Reverse logistics models for the collection of plastic waste: A literature review. Issue 9 (September 2021)

• Record Type: Journal Article
• Authors: Valenzuela, Juan; Alfaro, Miguel; Fuertes, Guillermo; Vargas, Manuel; Sáez-Navarrete, César
• Is Part Of: Waste management & research. Volume 39:Issue 9(2021)
• Page Start: 1116
• Page End: 1134
• Publication Date: 2021-09
• Subjects: Circular economy -- reverse supply chains -- solid waste -- waste management -- urban solid waste; Factory and trade waste -- Periodicals; Refuse and refuse disposal -- Periodicals; Municipal engineering -- Periodicals; Déchets industriels -- Périodiques; Déchets -- Élimination -- Périodiques; Génie urbain -- Périodiques; 628
• DOI: 10.1177/0734242X211003948
• Languages: English
• ISSNs: 0734-242X

Abstract

To support the understanding of recycling models applied to plastics, the main objective of this work is to offer a literature review of the different reverse logistics (RL) models for collecting plastic waste (PW). The methodology used for processing the scientific literature was content analysis, using the google scholar search engine. The main keywords used were RL and PW. This article is divided into two parts: the first part discusses the development of circular economy models and RL networks and raises the conceptual framework of the research, and the second part presents mathematical models and exploratory studies, proposed as a solution for RL problems of PW. Articles published between years 2014 and 2019 were reviewed. In total, 102 references were used, 70 of them are part of the literature review. According to our findings, we can state that the most widely used solution method for mathematical modeling is mixed-integer linear programming, and for exploratory studies, it was evaluations. About 93% of studies evaluated raw materials related to PW; only 13% of studies had models with stochastic processes; and 88% of the investigations used continuous variables, being the multiobjective functions one of the most used to provide solutions to RL problems. Regarding the mathematical models, 49% were evaluations, 9% corresponded to multicriteria analysis, 29% to linear and nonlinear programming, and 4% to another type of evaluation or model.