs and artificial neural networks.   Computers & Operations Research 27 (7-8) 601-620. 10.1016/S0305-0548(99)00108-2 <https://doi.org/10.1016/S0305-0548%2899%2900108-2>.