Determining consignment inventory strategy for a vendor and multiple buyers using a hybrid metaheuristic algorithm

Molamohamadi, Zohreh; Sharifyazdi, Mehdi; Arshizadeh, Rahman; Jafari, Ali Akbar

doi:10.1080/21693277.2013.858377

Cited by 1 publication

(3 citation statements)

References 32 publications

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“…Only Govindan et al (2014) used GA to develop a hybrid algorithm with PSO for routing problems in the optimization of the sustainable supply chain. It is also observed that in three articles (Che, 2012;Barr on et al (2012); Taleizadeh et al, 2013), GA displayed better convergence and closer values to optimality as compared to PSO, while in five articles (Sadeghi et al, 2013;Subramanian et al, 2012;Kannan et al, 2009;Molamohamadi et al, 2013;Silva et al, 2006) PSO exhibited better results on similar grounds. Only in two articles (Chen et al, 2009a(Chen et al, , 2009bLi and Rong, 2009) did the LINGO solver gave better results than GA and PSO.…”

Section: Variants Of Si Algorithmsmentioning

confidence: 81%

“…On the other hand, basic PSO has been used in Yang and Lin (2010), Mahnam et al (2009) and Gong et al (2007). Besides these, PSO has also been used with GA in a greater extent (Taleizadeh et al, 2013;Sadeghi et al, 2014Sadeghi et al, , 2013Kannan et al, 2009;Molamohamadi et al, 2013;Ma and Wang, 2013;Sue-Ann et al, 2012). The use of fuzzy demands in inventory optimization models is used by several authors (Che, 2010;Mehdizadeh, 2009;Zhu et al, 2007).…”

Section: Si Applications In Scmmentioning

confidence: 99%

“…The proposed GA tool provided better optimal solutions than PSO and were closer to industry solutions. In the majority of articles, GA was used for validation purposes (Che, 2012;Barr on et al (2012); Taleizadeh et al, 2013;Sadeghi et al, 2013;Subramanian et al, 2012;Kannan et al, 2009;Chen et al, 2009aChen et al, , 2009bMolamohamadi et al, 2013;Ma and Wang, 2013). Only Govindan et al (2014) used GA to develop a hybrid algorithm with PSO for routing problems in the optimization of the sustainable supply chain.…”

Section: Variants Of Si Algorithmsmentioning

confidence: 99%

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Swarm intelligence approaches in supply chain management: potentials, challenges and future research directions

Soni

Jain

Chan

et al. 2019

SCM

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Purpose It is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM. Design/methodology/approach A structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms. Findings The paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers. Originality/value The paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

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