Most of existing neural methods for multi-objective combinatorial optimization (MOCO)
problems solely rely on decomposition, which often leads to repetitive solutions for the …

In the last decade, decision diagrams (DDs) have been the basis for a large array of novel
approaches for modeling and solving optimization problems. Many techniques now use DDs …

Many real-world problems can be naturally formulated as discrete multi-objective
optimisation (DMOO) problems. We have proposed a novel Physarum-inspired competition …

Abstract Bin Packing with Minimum Color Fragmentation (BPMCF) is an extension of the Bin
Packing Problem in which each item has a size and a color and the goal is to minimize the …

Approaches based on Binary decision diagrams (BDDs) have recently achieved state-of-the-
art results for some multiobjective integer programming problems. The variable ordering …

In multicriteria decision-making, a user seeks a set of non-dominated solutions to a
(constrained) multiobjective optimization problem, the so-called Pareto frontier. In this work …

We present a new approach to generate representations with a coverage error quality
guarantee for multiobjective discrete optimization problems with any number of objectives …

Adaptive robust optimization problems have received significant attention in recent years,
but remain notoriously difficult to solve when recourse decisions are discrete in nature. In …

The detection of feed behavior at pig farms is essential in monitoring the welfare and health
of pigs. Addressing the low automation level of feeding behavior detection in group-housed …

Decision diagrams have been extensively and successfully used for solving challenging
discrete optimization problems during the last decade. This article provides a brief overview …