• DOI:10.1080/00207543.2016.1234084
• Corpus ID: 114390939

Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm

@article{Mladineo2017SolvingPS,
  title={Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm},
  author={Marko Mladineo and Ivica Veza and Nikola Gjeldum},
  journal={International Journal of Production Research},
  year={2017},
  volume={55},
  pages={2506 - 2521}
}

• M. Mladineo, I. Veza, N. Gjeldum
• Published 3 May 2017
• Business
• International Journal of Production Research

The idea of non-hierarchical production networks consisting of autonomous enterprises has been present in scientific community for more than 20 years. Although some global corporations are using their own production networks across continents, they are not similar to the original idea of non-hierarchical production networks in many aspects. It seems that this idea waited for production systems to acquire proper information and communications technology (ICT) or new industrial platforms, like Industry 4.0. The result is a new type of production network called Cyber-Physical Production Network (CPPN). The CPPN is, from ICT point of view, ready to act as non-hierarchical production networks consisting of autonomous production systems with many automated processes. One of the most important processes of the CPPN is a selection of optimal partners (enterprises) to be part of a new virtual enterprise, created inside production network. An optimisation problem emerges in this process, and it is called Partner Selection Problem (PSP). It is non-polynomial-hard combinatorial problem. Since metaheuristic algorithms are well-proven in solving that kind of problem, a specially designed metaheuristic algorithm derived from ant colony optimisation and named the HUMANT (HUManoid ANT) algorithm is used in this paper. It is multi-objective optimisation algorithm that successfully solves different instances of PSP with two, three, four or more objectives.