Abstract:In this paper we present a scenario analysis approach to perform water system planning and management under climatic and hydrological uncertainty. A DSS with a a graphical interface allows the user a friendly data-input phase and results analysis. Different generation techniques can be used to set up and analyze a number of scenarios. Uncertainty is modeled by a scenario-tree in a multistage environment, which includes different possible configurations of inflows in a wide time-horizon. The aim is to identify trends and essential features on which to base a robust decision policy. The DSS prevent obsolescence of optimizer codes exploiting the standard input format MPS. Obtained results show that scenario analysis could be an alternative approach to stochastic optimization when no probabilistic rules can be adopted and deterministic models are inadequate to represent uncertainty. Moreover, experimentation to a real water resources system in Sardinia, Italy, shows that the DSS can be easily used by practitioners and end-users.

Recently, there has been an increase in the use of meta-heuristic techniques addressing water distribution network design and management optimization problems. The meta-heuristic approach applied to water distribution systems has provided interesting results both for optimum pipe diameter sizing and for the location and management of network pressure control devices (i.e., pumps and valves). Regarding the insertion and calibration of pressure regulation valves, the use of meta-heuristic techniques is relatively recent. We search to strategically placing the valves in order to achieve pressure control in the network and, therefore, the valves must be calibrated in relation to water demand trends over time. In the Pressure Reference Method (PRM) described in this paper, the search for valve location is restricted to pipe-branch sets defined on the basis of hydraulic analysis and considering the range between minimum and maximum acceptable pressures in the network. In the PRM approach, the Scatter-Search (Glover and Laguna, 1997) meta-heuristic procedures are applied to obtain the optimal location and calibration of valves in the water distribution network.

Serum lipoprotein(a) levels are elevated in patients with early impairment of renal function and are associated with greater prevalence of cardiovascular disease. An inverse correlation between serum lipoprotein(a) level and creatinine clearance and a frequency distribution of apolipoprotein(a) isoforms similar to that of normal patients point to decreased renal catabolism as a probable mechanism of lipoprotein(a) elevation in patients with early renal failure.

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The minimum cost flow problem is to determine a least cost shipment of a commodity through a network G = (N, A) in order to satisfy demands at certain nodes from available supplies at other nodes. In this paper, we study a variant of the minimum cost flow problem where we are given a set R \subseteq A of arcs and require that each arc in R must carry the same amount of flow. This problem, which we call the simple equal flow problem, arose while modeling a water resource system management in Sardinia, Italy. We consider the simple equal flow problem in a directed network with n nodes, m arcs, and where all arc capacities and node supplies are integer and bounded by U. We develop several algorithms for the simple equal flow problem---the network simplex algorithm, the parametric simplex algorithm, the combinatorial parametric algorithm, the binary search algorithm, and the capacity scaling algorithm. The binary search algorithm solves the simple equal flow problem in O(log(nU)) applications of any minimum cost flow algorithm. The capacity scaling algorithm solves it in O(m(m + n logn) log (nU)) time, which is almost the same time needed to solve the minimum cost flow problem by the capacity scaling algorithm. These algorithms can be easily modified to obtain an integer solution of the simple equal flow problem.minimum cost flow problem, network simplex algorithm, scaling algorithm, parametric programming, network flows