scholarly journals A multi-stage stochastic programming model for sustainable closed-loop supply chain network design with financial decisions: A case study of plastic production and recycling supply chain

2019 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Amir Salar Mohammadi ◽  
Akbar Alemtabriz ◽  
Mir Saman Pishvaee ◽  
Mostafa Zandieh
Keyword(s):  
Supply Chain ◽  
Stochastic Programming ◽  
Closed Loop ◽  
Programming Model ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Financial Decisions ◽  
Multi Stage ◽  
Stochastic Programming Model

A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network

2018 ◽  
Vol 195 ◽  
pp. 27-44 ◽  
Author(s):  
Md Abdul Quddus ◽  
Sudipta Chowdhury ◽  
Mohammad Marufuzzaman ◽  
Fei Yu ◽  
Linkan Bian
Keyword(s):  
Supply Chain ◽  
Stochastic Programming ◽  
Programming Model ◽  
Supply Chain Network ◽  
Two Stage ◽  
Fuel Supply ◽  
Stochastic Programming Model

scholarly journals Green closed-loop supply chain network design: a novel bi-objective chance-constraint approach

2021 ◽  
Vol 55 (2) ◽  
pp. 811-840
Author(s):  
Amin Reza Kalantari Khalil Abad ◽  
Seyed Hamid Reza Pasandideh
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Closed Loop ◽  
Programming Model ◽  
Expected Value ◽  
Chance Constraint ◽  
Chance Constrained Programming ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Closed Loop Supply Chain

In this paper, a novel chance-constrained programming model has been proposed for handling uncertainties in green closed loop supply chain network design. In addition to locating the facilities and establishing a flow between them, the model also determines the transportation mode between facilities. The objective functions are applied to minimize the expected value and variance of the total cost CO2 released is also controlled by providing a novel chance-constraint including a stochastic upper bound of emission capacity. To solve the mathematical model using the General Algebraic Modeling System (GAMS) software, four multi-objective decision-making (MODM) methods were applied. The proposed methodology was subjected to various numerical experiments. The solutions provided by different methods were compared in terms of the expected value of cost, variance of cost, and CPU time using Pareto-based analysis and optimality-based analysis. In Pareto-based analysis, a set of preferable solutions were presented using the Pareto front; then optimality-based optimization was chosen as the best method by using a Simple Additive Weighting (SAW) method. Experimental experiments and sensitivity analysis demonstrated that the performance of the goal attainment method was 13% and 24% better that of global criteria and goal programming methods, respectively.

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