A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kazhal Gharibi ◽  
Sohrab Abdollahzadeh
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Integer Linear Programming ◽  
Reverse Logistics ◽  
Mixed Integer Linear Programming ◽  
Closed Loop ◽  
Mixed Integer ◽  
Content Type ◽  
Logistics Network ◽  
Reverse Logistics Network

PurposeTo maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.Design/methodology/approachThe design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.FindingsThe results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.Originality/value(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

scholarly journals Reverse Logistics Network Design for Effective Management of Medical Waste in Epidemic Outbreaks: Insights from the Coronavirus Disease 2019 (COVID-19) Outbreak in Wuhan (China)

2020 ◽  
Vol 17 (5) ◽  
pp. 1770 ◽  
Author(s):  
Hao Yu ◽  
Xu Sun ◽  
Wei Deng Solvang ◽  
Xu Zhao
Keyword(s):  
Network Design ◽  
Reverse Logistics ◽  
Medical Waste ◽  
Mixed Integer ◽  
Effective Management ◽  
Human Beings ◽  
Epidemic Disease ◽  
Logistics Network ◽  
Short Period ◽  
Reverse Logistics Network

The outbreak of an epidemic disease may pose significant treats to human beings and may further lead to a global crisis. In order to control the spread of an epidemic, the effective management of rapidly increased medical waste through establishing a temporary reverse logistics system is of vital importance. However, no research has been conducted with the focus on the design of an epidemic reverse logistics network for dealing with medical waste during epidemic outbreaks, which, if improperly treated, may accelerate disease spread and pose a significant risk for both medical staffs and patients. Therefore, this paper proposes a novel multi-objective multi-period mixed integer program for reverse logistics network design in epidemic outbreaks, which aims at determining the best locations of temporary facilities and the transportation strategies for effective management of the exponentially increased medical waste within a very short period. The application of the model is illustrated with a case study based on the outbreak of the coronavirus disease 2019 (COVID-19) in Wuhan, China. Even though the uncertainty of the future COVID-19 spread tendency is very high at the time of this research, several general policy recommendations can still be obtained based on computational experiments and quantitative analyses. Among other insights, the results suggest installing temporary incinerators may be an effective solution for managing the tremendous increase of medical waste during the COVID-19 outbreak in Wuhan, but the location selection of these temporary incinerators is of significant importance. Due to the limitation on available data and knowledge at present stage, more real-world information are needed to assess the effectiveness of the current solution.

scholarly journals Accelerated Benders’ Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty

2017 ◽  
Author(s):  
Vahab Vahdat ◽  
Mohammad Ali vahdatzad
Keyword(s):  
Network Design ◽  
Reverse Logistics ◽  
Valid Inequalities ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Master Problem ◽  
Test Problems ◽  
Distribution Centers ◽  
Logistics Network ◽  
Reverse Logistics Network

In this paper, a two-stage stochastic programming modelling is proposed to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. The problem involves both strategic and tactical decision levels. The first stage deals with strategic decisions, which are the number, capacity, and location of forward and reverse facilities. At the second stage tactical decisions such as base stock level as an inventory policy is determined. The generic introduced model consists of suppliers, manufactures, and distribution centers in forward logistic and collection centers, remanufactures, redistribution, and disposal centers in reverse logistic. The strength of proposed model is its applicability to various industries. The problem is formulated as a mixed-integer linear programming model and is solved by using Benders’ Decomposition (BD) approach. In order to accelerate the Benders’ decomposition, a number of valid inequalities are added to the master problem. The proposed accelerated BD is evaluated through small-, medium-, and large-sized test problems. Numerical results reveal that proposed solution algorithm increases convergence of lower bound and upper bound of BD and is able to reach an acceptable optimality gap in a convenient CPU time.

Reverse Logistics Network Design for the Collection of End-of-Life Vehicles

2012 ◽  
Author(s):  
Hang Dai ◽  
Qing Wang
Keyword(s):  
Network Design ◽  
End Of Life ◽  
Facility Location ◽  
Reverse Logistics ◽  
Location Problem ◽  
Strategic Decision ◽  
Mixed Integer ◽  
Logistics Network ◽  
Reverse Logistics Network ◽  
Reverse Logistic

Reverse logistic network design problems involve strategic decisions which influence tactical and operational decisions. In particular, they involve facility location, transportation and inventory decisions, which affect the cost of the distribution system and the quality of the customer service level. Locating a collection centre is an important strategic decision, as purchasing or building facilities requires sizable investment; also the network transportation cost is affected by the selection of facility locations. The location that is selected must therefore take into account all the parameters and variables that are relevant and the decision may even affect demand. In this paper, network design for reverse logistics is investigated to solve the End-of-life Vehicles (ELV) collection centres location problem. We start by giving an understanding of the process of this reverse logistics network design by considering the features of reverse logistics, the role of ELV management and use of optimization methods. Based on this, a reverse logistics network design case for collection of End-of-life Vehicles is presented by formulating the problem into a mixed-integer linear program (MILP), taking into consideration the Capacitated Facility Location Problem. The solution to this model is obtained using IBM CPLEX Optimization Studio©. In addition the applicability of the model in other reverse logistic networks is discussed and the subjects for further research are pointed out.

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