Problems of Combinatorial Optimization distinguish themselves by their well-structured problem description as well as by their huge number of possible action alternatives. Especially in the area of production and operational logistics these problems frequently occur. Their advantage lies in their subjective understanding of action alternatives and their objective functions. The use of classical...

Assortment optimization refers to the problem of designing a slate products offer potential customers, such as stocking shelves in convenience store. The price each product is fixed advance, and probabilistic choice function describes which customer will choose from any given subset. We introduce combinatorial assortment problem, where may select bundle products. consider model consumer selects...

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyz...

Evolutionary Computation (EC) is inspired from by evolution that explores the solution space by gene inheritance, mutation, and selection of the fittest candidate solutions. Since their inception in the 1960s, Evolutionary Computation has been used in various hard and complex optimization problems in search and optimization such as: combinatorial optimization, functions optimization with and wi...

The optimization of discrete problems is largely encountered in engineering and information domains. Solving these problems with continuous-variables approach then convert the continuous variables to discrete ones does not guarantee the optimal global solution. Evolutionary Algorithms (EAs) have been applied successfully in combinatorial discrete optimization. Here, the mathematical basics of r...

The paper presents a new genetic local search algorithm for multi-objective combinatorial optimization. The goal of the algorithm is to generate in a short time a set of approximately efficient solutions that will allow the decision maker to choose a good compromise solution. In each iteration, the algorithm draws at random a utility function and constructs a temporary population composed of a ...