A Hybrid Genetic Algorithm for Constrained Combinatorial Problems: An Application to Promotion Planning Problems

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Paulo A. Pereira (Author) Other; Fernando A.C.C. Fontes (Author) FEP; Dalila B.M.M. Fontes (Author) FEP

Conference proceedings International

Title: OPERATIONS RESEARCH PROCEEDINGS 2010

Pages: 301-306

Annual International Conference of the German-Operations-Research-Society (GOR)

Neubiberg, GERMANY, SEP 01-03, 2010

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Scientific classification

FOS: Social sciences > Economics and Business

CORDIS: Technological sciences > Engineering > Electrical engineering

Other information

Authenticus ID: P-002-VX7

DOI: 10.1007/978-3-642-20009-0_48

Abstract (EN): We propose a Hybrid Genetic Algorithm (HGA) for a combinatorial optimization problem, motivated by, and a simplification of, a TV Self-promotion Assignment Problem. Given the weekly self-promotion space (a set of TV breaks with known duration) and a set of products to promote, the problem consists of assigning the products to the breaks in the "best" possible way. The objective is to maximize contacts in the target audience for each product, whist satisfying all constraints. The HGA developed incorporates a greedy heuristic to initialize part of the population and uses a repair routine to guarantee feasibility of each member of the population. The HGA works on a simplified version of the problem that, nevertheless, maintains its essential features. The proposed simplified problem is a binary programming problem that has similarities with other known combinatorial optimization problems, such as the assignment problem or the multiple knapsack problem, but has some distinctive features that characterize it as a new problem. Although we are mainly interested in solving problems of large dimension (of about 200 breaks and 50 spots), the quality of the solution has been tested on smaller dimension problems for which we are able to find an exact global minimum using a branch-and-bound algorithm. For these smaller dimension problems we have obtained solutions, on average, within 1% of the optimal solution value.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6