Environmental sustainability EOQ model for closed-loop supply chain under market uncertainty: A case study of printer remanufacturing

Government legislation significantly impacts closed-loop supply chain (CLSC) operations. This study examines the collection rate of and decisions on the product greening improvement level in a three-level CLSC with the government’s reward–penalty and a manufacturer’s subsidy policy. Four game-theoretic models are analyzed in order to evaluate the ways in which the policy and revenue-sharing contracts (RSCs) between the manufacturer and retailer affect the CLSC members’ optimal decisions and profits. We found that a reward–penalty and subsidy policy raise the collection rate, as well as the product greening improvement level. A manufacturer’s financial conflict of interest can be mitigated using RSCs. The RSCs between the manufacturer and the retailer also increase the profit of a recycling company that successfully coordinates the CLSC. An interesting result is that, when the RSCs are used under the subsidy policy, the collection rate is higher than it is in a centralized model. We also found that the subsidy level needs to be adjusted according to the price of the recycling resources, and that increasing the value of the recyclable resources and lowering the recycling costs in the early stages of the supply chain collaboration could lead to higher environmental sustainability. These results illustrate that using an RSC can effectively coordinate the CLSC, and can thus help policy implementation by governments.

Download Full-text

Economic pricing of complex products in a competitive closed-loop supply chain network under uncertainty: A case study of CoPS industry

RAIRO - Operations Research ◽

10.1051/ro/2021001 ◽

2021 ◽

Author(s):

Omid - Solgi ◽

Alireza - Taromi ◽

jafar ghidar kheljani ◽

Ehsan - Dehghani

Keyword(s):

Mathematical Model ◽

Supply Chain ◽

Closed Loop ◽

Optimal Solution ◽

Production Costs ◽

Supply Chain Network ◽

Closed Loop Supply Chain ◽

Complex Products ◽

Complex Product Systems

The development of technology, the globalization of the economy, and the unpredictable behavior of customers have led to a dynamic and competitive environment in the Complex Product Systems (CoPS) market. Besides, CoPS economic pricing is one of the key factors that significantly reduces production costs of Complex products and systems and increases competitiveness . In this regard, this paper develops a hybrid data envelopment analysis (DEA) fuzzy mathematical model for economic pricing of CoPS in a competitive closed-loop supply chain network under uncertainty, which leads to productivity and reducing the costs. To achieve the aim of this study, at first, different CoPS providers were evaluated using DEA based on a set of economic, technical, and geographical criteria . The advantage of this evaluation was choosing the right providers, eliminating inappropriate providers, and reducing complexity as one of the fundamental problems in mathematical models. Next, we maximize the benefit of the supply chain using the mathematical model. The objective of the proposed model is to identify strategic and tactical decisions at the same time to provide a fully optimal solution to the model. Furthermore, the presented robust model is capable of providing a stable structure under different uncertainties. This leads to minimizing the purchasing cost of CoPS manufacturers. Eventually, to evaluate the effectiveness and usefulness of the proposed approach, a case study was used to derive important managerial results .

Download Full-text

Sustainable Design of a Multi-Echelon Closed Loop Supply Chain under Uncertainty for Durable Products

Sustainability ◽

10.3390/su131911126 ◽

2021 ◽

Vol 13 (19) ◽

pp. 11126

Author(s):

Abbas Al-Refaie ◽

Yasmeen Jarrar ◽

Natalija Lepkova

Keyword(s):

Supply Chain ◽

Stochastic Model ◽

Environmental Sustainability ◽

Closed Loop ◽

Transportation Cost ◽

Return Rate ◽

Raw Material ◽

Mixed Integer ◽

Closed Loop Supply Chain ◽

Durable Products

The increased awareness of environmental sustainability has led to increasing attention to closed loop supply chains (CLSC). The main objective of the CLSC is to capture values from end-of-life (EOL) products in a way that ensures a business to be economically and environmentally sustainable. The challenge is the complexity that occurrs due to closing the loop. At the same time, considering stochastic variables will increase the realism of the obtained results as well as the complexity of the model. This study aims to design a CLSC for durable products using a multistage stochastic model in mixed-integer linear programming (MILP) while considering uncertainty in demand, return rate, and return quality. Demand was described by a normal distribution whereas return rate and return quality were represented by a set of discrete possible outcomes with a specific probability. The objective function was to maximize the profit in a multi-period and multi-echelon CLSC. The multistage stochastic model was tested on a real case study at an air-conditioning company. The computational results identified which facilities should be opened in the reversed loop to optimize profit. The results showed that the CLSC resulted in a reduction in purchasing costs by 52%, an annual savings of 831,150 USD, and extra annual revenue of 5459 USD from selling raw material at a material market. However, the transportation cost increased by an additional annual cost of 6457 USD, and the various recovery processes costs were annually about 152,897 USD. By running the model for nine years, the breakeven point will be after three years of establishing the CLSC and after the annual profit increases by 1.92%. In conclusion, the results of this research provide valuable analysis that may support decision-makers in supply chain planning regarding the feasibility of converting the forward chain to closed loop supply chain for durable products.

Download Full-text