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The Robust Network Loading Problem Under Hose Demand Uncertainty: Formulation, Polyhedral Analysis, and Computations

We consider the network loading problem (NLP) under a polyhedral uncertainty description of traffic demands. After giving a compact multicommodity flow formulation of the problem, we state a decomposition property obtained from projecting out the flow variables. This property considerably simplifies the resulting polyhedral analysis and computations by doing away with metric inequalities. Then we focus on a specific choice of the uncertainty description, called the “hose model,” which specifies aggregate traffic upper bounds for selected endpoints of the network. We study the polyhedral aspects of the NLP under hose demand uncertainty and use the results as the basis of an efficient branch-and-cut algorithm. The results of extensive computational experiments on well-known network design instances are reported.

Intra-domain traffic engineering with shortest path routing protocols

Throughout the last decade, extensive deployment of popular intra-domain routing protocols such as open shortest path first and intermediate system–intermediate system, has drawn an ever increasing attention to Internet traffic engineering. This paper reviews optimization techniques that have been deployed for managing intra-domain routing in networks operated with shortest path routing protocols, and the state-of-the-art research that has been carried out in this direction.

Oblivious OSPF routing with weight optimization under polyhedral demand uncertainty

The desire for configuring well-managed open shortest path first (OSPF) routes to handle the communication needs in the contemporary business world with larger networks and changing service requirements has opened the way to use traffic engineering tools with the OSPF protocol. Moreover, anticipating possible shifts in expected traffic demands while using network resources efficiently has started to gain more attention. We take these two crucial issues into consideration and study the weight setting problem for OSPF routing problem with polyhedral demands. Our motivation is to optimize the link weight metric such that the minimum cost routing uses shortest paths with equal cost multipath splitting and the routing decisions are robust to possible fluctuations in demands. In addition to a compact mixed integer programming model, we provide an algorithmic approach with two variations to tackle the problem. We present several test results for these two strategies and discuss whether we could make our weight-managed OSPF comparable to unconstrained routing under polyhedral demand uncertainty.

A Hybrid Polyhedral Uncertainty Model for the Robust Network Loading Problem

Due to the increasing importance of effective communication in today’s business, designing net- works efficiently has started to attract more attention. In this chapter, we consider the Robust Network Loading Problem (Rob-NLP), which includes private network design as a special case, an important problem in telecommunications sector. We study the problem from the view point of a com- munications service provider who wants to lease private communication lines to its customers in the most cost-efficient manner. Rather than assuming that the point-to-point communication demands are given in advance, we introduce a polyhedral demand model, which inherits the strengths of two known polyhedral demand definitions. We first develop two compact mixed integer programming for- mulations of the problem. Then we provide some experimental results for several well-known network design instances to discuss the economic and performance implications of incorporating robustness into our design efforts.

Virtual Private Network Design Under Traffic Uncertainty

We propose different formulations as well as efficient solution approaches for the VPN design problem under traffic uncertainty with symmetric bandwidths.

An Eco-Improvement Methodology for Reduction of Product Embodied Energy in Discrete Manufacturing

There is an increasing awareness of global warming due to excessive greenhouse gas emissions and need to decrease the adverse effects of production on environment. It is known that greenhouse gasses are mainly originated from the fossil fuel combustion for electrical energy generation. This paper proposes an eco-improvement methodology for minimizing the product embodied energy by decreasing the electrical energy dependence of production. The methodology involves generation of STEP based process plan resulting in minimum embodied energy and simulation models enabling what-if analyses. The proposed eco-improvement methodology will be implemented in refrigerator compressor plant of Arcelik A.Ş., aiming at assessment and reduction of energy consumption in discrete manufacturing.