Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Operations research and Decision support

Today, most companies have more complex supply chain networks. In such environment, Supply Chain (SC) members must focus on the efficient management and coordination of material flow in the multi-echelon system to handle with the challenges occur. In many cases, the supply chain of a company includes various decisions at different planning levels, such as facility location, inventory and transportation. Each of these decisions plays a significant role in the overall performance and the relationship between them cannot be ignored. Furthermore, effective management of material flow across a supply chain is a difficult problem due to the dynamic environment. In the past, the majority of the solution approaches used to solve multi-echelon supply chain problems were based on conventional methods using analytical techniques. However, they are insufficient to cope with the SC dynamics because of the inability to handle the complex interactions between the SC members and to represent stochastic behaviors existing in many real-world problems. Simulation modeling has recently become a major tool since an analytical model is unable to formulate a system that is subject to both variability and complexity. However, simulations require extensive runtime to evaluate many feasible solutions and to find the optimal one for a defined problem. To deal with this problem, simulation model needs to be integrated with optimization algorithms. In response to the aforementioned challenges, one of the primary objectives of this thesis is to propose a software getting the optimal transshipment policy for any multi echelon supply chain network and apply it on a public-sector supply chain application. The problem is formulated with objective functions to minimize the total cost. In order to find optimal number, two models were constructed one allowing variable transshipment and the other not. Due to the complexities of multi-echelon system and the underlying uncertainty, optimizing transshipment amount across the supply chain has become other major challenge to reduce the cost and to meet service requirements. In this context, the aim of this thesis is to present a simulation-based optimization framework, in which Discrete Event simulation is developed and integrated with the Simulated Annealing algorithm used for solving this optimization problem

Issued also as CD