scholarly journals A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy

2016 ◽  
Vol 13 (2) ◽  
pp. 199-213 ◽  
Author(s):  
Mustafa Jahangoshai Rezaee ◽  
Samuel Yousefi ◽  
Jamileh Hayati
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Closed Loop ◽  
Competitive Environment ◽  
Supply Chain Optimization ◽  
Quantity Discount ◽  
Closed Loop Supply Chain ◽  
Multi Objective ◽  
Objective Model

scholarly journals A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Maedeh Agahgolnezhad Gerdrodbari ◽  
Fatemeh Harsej ◽  
Mahboubeh Sadeghpour ◽  
Mohammad Molani Aghdam
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Perishable Products ◽  
Closed Loop Supply Chain ◽  
Multi Objective ◽  
Objective Model

scholarly journals The effects of different kinds of returns on an integrated chance-constrained stochastic mobile phone closed-loop supply chain configuration and supplier selection

2021 ◽  
Author(s):  
Shahrzad Ahmadi Kermanshah
Keyword(s):  
Supply Chain ◽  
Mobile Phone ◽  
Supplier Selection ◽  
Closed Loop ◽  
Stochastic Demand ◽  
Mixed Integer ◽  
Chance Constraint ◽  
Distance Method ◽  
Closed Loop Supply Chain ◽  
Multi Objective

One of the important concerns in the world is E-waste. Ending up e-waste in the landfill and inappropriate disposing of it are hazardous to the environment. The goal of this research is to design and optimize a multi-period, multi-product, multi-echelon, and multi-customer Closed-Loop Supply Chain (CLSC) network for a mobile phone network considering different types of product returns. Commercial, end of life, and end-of-use returns are well-known in practice. In this research, a multi-objective mixed-integer linear programming formulation with stochastic demand and return is proposed to maximize the total profit in the mobile phone CLSC network, alongside maximizing the weights of eligible suppliers which are estimated based on a fuzzy method for efficient supplier selection and order allocation. Chance-constraint programming is applied in order to deal with the stochastic demand and return. Moreover, distance method and εε-constraint technique are employed to solve the proposed multi-objective problem. The application of the proposed mathematical model is illustrated in Toronto, Canada using real maps.

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