Study on Location Model of Multi-Node Distribution Centers in Closed-Loop Supply Chain

ICLEM 2010 ◽  
2010 ◽  
Author(s):  
Renxiang Wang ◽  
Yapeng Zhao ◽  
Jianmin Sho
Author(s):  
Nasrin Mohabbati-Kalejahi ◽  
Alexander Vinel

Hazardous materials (hazmat) storage and transportation pose threats to people’s safety and the environment, which creates a need for governments and local authorities to regulate such shipments. This paper proposes a novel mathematical model for what is termed the hazmat closed-loop supply chain network design problem. The model, which can be viewed as a way to combine several directions previously considered in the literature, includes two echelons in the forward direction (production and distribution centers), three echelons in the backward direction (collection, recovery, and disposal centers), and emergency response team positioning. The two objectives of minimizing the strategic, tactical, and operational costs as well as the risk exposure on road networks are considered in this model. Since the forward flow of hazmat is directly related to the reverse flow, and since hazmat accidents can occur at all stages of the lifecycle (storage, shipment, loading, and unloading, etc.), it is argued that such a unified framework is essential. A robust framework is also presented to hedge the optimization model in case of demand and return uncertainty. The performance of both models is evaluated based on a standard dataset from Albany, NY. Considering the trade-offs between cost and risk, the results demonstrate the design of efficient hazmat closed-loop supply chain networks where the risk exposure can be reduced significantly by employing the proposed models.


2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Komeyl Baghizadeh ◽  
Julia Pahl ◽  
Guiping Hu

In this study, we present a multiobjective mixed-integer nonlinear programming (MINLP) model to design a closed-loop supply chain (CLSC) from production stage to distribution as well as recycling for reproduction. The given network includes production centers, potential points for establishing of distribution centers, retrieval centers, collecting and recycling centers, and the demand points. The presented model seeks to find optimal locations for distribution centers, second-hand product collection centers, and recycling centers under the uncertainty situation alongside the factory’s fixed points. The purpose of the presented model is to minimize overall network costs including processing, establishing, and transportation of products and return flows as well as environmental impacts while maximizing social scales and network flexibility according to the presence of uncertainty parameters in the problem. To solve the proposed model with fuzzy uncertainty, first, the improved epsilon (ε)-constraints approach is used to transform a multiobjective to a single-objective problem. Afterward, the Lagrangian relaxation approach is applied to effectively solve the problem. A real-world case study is used to evaluate the performance of the proposed model. Finally, sensitivity analysis is performed to study the effects of important parameters on the optimal solution.


2022 ◽  
Author(s):  
Shahab Safaei ◽  
Peiman Ghasemi ◽  
Fariba Goodarzian ◽  
Mohsen Momenitabar

Abstract In the closed-loop supply chain, demand plays a critical role. The flow of materials and commodities in the opposite direction of the normal chain is inevitable too. So, in this paper, a new multi-echelon multi-period closed-loop supply chain network is addressed to minimize the total costs of the network. The considered echelons include suppliers, manufacturers, distribution centers, customers, and recycling and recovery units of components in the proposed network. Also, a linear programming model considering factories' vehicles and rental cars of transportation companies is formulated for the proposed problem. Moreover, the products demand is predicted by Auto-Regressive Integrated Moving Average (ARIMA) time series model to decrease the amount of shortage may happens in the network. To solve the proposed model, GAMS software is used in small-sized problems and a genetic algorithm in large-sized problems is employed. Numerical results show that the proposed model is closer to the real situation and the proposed solution method is efficient. Accordingly, sensitivity analysis is performed on important parameters to show the performance of the proposed model.


2020 ◽  
Vol 31 (5) ◽  
pp. 1351-1373
Author(s):  
Younis Jabarzadeh ◽  
Hossein Reyhani Yamchi ◽  
Vikas Kumar ◽  
Nader Ghaffarinasab

PurposeThis paper aims to present a closed-loop supply chain (CLSC) optimization problem for a perishable agricultural product to achieve three pillars of sustainability, including minimizing total network costs and carbon dioxide emissions from different network activities and maximizing responsiveness to demands simultaneously.Design/methodology/approachThe research problem is formulated as a multi-objective mixed-integer linear programming model, and classical approaches, including the LP-Metric and weighted Tchebycheff method, have been applied to solve the optimization model. A set of test problems has been proposed to validate the model, and the results are presented.FindingsComputational time to find Pareto optimal solutions by using the weighted Tchebycheff method was twice as much as that of the LP-Metric method. Also, the result of the study is a mathematical model that can be applied to other products that are close to the fruit, such as vegetables.Research limitations/implicationsThe present study is limited to fruits supply chains and the inventory is considered at the distribution centers only. The study also considers only one type of transport.Practical implicationsThe paper can assist supply chain managers to define strategies to achieve a sustainable CLSC network configuration for the fruits.Originality/valueThis study is one of the early studies to consider environmental indicators in fruits supply chain design along with two other indicators of sustainability, namely, economic and social indicators. Therefore, this can help supply chain managers to achieve sustainability by optimizing location decisions, inventory quantities and flow between facilities.


2015 ◽  
Vol 744-746 ◽  
pp. 1910-1914
Author(s):  
Zhuo Dai

This paper designs a model of muti-echelon closed-loop supply chain network (CLSC network). CLSC network includes raw material suppliers, manufacturers, distribution centers, collection centers and customer zones. The purpose of this paper is to minimize the overall costs of CLSC network. The overall costs include transportation cost, fixed cost, variable cost, penalty cost. This model is a mixed integer linear programming model. In general, it is very difficult to solve the model. Cplex12.6 is used in order to deal with this model. The results show that this model can be solved by Cplex12.6 well.


2019 ◽  
Vol 39 (1) ◽  
pp. 58-76 ◽  
Author(s):  
Behzad Karimi ◽  
Amir Hossein Niknamfar ◽  
Babak Hassan Gavyar ◽  
Majid Barzegar ◽  
Ali Mohtashami

Purpose Today’s, supply chain production and distribution of products to improve the customer satisfaction in the shortest possible time by paying the minimum cost, has become the most important challenge in global market. On the other hand, minimizing the total cost of the transportation and distribution is one of the critical items for companies. To handle this challenge, this paper aims to present a multi-objective multi-facility model of green closed-loop supply chain (GCLSC) under uncertain environment. In this model, the proposed GCLSC considers three classes in case of the leading chain and three classes in terms of the recursive chain. The objectives are to maximize the total profit of the GCLSC, satisfaction of demand, the satisfactions of the customers and getting to the proper cost of the consumers, distribution centers and recursive centers. Design/methodology/approach Then, this model is designed by considering several products under several periods regarding the recovery possibility of products. Finally, to evaluate the proposed model, several numerical examples are randomly designed and then solved using non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. Then, they are ranked by TOPSIS along with analytical hierarchy process so-called analytic hierarchy process-technique for order of preference by similarity to ideal solution (AHP-TOPSIS). Findings The results indicated that non-dominated ranked genetic algorithm (NRGA) algorithm outperforms non-dominated sorting genetic algorithm (NSGA-II) algorithm in terms of computation times. However, in other metrics, any significant difference was not seen. At the end, to rank the algorithms, a multi-criterion decision technique was used. The obtained results of this method indicated that NSGA-II had better performance than ones obtained by NRGA. Originality/value This study is motivated by the need of integrating the leading supply chain and retrogressive supply chain. In short, the highlights of the differences of this research with the mentioned studies are as follows: developing multi-objective multi-facility model of fuzzy GCLSC under uncertain environment and integrating the leading supply chain and retrogressive supply chain.


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