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A multi-objective particle swarm optimization algorithm for business sustainability analysis of small and medium sized enterprises

Author

Listed:
  • Fouad Ben Abdelaziz

    (NEOMA Business School)

  • Houda Alaya

    (IHE Paris
    Tunis Business School)

  • Prasanta Kumar Dey

    (Aston University)

Abstract

Sustainability is the major issue of small and medium sized enterprises (SMEs) all across the globe. Although SMEs contribute to GDP of any country their negative contribution to environment is also significant. Prior studies on SMEs’ sustainability mainly classified into three categories—the correlation between environmental and social practices with economic performance, sustainable supply chain performance measurement, and empirical research on sustainability practices. There is no study that objectively derives the sustainable structure of SMEs through optimal combination of sustainability practices (inputs) and performance (outputs). Therefore, the main objective of this paper is to generate optimal structure of sustainable SMEs by combining neural network and particle swarm algorithm while considering Multi-Objective framework. The study uses data from 54 SMEs of Normandy in France and 30 SMEs of Midlands in the UK. The data was gathered through questionnaire survey. As we do not have the explicit expression of our objective functions, we train a neural network on our databases in order to enable the generation of value of the different objectives for any profile. We design and run a multi-objective version of particle swarm optimization (MPSO) to generate efficient companies’ structures. The weighted sum method is then used for different weights. The comparison of observed data and the results of the PSO analysis facilitates to derive improvement measures for each individual SME.

Suggested Citation

  • Fouad Ben Abdelaziz & Houda Alaya & Prasanta Kumar Dey, 2020. "A multi-objective particle swarm optimization algorithm for business sustainability analysis of small and medium sized enterprises," Annals of Operations Research, Springer, vol. 293(2), pages 557-586, October.
    • Handle: RePEc:spr:annopr:v:293:y:2020:i:2:d:10.1007_s10479-018-2974-0
    DOI: 10.1007/s10479-018-2974-0
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    References listed on IDEAS

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    3. Luciano Ferreira Cruz & Flavia Bernardo Pinto & Lucas Camilotti & Angelo Marcio Oliveira Santanna & Roberto Zanetti Freire & Leandro Santos Coelho, 2022. "Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process," Annals of Operations Research, Springer, vol. 319(2), pages 1565-1587, December.

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