2014
DOI: 10.1016/j.cie.2014.01.004
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Hybrid simulation-analytical modeling approaches for the reverse logistics network design of a third-party logistics provider

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Cited by 62 publications
(24 citation statements)
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“…Several authors have used for their contributions, mathematical integer linear programming (MILP) models. For instance, the design of a logistic network for returns in electronic commerce [14]; design of a model to examine the heterogeneous and non-uniform quality of the returned products in reverse logistics [15]; design of a reverse logistics network for the recycling of plastics [16]; design of a reverse logistics network for tires out of use in cities of Colombia [17]; design of an integrated network for optimization of product recovery [18]; design of a reverse logistics network based on a genetic algorithm that satisfies the demand for the logistic network [19]; design of a network for reverse logistics providers [20]; design of reverse logistics networks for waste of electrical and electronic equipment (WEEE) [21] [22]; design of a system to establish the batch size of hybrid products which must be remanufactured [23]; design of a flexible reverse logistics network with different supply lines [24]; design of a reverse logistics network for the recovery of vehicles at the end of their life cycle [25], among other authors, as [26] The design and use of mathematical tools to design reverse logistics systems for the recovery of products that have ended their life cycle, is acquiring great importance in the field of research. The commitment to these designs is to offer recycled products that contribute to sustainable development, fulfilling economic, social and ecological objectives.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Several authors have used for their contributions, mathematical integer linear programming (MILP) models. For instance, the design of a logistic network for returns in electronic commerce [14]; design of a model to examine the heterogeneous and non-uniform quality of the returned products in reverse logistics [15]; design of a reverse logistics network for the recycling of plastics [16]; design of a reverse logistics network for tires out of use in cities of Colombia [17]; design of an integrated network for optimization of product recovery [18]; design of a reverse logistics network based on a genetic algorithm that satisfies the demand for the logistic network [19]; design of a network for reverse logistics providers [20]; design of reverse logistics networks for waste of electrical and electronic equipment (WEEE) [21] [22]; design of a system to establish the batch size of hybrid products which must be remanufactured [23]; design of a flexible reverse logistics network with different supply lines [24]; design of a reverse logistics network for the recovery of vehicles at the end of their life cycle [25], among other authors, as [26] The design and use of mathematical tools to design reverse logistics systems for the recovery of products that have ended their life cycle, is acquiring great importance in the field of research. The commitment to these designs is to offer recycled products that contribute to sustainable development, fulfilling economic, social and ecological objectives.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Sahyouni et al (2007), Amin e Zhang Suyabatmaz et al (2014) sugerem outra direção para pesquisas futuras, que incluem a formulação de modelos multiobjetivos, ou seja, que incorpore outra abordagem ao problema. Na prática, é muito difícil que um problema real tenha um único objetivo, mas para facilitar ou possibilitar a análise, trabalha-se com um único objetivo.…”
Section: Análise Das Pesquisas Futuras Das Publicaçõesunclassified
“…Suyabatmaz et al [16] investigated a hybrid simulation model for reverse logistics network design from third-party provider's perspective. Alshamsi and Diabat [17] proposed a mixed integer programming for determining the facility location, product allocation and inventory level of a reverse logistics system.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The model is tested in a realworld case study and deep insight of the applicability is also given in this paper. Demirel et al [11] √ √ √ Alumur et al [12] √ √ √ Dat et al [13] √ √ √ Zarei et al [14] √ √ √ Mahapatra et al [15] √ √ √ Suyabatmaz et al [16] √ √ √ Alshamsi and Diabat [17] √ √ √ Liu [18] √ √ √ Demirel and Gokcen [5] √ √ √ Sasikumar et al [19] √ √ √ Kannan et al [20] √ √ √ Jonrinaldi and Zhang [21] √ √ √ Eskandarpour et al [22] √ √ √ Zaarour et al [23] √ √ √ Chiang et al [6] √ √ √ Lee et al [24] √ √ √ Lee et al [25] √ √ √ Pishvaee et al [26] √ √ √ Yu et al [27] √ √ √ Pati et al [28] √ √ √ El-Sayed et al [29] √ √ √ Salema et al [30] √ √ √ Roghanian and Pazhoheshfar [31] √ √ √ Ramezani et al [32] √ √ √ Cardoso et al [33] √ √ √ Hatefi and Jolai [34] √ √ √ Soleimani and Govindan [35] √ √ √ Niknejad and Petrovic [36] √ √ √ Keyvanshokooh et al [37] √ √ √ Wang and Yang [38] √ √ √ Kannan et al [30] √ √ √ Diabat et al [31] √ √ √ Bing et al [32] √ √ √ The formulation of uncertain input parameters related to reverse logistics network design is also well-developed. ElSayed et al [29] studied a multi-period mixed integer programming with stochastic input parameters for the integrated supply chain network design under risk.…”
Section: Literature Reviewmentioning
confidence: 99%