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Closed-loop Supply Chain Network Design
100%
Quality of Returns
100%
Bi-objective
100%
Constraint Method
100%
Network Performance
50%
Performance Level
50%
Scenario Approach
50%
Stochastic Model
50%
Numerical Experiments
50%
CO2 Emissions
50%
Forward Flow
50%
Two-stage Stochastic Programming
50%
Network Configuration
50%
Computational Performance
50%
Quality Cost
50%
Heuristic Approach
50%
Time Limits
50%
Distribution Center
50%
Feasible Solution
50%
Non-dominated Sorting Genetic Algorithm (NSGA-II)
50%
Stochastic Modeling
50%
Uncertainty Type
50%
Pareto Frontier
50%
Linear Programming Relaxation
50%
Pareto Optimal Solution
50%
Bi-objective Model
50%
System Profit
50%
Remanufacturing Cost
50%
Value of Stochastic Solution
50%
Recoverable Resources
50%
Collection Facilities
50%
Reverse Flow
50%
Engineering
Closed Loop
100%
Supply Chain Network
100%
Genetic Algorithm
50%
Linear Programming
50%
Stochastic Model
50%
Numerical Experiment
50%
Feasible Solution
50%
Pareto Frontier
50%
Remanufacturing
50%
Pareto Optimal Solution
50%
Return Quantity
50%
Reverse Flow
50%
Stochastic Solution
50%
Computer Science
Network Design
100%
Supply Chain
100%
Pareto-optimality
50%
Network Performance
50%
Objective Model
50%
Genetic Algorithm
50%
Linear Programming
50%
Stochastic Model
50%
Network Configuration
50%
Metaheuristics
50%
Feasible Solution
50%
Pareto Frontier
50%
Economics, Econometrics and Finance
Closed-Loop Supply Chain
100%
Genetic Algorithm
50%
Metaheuristics
50%
Stochastic Modeling
50%