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Appl. Sci. 2019, 9(3), 588; https://doi.org/10.3390/app9030588

An Artificial Bee Colony Algorithm Based on a Multi-Objective Framework for Supplier Integration

1
Department of Industrial Engineering, University of Engineering & Technology, Taxila 47080, Pakistan
2
Department of Industrial Engineering, University of Engineering & Technology, Peshawar P.O.B. 814, KPK, Pakistan
3
Department of Electrical Engineering, University of Engineering & Technology, Peshawar P.O.B. 814, KPK, Pakistan
4
School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea
*
Author to whom correspondence should be addressed.
Received: 6 December 2018 / Revised: 29 January 2019 / Accepted: 4 February 2019 / Published: 11 February 2019
(This article belongs to the Section Computing and Artificial Intelligence)
Full-Text   |   PDF [2209 KB, uploaded 11 February 2019]   |  

Abstract

Modern day industries strive to obtain long-term supplier integrations (SI) with potentially stronger supplier groups, to achieve fast and reliable production. This paper studies the process of selecting vendors, while simultaneously considering the aspects of random factors, multiple criteria, and efficiently reaching optimal solutions to improve the SI. A framework was developed that consists of three layers of expert opinions, supplier requirements, and multi-objective bee colony optimization. The model factors affecting the SI decision were explored from the comprehensive relevant literature, and these factors were shortlisted and prioritized. Routines for the modeled framework were coded by using the proposed algorithms which were implemented for a real-world problem from a manufacturing small and medium enterprise (SME) in Pakistan. Optimization of SI was carried out on an archived artificial bee colony (AABC). Its effectiveness was also evaluated by comparison with simple artificial bee colony (ABC) and particle swarm algorithms. The methodologically calculated results, obtained from simulation of a mathematically reinforced optimization framework, are highly beneficial for the industry, as well as local and international suppliers. A detailed and in-depth evaluation of suppliers was provided by the sensitivity analysis, which presented a more rigorous authentication and elaboration of the results. The presented framework is the first of its kind for the SMEs of Pakistan and can be applied with little modification to other industries. View Full-Text
Keywords: multi-objective optimization; artificial bee colony algorithm; supplier integration multi-objective optimization; artificial bee colony algorithm; supplier integration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Farooq, M.U.; Salman, Q.; Arshad, M.; Khan, I.; Akhtar, R.; Kim, S. An Artificial Bee Colony Algorithm Based on a Multi-Objective Framework for Supplier Integration. Appl. Sci. 2019, 9, 588.

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