A Novel Sustainable Closed-loop Supply Chain Network Design by Considering Routing and Quality of Products

Abstract

A B S T R A C TOne of the strategic decisions that can be made in supply chain is designing its network which has high impact on costs, and satisfaction level of customers. This paper focuses on designing a distribution network including determining the number and location of facilities, how to allocate the customers in network, and also determining the extent of carrying different products from different origins to different destinations; in this distribution network, according to the existing restrictions, customer demand is considered at minimum cost. In addition to secondary chain and reuse market as a retrieval option, model flexibility in defining quality and routing-locating is also among the innovation points of the model. Firstly, in forward chain the model consists of supplier, manufacturer, warehouse, distributor, and customer. In reverse chain, the model includes reuse market, secondary supply chain, collection, reprocess and disposal centers. The model could be generalized to industries with various strategies. Secondly, a sensitivity analysis was performed on a numerical example; also the nondominated sorting algorithm (NSGA II) was used for a large-sized sample; which its performance was measured by analysis of variance (ANOVA) test. The results show that, returned products with average quality lead to lower costs and higher social benefits; and meta-heuristic NSGA II method is efficient. Because, it creates business opportunities and leads to less economic and environmental costs.

Sustainability assessment in circular inter-firm networks: An integrated framework of industrial ecology and circular supply chain management approaches

“…Darbari et al (2019) started out with the AHP-TOPSIS method to determine the sustainability criteria relevant to stakeholders and then use the MILP with weighted fuzzy goal programming to optimise for the objectives identified through the MCDM method. In contrast, the results of a mixed integer non-linear programming (MINLP) model (Mirmohammadi and Sahraeian, 2018) and a MILP (Taleizadeh et al, 2019) have three objectives according to which they optimise first, and only in a second step do they employ the TOPSIS method and fuzzy AHP method respectively to identify the best option of the Pareto-optimal results. Another approach to evaluate Pareto-optimal solutions is the data envelopment analysis (DEA) method (Bal and Satoglu, 2019).…”

Assessing the social dimension in strategic network optimization for a sustainable development: The case of bioethanol production in the EU

“…(2018); Govindan et al. (2016b); Mirmohammadi & Sahraeian (2018); Pishvaee et al. (2014); Rahimi et al.…”

Designing a Sustainable Reverse Logistics Network Considering the Conditional Value at Risk and Uncertainty of Demand under Different Quality and Market Scenarios

“…Constraints (18) and (19) ensure that the total demand of customers may not be met in the direct flow, and that all returned products will be collected from customer centers in the reverse flow.…”

“…This issue causes many problems when using traditional supply [11]. For example, it is very difficult to find variable production prices in a situation where the price of raw materials fluctuates [18]. But The fuzzy logic can greatly help decision makers to deal with uncertainty [19].…”