Fast data replenishment in peer to peer networks

Peer-to-Peer (P2P) based distributed storage systems have gain much popularity in recent years. These systems rely greatly on the data redundancy to be robust under network dynamics, i.e., the dynamics of peer entering and departing the network. Hence, it is important to implement mechanisms for maintaining a certain level of data redundancy at all times in the network. One such mechanism is called distributed data replenishment which attempts to repair data due to a peer failure or departure in a distributed manner. Such distributed data replenishment schemes make use of the well-known Repetition code, the Reed Solomon code, or recently the Random Linear Network Coding techniques. However, these schemes do not consider bandwidth associated with peers during the replenishment. In this thesis we explore techniques for fast replenishment by taking into consideration the bandwidth capacities of peer links. Specifically, we formulate the problem of fast replenishment via linear programing framework. Our simulation results indicate that the proposed fast replenishment significantly outperforms the current approach under many typical network scenarios

An Iterative Cyclic Algorithm for Designing Vaccine Distribution Networks in Low and Middle-Income Countries

The World Health Organization's Expanded Programme on Immunization (WHO-EPI) was developed to ensure that all children have access to common childhood vaccinations. Unfortunately, because of inefficient distribution networks and cost constraints, millions of children in many low and middle-income countries still go without being vaccinated. In this paper, we formulate a mathematical programming model for the design of a typical WHO-EPI network with the goal of minimizing costs while providing the opportunity for universal coverage. Since it is only possible to solve small versions of the model optimally, we describe an iterative heuristic that cycles between solving restrictions of the original problem and show that it can find very good solutions in reasonable time for larger problems that are not directly solvable.Comment: International Joint Conference on Industrial Engineering and Operations Management- ABEPRO-ADINGOR-IISE-AIM-ASEM (IJCIEOM 2019). Novi Sad, Serbia, July 15-17t

Analysing divergent logistic networks with local (R, S) inventory control

This paper deals with divergent logistic networks where the inventory at each node is controlled using a periodic review strategy with order-up-to level. An approximate method is presented to analyse the network performance (service levels, mean physical stock). The method is tested on a range of 2-echelon and 3-echelon networks by comparison to results from Monte Carlo simulation. We conclude that the approximation accuracy is sufficient for global network design in many practical situation

Computing (R, S) policies with correlated demand

This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we introduce a mixed integer linear programming (MILP) model which can be easily implemented by using off-theshelf optimisation software. Our modelling strategy can tackle a wide range of time-seriesbased demand processes, such as autoregressive (AR), moving average(MA), autoregressive moving average(ARMA), and autoregressive with autoregressive conditional heteroskedasticity process(AR-ARCH). In an extensive computational study, we compare the performance of our model against the optimal policy obtained via stochastic dynamic programming. Our results demonstrate that the optimality gap of our approach averages 2.28% and that computational performance is good