* abbrev.bib (palmac-pub): New.

* articles (MieEskRui2010nautilus): New. * biblio.bib (FraHam2016bor): New. * crossref.bib (BOR2016): New.
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diff --git a/abbrev.bib b/abbrev.bib
@@ -146,6 +146,7 @@ @string{ob
 @string{openreview = "OpenReview.net"}
 @string{oup	= "Oxford University Press"}
 @string{oup-n	= oup # ", " # add-ny}
+@string{palmac-pub = "Palgrave Macmillan"}
 @string{pergamon	= "Pergamon Press, " # add-ny}
 @string{ph	= "Prentice Hall, Englewood Cliffs, NJ"}
 @string{pm = "Palgrave Macmillan Ltd., Basingstoke, UK"}

diff --git a/articles.bib b/articles.bib
@@ -12886,6 +12886,50 @@ @Article{Mie2014or
   doi =          "10.1007/s00291-012-0297-0"
 }
 
+@Article{MieEskRui2010nautilus,
+  author       = Miettinen #and# {Eskelinen, Petri} #and# Ruiz_Francisco #and#
+                  Luque_M,
+  title        = {{NAUTILUS} method: {An} interactive technique in
+                  multiobjective optimization based on the nadir point},
+  journal      = ejor,
+  year         = 2010,
+  volume       = 206,
+  number       = 2,
+  pages        = {426--434},
+  month        = oct,
+  issn         = {0377-2217},
+  shorttitle   = {{NAUTILUS} method},
+  doi          = {10.1016/j.ejor.2010.02.041},
+  abstract     = {Most interactive methods developed for solving multiobjective
+                  optimization problems sequentially generate Pareto optimal or
+                  nondominated vectors and the decision maker must always allow
+                  impairment in at least one objective function to get a new
+                  solution. The NAUTILUS method proposed is based on the
+                  assumptions that past experiences affect decision makers'
+                  hopes and that people do not react symmetrically to gains and
+                  losses. Therefore, some decision makers may prefer to start
+                  from the worst possible objective values and to improve every
+                  objective step by step according to their preferences. In
+                  NAUTILUS, starting from the nadir point, a solution is
+                  obtained at each iteration which dominates the previous
+                  one. Although only the last solution will be Pareto optimal,
+                  the decision maker never looses sight of the Pareto optimal
+                  set, and the search is oriented so that (s)he progressively
+                  focusses on the preferred part of the Pareto optimal
+                  set. Each new solution is obtained by minimizing an
+                  achievement scalarizing function including preferences about
+                  desired improvements in objective function values. NAUTILUS
+                  is specially suitable for avoiding undesired anchoring
+                  effects, for example in negotiation support problems, or just
+                  as a means of finding an initial Pareto optimal solution for
+                  any interactive procedure. An illustrative example
+                  demonstrates how this new method iterates.},
+  language     = {en},
+  keywords     = {Reference point methods, Interactive methods, Multiple
+                  objective programming, Pareto optimality, Preference
+                  information}
+}
+
 @Article{MieMusSte2014nimbus,
   title =        {Interactive multiobjective optimization with {NIMBUS} for
                   decision making under uncertainty},
@@ -15543,13 +15587,19 @@ @Article{RuiMar06
 }
 
 @Article{RuiSabLuq2015wasfga,
-  author =       Ruiz_AB #and# Saborido_R #and# Luque_M,
-  title =        {A preference-based evolutionary algorithm for multiobjective
+  author       = Ruiz_AB #and# Saborido_R #and# Luque_M,
+  title        = {A preference-based evolutionary algorithm for multiobjective
                   optimization: the weighting achievement scalarizing function
                   genetic algorithm},
-  volume =       62,
-  doi =          {10.1007/s10898-014-0214-y},
-  abstract =     {When solving multiobjective optimization problems,
+  journal      = jgo,
+  year         = 2015,
+  volume       = 62,
+  number       = 1,
+  pages        = {101--129},
+  month        = may,
+  annote       = {Proposed WASF-GA},
+  doi          = {10.1007/s10898-014-0214-y},
+  abstract     = {When solving multiobjective optimization problems,
                   preference-based evolutionary multiobjective optimization
                   (EMO) algorithms introduce preference information into an
                   evolutionary algorithm in order to focus the search for
@@ -15576,13 +15626,7 @@ @Article{RuiSabLuq2015wasfga
                   other preference-based EMO algorithms. Regarding a metric
                   based on the hypervolume, we can say that WASF-GA has
                   outperformed the other algorithms considered in most of the
-                  problems.},
-  number =       1,
-  journal =      jgo,
-  month =        may,
-  year =         2015,
-  pages =        {101--129},
-  annote =       "Proposed WASF-GA"
+                  problems.}
 }
 
 @Article{RuiStu04:ejor,

diff --git a/biblio.bib b/biblio.bib
@@ -3294,13 +3294,13 @@ @InCollection{DebSin2009emo
 }
 
 @InCollection{DebSun2006gecco,
-  author =       Deb #and# {Sundar, J.},
-  title =        {Reference point based multi-objective optimization using
+  author       = Deb #and# {Sundar, J.},
+  title        = {Reference point based multi-objective optimization using
                   evolutionary algorithms},
-  crossref =     "GECCO2006",
-  pages =        {635--642},
-  doi =          "10.1145/1143997.1144112",
-  annote = "Proposed R-NSGA-II"
+  pages        = {635--642},
+  annote       = {Proposed R-NSGA-II},
+  crossref     = {GECCO2006},
+  doi          = {10.1145/1143997.1144112}
 }
 
 @InProceedings{DebTewDixDut2007finding,
@@ -4654,6 +4654,15 @@ @InProceedings{FraGyoNad2020
   crossref =     "BNAIC2020"
 }
 
+@InCollection{FraHam2016bor,
+  author       = {Franco, L Alberto} #and# Hamalainen,
+  title        = {Engaging with Behavioral Operational Research: On Methods,
+                  Actors and Praxis},
+  pages        = {3--25},
+  crossref     = {BOR2016},
+  doi          = {10.1057/978-1-137-53551-1_1}
+}
+
 @Book{FraLeiRui2014,
   title =        {Manufacturing Scheduling Systems: An Integrated View on
                   Models, Methods, and Tools},

diff --git a/crossref.bib b/crossref.bib
@@ -578,6 +578,16 @@ @Proceedings{BNAIC2020
   url =          {https://bnaic.liacs.leidenuniv.nl/wordpress/wp-content/uploads/bnaic2020proceedings.pdf}
 }
 
+@Book{BOR2016,
+  booktitle    = {Behavioral Operational Research},
+  editor       = {Kunc, M. and Malpass, J. and White, L.},
+  title        = {Behavioral Operational Research Theory, Methodology and
+                  Practice},
+  year         = 2016,
+  publisher    = palmac-pub,
+  address      = add-london
+}
+
 @Book{BarChiPaqPre2010emaoa,
   title =        {Experimental Methods for the Analysis of
                   Optimization Algorithms},
@@ -4703,7 +4713,6 @@ @Book{evoworkshops2004
                   #and#{E. Marchiori and R. Rothlauf and G. D. Smith and
                   G. Squillero}
 }
-
 @Proceedings{wae1998,
   title        = {Algorithm Engineering, 2nd International Workshop, {WAE}'92},
   year         = 1998,