Assessing performance and uncertainty in developing carpet reverse logistics systems

International Advisory Board

“…In summary, both papers addressed a single-item flow with multi-period. Biehl et al [64] investigated closed-loop and two-level network structure while Kara et al [65] considered open-loop and multi-level network structure. Regarding the model results, both simulation models were able to provide several crucial managerial recommendations for the improvement of reverse logistics network.…”

“…The objective of the simulation models was to investigate which possible design variables are important for a reverse logistics network design. For example, Biehl et al [64] took potential design variables such as the number of collection centres, collection rates, type and set up of information technology for forecasting and control systems, recycling rates and return rates into account in order to assess how the US carpet industry would able to meet a 40% diversion from landfills by 2012. In addition, Kara et al [65] considered design variables including the number and type of participants in the system, number and location of the disassembly centre, collection points, return rate and characteristics of the material flow and product characteristics in order to design a cost effective reverse logistics networks for taking back end-of-life white goods.…”

Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach

“…The first class, bulk recycling networks, concerns material recovery from rather low-value products. Barros et al, 1998;Biehl et al, 2007;Listes and Dekker, 2005;Lebreton and Tuma, 2006 are examples of this class. Barros et al (1998) proposed a two-level location model for the sand problem optimization using heuristic procedures.…”

“…Barros et al (1998) proposed a two-level location model for the sand problem optimization using heuristic procedures. Biehl et al (2007) simulated a carpet RL supply chain and used a designed experiment to analyze the impact of the system design factors on the operational performance of the RL system. Listes (2005) presented a stochastic approach to a case study for product recovery network design.…”

Design of Demand Driven Return Supply Chain for High-Tech Products

“…Even though some common denominators can be found, the uncertainties of the design environment vary from one case to another case. Demand (or quantity) uncertainty Zhang et al (2010), Xiao et al (2010), Qin and Ji (2010), Amaro and Barboso-Povoa (2009), Dong (2009), Chouinard et al (2008), El-Sayed et al (2010), Salema et al (2007), Biehl et al (2007), quality uncertainty (Qin & Ji, 2010 ;Chouinard et al, 2008) price uncertainty (Amaro & Barboso-Povoa, 2009), lead times or timing uncertainty (Lieckens & Vandaele, 2007;Biehl et al, 2007) are some of the investigated uncertainty dimensions of return networks. The modelling efforts to include these uncertainty dimensions vary.…”