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

Fouad Ben Abdelaziz (), Houda Alaya () and Prasanta Kumar Dey ()
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Fouad Ben Abdelaziz: NEOMA Business School
Houda Alaya: IHE Paris
Prasanta Kumar Dey: Aston University

Annals of Operations Research, 2020, vol. 293, issue 2, No 7, 557-586

Abstract: 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.

Keywords: Neural network; Particle swarm optimization; Multi-objective programming; Sustainability practices and performance (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-018-2974-0

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