Global spatial optimization with hydrological systems simulation: appliication to land-use allocation and peak runoff minimization

A general methodology is presented to integrate complex simulation models of hydrological systems into optimization models, as an alternative to scenario-based approaches. A gradient-based hill climbing algorithm is proposed to reach locally optimal solutions from distinct starting points. The gradient of the objective function is estimated numerically with the simulation model. A statistical procedure based on the Weibull distribution is used to build a confidence interval for the global optimum. The methodology is illustrated by an application to a small watershed in Ohio, where the decision variables are related to land-use allocations and the objective is to minimize peak runoff. The results suggest that this specific runoff function is convex in terms of the land-use variables, and that the global optimum has been reached. Modeling extensions and areas for further research are discussed

Solar energy and access to sunlight: an optimization model of energy supply and land-use design

This paper analyzes the interactions between land-use configurationsand on-site solar-energy devices used for heating and cooling. A mathematical programming model is developed to determine the optimal trade-off between the benefits of land-use development and those of solar-energy utilization. The measure of this trade-off is the extent to which access to sunlight should be guaranteed to prospective solar-energy users. The methodology is illustrated by numerical applications of a simplified model.

An econometric model of electricity distribution systems in urban areas

The purpose of this paper is to analyze the production structure of electricity distribution systems in urban areas. Under the assumption that electric utilities minimize their distribution capital and operating costs, input demand functions are developed for the various distribution facilities (for example, poles, wires, streetlights) and are econometrically estimated, using cross-sectional data on communities served by a major utility. These functions are then used to allocate total costs on the basis of marginal costs and to assess the extent of economies of scale and density in electricity distribution.

A mathematical experiment in landscape planning

A methodology for landscape planning is presented, including (1) a procedure for the quantification of visual intrusions in a landscape, (2) the specification of visual preference functions, and (3) the utilization of such functions for planning purposes, by means of an optimization procedure. This methodology is illustrated by numerical applications with synthetic data, and various areas for further research are outlined.

Centralized air-pollution treatment and the optimal location of industries

A cost-effectiveness optimization approach to industrial location planning and air quality management is developed, focusing on the feasibility of a centralized air-pollution-control system. The welfare criteria include air-pollution-control-related costs, but also other costs, such as commuting and land development costs. A multilevel optimization approach is outlined in order to devise economic incentives which may help to implement the optimal plan in a decentralized competitive decisionmaking context. A simplified linear programming formulation of the general model is applied to the Haifa area. Results confirm the need to adopt an integrated approach in examining the feasibility of a centralized air-pollution-control system.

Global spatial optimization with hydrological systems simulation: application to land-use allocation and peak runoff minimization

A general methodology is presented to integrate complex simulation models of hydrological systems into optimization models, as an alternative to scenario-based approaches. A gradient-based hill climbing algorithm is proposed to reach locally optimal solutions from distinct starting points. The gradient of the objective function is estimated numerically with the simulation model. A statistical procedure based on the Weibull distribution is used to build a confidence interval for the global optimum. The methodology is illustrated by an application to a small watershed in Ohio, where the decision variables are related to land-use allocations and the objective is to minimize peak runoff. The results suggest that this specific runoff function is convex in terms of the land-use variables, and that the global optimum has been reached. Modeling extensions and areas for further research are discussed