ECCO (European Chapter on Combinatorial Optimization) is a Working Group of EURO (Association of European Operational Research Societies) that provides an excellent opportunity to discuss recent and important issues in Combinatorial Optimization and its applications. CO series of biennial symposia, The Combinatorial Optimization Conferences, started in the UK in 1977 with meeting venues alternated between the UK and continental Europe.

The ECCO annual meetings aim to bring together researchers in the field of Combinatorial Optimization to present their work, share experiences, and discuss recent advances in theory and applications. The primary objectives are:

- exchanging results and experiences in solving real-world combinatorial optimization problems
- reporting on development and implementation of appropriate models and efficient solution methods for combinatorial optimization problems
- establishing networking contacts between individuals and research groups working on related topics
- promoting the work on combinatorial optimization (theory and applications) to the broader scientific community
- identifying challenging research problems for the field, as well as promising research outlets (both in theory and applications)
- promoting interactions with researchers in other related fields

Topics of interest:

- theory and applications of combinatorial optimization
- exact solution algorithms, approximation algorithms, heuristics, and meta-heuristics for combinatorial optimization problems
- integer programming, global optimization, stochastic integer programming, multi-objective programming, graph theory and network flows
- application areas include logistics and supply chain optimization, manufacturing, energy production and distribution, land consolidation, telecommunications, bioinformatics, finance, discrete tomography, discrete and hybrid dynamical systems, and other fields

- Abstract submission deadline: April 30, 2022
- Notification of acceptance: May 7, 2022
- Registration deadline: June 7, 2022

Andrea Lodi (Jacobs Technion-Cornell Institute at Cornell Tech and Technion)

Mathematical Programming Games: motivation, algorithms and challenges

Nikolaos Matsatsinis (Technical University of Crete)

Evolutionary and Swarm Intelligence Algorithms for Combinatorial Optimization Problems

In the last thirty years, a breakthrough was obtained with the introduction of metaheuristics, such as Simulated Annealing, Tabu Search, Genetic Algorithms and Differential Evolution. These algorithms have the possibility to find their way out of local optima. Also, the development and utilization of nature inspired approaches in advanced information systems became increasingly popular. The most popular nature inspired methods are those representing successful animal and micro-organism team behavior, i.e. birds flocks or fish schools inspired Particle Swarm Optimization, the imitation of biological immune systems led to the Artificial Immune Systems, the ants foraging behaviors gave rise to Ant Colony Optimization, the optimized performance of bees inspired Honey Bees Mating Optimization algorithm and Bumble Bees Mating Optimization algorithm, etc. We present a collection of different metaheuristic and nature inspired algorithms, we analyze and compare them based on their results when applied to some classic combinatorial optimization problems, like vehicle routing problem. Each metaheuristic and nature inspired method is presented not only in its classic form but also the most known variants of each method are presented and used in the comparisons. Thus, the methods presented are Genetic Algorithms and their variants, Differential Evolution and Particle Swarm Optimization and its variants. Genetic Algorithms (GAs) are randomized search techniques that simulate some of the processes observed in natural evolution and they offer a particularly attractive approach for many kinds of problems since they are generally quite effective in solving large-scale problems and for rapid global search of large, non-linear and poorly understood spaces. Differential Evolution (DE) is a stochastic, population-based algorithm. Although Differential Evolution is an evolutionary algorithm and, thus, it shares the basic characteristic of the evolutionary algorithms, it has a number of differences compared to them. Particle Swarm Optimization (PSO) is a population-based swarm intelligence algorithm that uses the physical movements of the individuals in the swarm. The advantages and disadvantages of these and other nature inspired methods are presented based on the results of the algorithms in classic combinatorial optimization problems.

Ulrich Pferschy (University of Graz)

Fairness and Conflicts: Allocating Items and Resources

In the first part we consider the allocation of a bounded resource among several agents. For each agent the obtained share of the resource serves as a capacity bound for a subset sum problem with the agent’s item set. If a central decision maker maximizes the total sum of item weights, this may result in a highly un- balanced allocation of the resource to the agents and hence be perceived as unfair. On the other hand, more balanced allocations may be far from the overall optimum. Therefore, we will discuss the resulting

In the second part items cannot be selected by the agents themselves but are allocated by the central decision maker from a common ground set. However, agents can express their preferences by setting a profit value for every item. The decider wants to assign each item to an agent such that the satisfaction, i.e. sum of profits, guaranteed for each of the agents gets as high as possible. This maximin problem is known as the so-called

To allow a more flexible approach of expressing preferences we also introduce a new satisfaction measure based on a directed preference graph of each agent. We measure the