Report : review of the literature : maintenance and rehabilitation costs for roads (Risk-based Analysis)

Realistic estimates of short- and long-term (strategic) budgets for maintenance and rehabilitation of road assessment management should consider the stochastic characteristics of asset conditions of the road networks so that the overall variability of road asset data conditions is taken into account. The probability theory has been used for assessing life-cycle costs for bridge infrastructures by Kong and Frangopol (2003), Zayed et.al. (2002), Kong and Frangopol (2003), Liu and Frangopol (2004), Noortwijk and Frangopol (2004), Novick (1993). Salem 2003 cited the importance of the collection and analysis of existing data on total costs for all life-cycle phases of existing infrastructure, including bridges, road etc., and the use of realistic methods for calculating the probable useful life of these infrastructures (Salem et. al. 2003). Zayed et. al. (2002) reported conflicting results in life-cycle cost analysis using deterministic and stochastic methods. Frangopol et. al. 2001 suggested that additional research was required to develop better life-cycle models and tools to quantify risks, and benefits associated with infrastructures. It is evident from the review of the literature that there is very limited information on the methodology that uses the stochastic characteristics of asset condition data for assessing budgets/costs for road maintenance and rehabilitation (Abaza 2002, Salem et. al. 2003, Zhao, et. al. 2004). Due to this limited information in the research literature, this report will describe and summarise the methodologies presented by each publication and also suggest a methodology for the current research project funded under the Cooperative Research Centre for Construction Innovation CRC CI project no 2003-029-C

Integrating Energy Storage into the Smart Grid: A Prospect Theoretic Approach

In this paper, the interactions and energy exchange decisions of a number of geographically distributed storage units are studied under decision-making involving end-users. In particular, a noncooperative game is formulated between customer-owned storage units where each storage unit's owner can decide on whether to charge or discharge energy with a given probability so as to maximize a utility that reflects the tradeoff between the monetary transactions from charging/discharging and the penalty from power regulation. Unlike existing game-theoretic works which assume that players make their decisions rationally and objectively, we use the new framework of prospect theory (PT) to explicitly incorporate the users' subjective perceptions of their expected utilities. For the two-player game, we show the existence of a proper mixed Nash equilibrium for both the standard game-theoretic case and the case with PT considerations. Simulation results show that incorporating user behavior via PT reveals several important insights into load management as well as economics of energy storage usage. For instance, the results show that deviations from conventional game theory, as predicted by PT, can lead to undesirable grid loads and revenues thus requiring the power company to revisit its pricing schemes and the customers to reassess their energy storage usage choices.Comment: 5 pages, 4 figures, conferenc

NETWORK DESIGN UNDER DEMAND UNCERTAINTY

A methodology for network design under demand uncertainty is proposed in this dissertation. The uncertainty is caused by the dynamic nature of the IP-based traffic which is expected to betransported directly over the optical layer in the future. Thus, there is a need to incorporate the uncertainty into a design modelexplicitly. We assume that each demand can be represented as a random variable, and then develop an optimization model to minimizethe cost of routing and bandwidth provisioning. The optimization problem is formulated as a nonlinear Multicommodity Flow problemusing Chance-Constrained Programming to capture both the demand variability and levels of uncertainty guarantee. Numerical work ispresented based on a heuristic solution approach using a linear approximation to transform the nonlinear problem to a simpler linearprogramming problem. In addition, the impact of the uncertainty on a two-layer network is investigated. This will determine how theChance-Constrained Programming based scheme can be practically implemented. Finally, the implementation guidelines for developingan updating process are provided

An efficient model for mobile network slice embedding under resource uncertainty

The fifth generation (5G) of mobile networks will support several new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. End-to-end network slicing is a key-feature of 5G since it allows to share and at the same time isolate resources between several different use cases as well as between tenants by providing logical network. The virtual separation of the network slices on a common end-to-end mobile network infrastructure enables an efficient usage of the underlying network resources and provides means for security and safety related isolation of the defined logical networks. A much-discussed challenge is the reuse or overbooking of resources guaranteed by contract. However, there is a consensus that over-provisioning of mobile communication bands is economically infeasible and a certain risk of network overload is acceptable for the majority of the 5G use cases. In this paper, an efficient model for mobile network slice embedding is presented which enables an informed decision on network slice admission. This is based on the guaranteed end-to-end mobile network resources that have to be provided on the one hand and the capacities and capabilities of the underlying network infrastructure on the other hand. The network slice embedding problem is solved in form of a Mixed Integer Linear Program with an uncertainty-aware objective function. Subsequently, the confidence in the availability of each resource is analyzed

Proactive model to determine information technologies supporting expansion of air cargo network

Shippers and recipients expect transportation companies to provide more than just the movement of a package between points; certain information must be available to them as well, to enable forecasts and plans within the supply chain. The transportation companies also need the information flow that undergirds a transportation grid, to support ad-hoc routing and strategic structural re-alignment of business processes. This research delineates the information needs for an expanding air cargo network, then develops a new model of the information technologies needed to support expansion into a new country. The captured information will be used by shippers, recipients, and the transportation provider to better guide business decisions. This model will provide a method for transportation companies to balance the tradeoffs between the operating efficiencies, capital expenditures, and customer expectations of their IT systems. The output of the model is a list of technologies – optimized by cost – which meet the specific needs of internal and external customers when expanding air cargo networks into a new country