<!DOCTYPE html>
<html>
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
</head>
<body>
Call for Papers: <b>Special Issue on Multi-objective Programming</b><br>
in the <i>Journal of Multi-Criteria Decision Analysis (JMCDA)</i>,
Wiley<br>
<br>
Deadline: 30th September 2025<br>
Submission: <a class="moz-txt-link-freetext" href="https://submission.wiley.com/journal/MCDA">https://submission.wiley.com/journal/MCDA</a><br>
<br>
<br>
Guest Editors:<br>
Lavinia Amorosi, Sapienza Università di Roma, Italy<br>
Sophie N. Parragh, Johannes Kepler University Linz, Austria<br>
Michael Stiglmayr, University of Wuppertal, Germany<br>
<br>
<br>
Multi-objective optimization has become an indispensable tool in
many application areas. Since<br>
handling multiple objective functions adds a further layer of
difficulty to an optimization<br>
problem, compact problem formulations, theoretical analyses, and
efficient solution algorithms<br>
are of particular importance to solve multi-objective optimization
problems arising in real-<br>
world applications. This special issue will focus on recent advances
in multi-objective programming<br>
<br>
We invite high-quality submissions addressing theoretical and
algorithmic developments, and<br>
advancing the theory and methodology of multi-objective
optimization. Subject areas of this<br>
special issue include (but are not limited to):<br>
• scalarization methods and objective space algorithms<br>
• decision space algorithms<br>
• approximation and representation algorithms for multi-objective
optimization<br>
• multi-objective branch-and-bound and branch-and-cut algorithms<br>
• column generation and branch-and-price algorithms<br>
• multi-objective discrete and combinatorial problems<br>
• multi-objective continuous linear or non-linear problems<br>
• multi-objective mixed integer (non-)linear problems<br>
• stochastic and robust multi-objective optimization<br>
• complexity analysis for multi-objective optimization algorithms<br>
• parallelization of exact multi-objective optimization algorithms<br>
• multi-objective optimization with general dominance cones<br>
<br>
<br>
<br>
<br>
<br>
<pre class="moz-signature">--
-----------------------------------------
PD Dr. Michael Stiglmayr
University of Wuppertal
School of Mathematics and Natural Sciences
Institute of Mathematical Modelling, Analysis and Computational Mathematics
Optimization Group
Gaussstr. 20, 42119 Wuppertal
<a class="moz-txt-link-abbreviated" href="mailto:stiglmayr@math.uni-wuppertal.de">stiglmayr@math.uni-wuppertal.de</a>
<a class="moz-txt-link-freetext" href="http://uni-w.de/u9">http://uni-w.de/u9</a>
-----------------------------------------
</pre>
</body>
</html>