Development of a genetic algorithm for partner selection in business networks with or without intermediaries
Keywords:
business partner selection, genetic algorithm, metaheuristic, NP-HardAbstract
This research investigates the problem of business partner selection within a business network, where some organizations can invest in partnerships directly, while others must invest through intermediaries. The remaining organizations have the flexibility to invest either directly or via intermediaries to maximize overall benefits under a limited budget. This problem is highly complex and computationally challenging. The study begins by proving that the problem is NP-hard, highlighting its computational complexity and justifying the application of genetic algorithms as a suitable approach. Subsequently, an exact optimization algorithm is developed alongside the implementation of a genetic algorithm. The results indicate that the exact optimization algorithm is effective for small business networks with no more than 14 organizations. A performance comparison between the two methods reveals that the genetic algorithm produces solutions consistent with those obtained from the exact optimization algorithm on the same test instances while requiring significantly less computational time. Furthermore, this research conducts an experimental comparison between the genetic algorithm, particle swarm optimization (PSO), and the exact optimal solutions. The results indicate that the genetic algorithm consistently produces accurate solutions across all tested cases. However, the PSO approach exhibits minor inaccuracies, with errors not exceeding 15%. A statistical t-test (t = 1.497, p = 0.145 > 0.05) suggests no statistically significant difference between the two methods. Nonetheless, in terms of computational efficiency, the genetic algorithm significantly outperforms the PSO approach. To further demonstrate the suitability of the genetic algorithm for large-scale business networks, this study evaluates its performance on networks with 60 to 100 nodes. The results confirm that the genetic algorithm consistently provides accurate solutions within a reasonable computation time. Consequently, the genetic algorithm is deemed an appropriate approach for solving this problem in large and complex business networks.
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