closed loop supply chain
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Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hanieh Shambayati ◽  
Mohsen Shafiei Nikabadi ◽  
Seyed Mohammad Ali Khatami Firouzabadi ◽  
Mohammad Rahmanimanesh ◽  
Sara Saberi

PurposeSupply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.Design/methodology/approachThe proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.FindingsThe findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.Originality/valueThere are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.HighlightsInvestigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).


2022 ◽  
Author(s):  
Omid Keramatlou ◽  
Nikbakhsh Javadian ◽  
Hosein Didehkhani ◽  
Mohammad Amirkhan

Abstract In this paper, a closed-loop supply chain (CLSC) is modeled to obtain the best location of retailers and allocate them to other utilities. The structure of CLSC includes production centers, retailers’ centers, probabilistic customers, collection, and disposal centers. In this research, two strategies are considered to find the best location for retailers by focusing on 1- the type of expected movement 2- expected coverage (distance and time) for minimizing the costs and maximizing the profit by considering the probabilistic customer and uncertainly demand. First of all, the expected distances between customers and retailers are calculated per movement method. These values are compared with the Maximum expected coverage distance of retailers, which is displayed in algorithm 1 heuristically, and the minimum value is picked. Also, to allocate customers to retailers, considering the customer's movement methods and comparing it with Maximum expected coverage time, which is presented in Algorithm 2 heuristically, the minimum value is chosen to this end, a bi-objective nonlinear programming model is proposed. This model concurrently compares Strategies 1 and 2 to select the best competitor. Based on the chosen strategy, the best allocation is determined by employing two heuristic algorithms, and the locations of the best retailers are determined. As the proposed model is NP-hard, a meta-heuristics (non-dominated sorting genetic) algorithm is employed for the solution process. Afterward, the effectiveness of the proposed model is validated and confirmed, and the obtained results are analyzed. For this purpose, a numerical example is given and solved through the optimization software.


Author(s):  
Matineh ziari ◽  
Mohsen Sheikh Sajadieh

Closed-loop supply chains have attracted more attention by researchers and practitioners due to strong government regulations, environmental issues, social responsibilities and natural resource constraints over past few years. This paper presents a mixed-integer linear programming model to design a closed-loop supply chain network and optimizing pricing policies under random disruption. Reusing the returned products is applied as a resilience strategy to cope with the waste of energy and improving supply efficiency. Moreover, it is necessary to find the optimal prices for both final and returned products. Therefore, the model is formulated based on demand function and it maximizes total supply chain’s profit. Finally, its application is explored through using the real data of an industrial company in glass industry.


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.


Author(s):  
Wenjie Liu ◽  
Wei Liu ◽  
Ningning Shen ◽  
Zhitao Xu ◽  
Naiming Xie ◽  
...  

2022 ◽  
Vol 101 ◽  
pp. 600-631
Author(s):  
Amirhossein Salehi-Amiri ◽  
Ali Zahedi ◽  
Fatemeh Gholian-Jouybari ◽  
Ericka Zulema Rodríguez Calvo ◽  
Mostafa Hajiaghaei-Keshteli

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