الفهرس 
Abstract
Mixed Variables Optimization ProblemOnly 14 pages are availabe for public view
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Abstract
The reverse logistics is referred to as the process of logistics management involved in planning, managing, and controlling the flowof medical waste and hospital waste for either reuse or final disposal of waste. The area of reverse logistics has recently receivedconsiderable attention, due to a combination of environmental,economic and social factors. Much of the previous work has beenexploratory, emphasizing the need and importance of reverse logistics issues.
Nowadays, some studies have focused on the practicability of applying the concept of reverse logistics to the medical waste management, which may greatly improve the efficiency of medicalwaste management and reduce the negative influence it may impose on the environment.
The concept of closed loop supply chain reverse logistics network,which means the combination of forward and reverse logistics, isnow widely taking attention both researchers and practitionersfrom the last decade because of the customer expectations, increasing business competition and regulatory pressures.
The area of optimization has received enormous attention in recent years, primarily because of the rapid progress in computertechnology, including the development and availability of user-friendly software, high-speed with parallel processors and artificial neural networks.
Optimization problem involving both continuous and discrete variables are able to describe many real world problems. Mixed variable optimization problem is important but difficult to solve. Different type of problems in economics, science, engineering, traffic
and medicine can be reformulated as a mixed variable optimization problem. So, in this work, we pay a great attention to solvethis problem. A mixed variable optimization problem is an optimization problem refers to mathematical programming with continuous variables and discrete variables.
Global optimization problems represent a main category of suchproblems. Global optimization refers to finding the extreme valueof a given non-convex function in a certain feasible region. Suchproblems are classified in two classes; unconstrained and constrained problems.
In this study, we propose a cellular genetic algorithm with patternsearch for solving nonlinear mixed variable optimization problemand it is found promising when compared with the other methods.The proposed method is implemented for solving mixed variable
optimization model for minimizing costs of medical waste reverse
logistics networks. Finally, we propose a mixed variable optimization model for minimizing costs of hospital waste closed loop supply chain reverse logistics networks and solving this model withour proposed method.
The reverse logistics is referred to as the process of logistics management involved in planning, managing, and controlling the flowof medical waste and hospital waste for either reuse or final disposal of waste. The area of reverse logistics has recently receivedconsiderable attention, due to a combination of environmental,economic and social factors. Much of the previous work has beenexploratory, emphasizing the need and importance of reverse logistics issues.
Nowadays, some studies have focused on the practicability of applying the concept of reverse logistics to the medical waste management, which may greatly improve the efficiency of medicalwaste management and reduce the negative influence it may impose on the environment.
The concept of closed loop supply chain reverse logistics network,which means the combination of forward and reverse logistics, isnow widely taking attention both researchers and practitionersfrom the last decade because of the customer expectations, increasing business competition and regulatory pressures.
The area of optimization has received enormous attention in recent years, primarily because of the rapid progress in computertechnology, including the development and availability of user-friendly software, high-speed with parallel processors and artificial neural networks.
Optimization problem involving both continuous and discrete variables are able to describe many real world problems. Mixed variable optimization problem is important but difficult to solve. Different type of problems in economics, science, engineering, traffic
and medicine can be reformulated as a mixed variable optimization problem. So, in this work, we pay a great attention to solvethis problem. A mixed variable optimization problem is an optimization problem refers to mathematical programming with continuous variables and discrete variables.
Global optimization problems represent a main category of suchproblems. Global optimization refers to finding the extreme valueof a given non-convex function in a certain feasible region. Suchproblems are classified in two classes; unconstrained and constrained problems.
In this study, we propose a cellular genetic algorithm with patternsearch for solving nonlinear mixed variable optimization problemand it is found promising when compared with the other methods.The proposed method is implemented for solving mixed variable
optimization model for minimizing costs of medical waste reverse
logistics networks. Finally, we propose a mixed variable optimization model for minimizing costs of hospital waste closed loop supply chain reverse logistics networks and solving this model withour proposed method.
The reverse logistics is referred to as the process of logistics management involved in planning, managing, and controlling the flowof medical waste and hospital waste for either reuse or final disposal of waste. The area of reverse logistics has recently receivedconsiderable attention, due to a combination of environmental,economic and social factors. Much of the previous work has beenexploratory, emphasizing the need and importance of reverse logistics issues.
Nowadays, some studies have focused on the practicability of applying the concept of reverse logistics to the medical waste management, which may greatly improve the efficiency of medicalwaste management and reduce the negative influence it may impose on the environment.