The use of computer models in planning upland land use

The British Ecological Society has suggested that computer‐based techniques could be used in the coordination of policies across different land uses in upland planning. This paper looks briefly at one such technique — a linear programming model — and describes its use at a regional scale for the Sedburgh area in north‐west England. The paper concludes with consideration of why such models are not in more common use.     

Spatial based compromise programming for multiple criteria decision making in land use planning

Today, competing land use is continuing to occur in many developed regions. In the Agricultural Development Zone of Western Sydney Region, which is characterised by complex landscape patterns, land use competition is widespread. From a land use planning perspective, identification of suitable locations for a given type of land use is necessary for decision makers to formulate land use alternatives in different locations, based on existing land potential and constraints. For such a region, use of a simple method that implements a categorical system and considers only inherent land characteristics in the analysis is often inadequate to arrive at an optimal spatial decision. The primary aim of this paper is to develop spatial modelling procedures for agricultural land suitability analysis using compromise programming (CoPr) and fuzzy set approach within a geographical information systems (GIS) environment. Five main sets of spatial data for use as decision criteria were developed by using fuzzy set methodology: a land suitability index (LSI) for maximising the land productivity objective; an erosion tolerance index (ETI) for minimising the erosion risk objective; a runoff curve number (CN) for maximising the water discharge regulation objective; an accessibility (RP) measure for maximising the land accessibility objective; and the proximity to water body (WP) for minimising the water pollution objective. An L _p -metric was used in the analysis utilising different strategies with representative indices ranging from a situation where full tradeoff among criteria occurs to a noncompensatory condition. Different weighting combinations were also applied, and decision analysis was carried out by using values ranging from 0 to 1.0, where 1.0 is considered as an ideal point. The CoPr model demonstrated in this paper yielded a promising result, as several different techniques of sensitivity analysis show reasonably good results. Likewise, an overlay of that result with the present land use/land cover indicates a good corresponding spatial matching between existing land use (orchard and cultivated land), and the cells (land parcels) classified as the best in CoPr. The results are amenable to various map display techniques, either using continuous values or by defining different cut off points in the data space within a raster GIS environment.     

线性规划在滇池草海污染目标控制优化决策中的运用

滇池草海污染治理工程是一个复杂的系统，做好规划是保证工程治理效果和有效使用投资的前提。本文将综合治理目标和各决策变量建立函数关系，并将其分析归纳为线性规划问题，根据线性规划的求解原则求出了目标规划的优化方案。     

SOIL EROSION AND GROUND WATER POLLUTION TRADEOFFS FOR NONIRRIGATED FARMING SYSTEMS1

ABSTRACT: Traditional focus on reducing one environmental externality may cause another externality to increase. This article examines the environmental and economic costs of abating soil loss and (or) nitrate leaching through alternative optimal production systems in the nonirrigated farming systems of Northeastern Oregon. Models estimating soil loss and nitrate‐nitrogen leaching rates associated with current production processes, are linked to a Multi‐Objective Programming (MOP) model. The results show that site specific conditions influence the level of abatement expenditures and optimal production strategies to reduce soil loss and leaching rates. Moreover, while existing production strategies are effective in reducing soil loss at little cost, no strategies could be identified to reduce nitrate leaching rate on some soils.     

Optimization of gas detector placement considering scenario probability and detector reliability in oil refinery installation

Gas detection system is a critical layer of protection in process safety. Leak scenario probability and detector reliability are two key factors in the optimization of gas detector placement. However, they are easily neglected in previous studies, which may lead to an inaccurate evaluation of the optimization solutions. In this study, a stochastic programming (SP) optimization method is proposed considering these two factors. In order to quantitatively represent the probability of leak scenarios, a complete accident scenario set (CASS) is built combining leak sources and wind fields. Then, the computational fluid dynamics (CFD) method is adopted for consequence modeling of gas dispersion. The Markov model is developed to predict the detector reliability. With the objective of minimal cumulative detection time (MCDT), the SP formulation considering scenario probability and detector reliability (MCDT-SPR) is proposed. By introducing the particle swarm optimization (PSO) algorithm, the optimization formulations can be solved. A case study is investigated on a diesel hydrogenation refining unit. Results validate this approach is promising to improve the detection efficiency. This method is more practical and matching with the actual industrial environment, where the leak scenarios and the detector reliability can change dynamically in real process setting.     

校园网建设的前期规划研究

本文对建设校园网的前期准备工作作了详细的阐述,对各阶段的工作详细作了规划,对于准备建设校园网的学校具有指导意义.     

A Routing and Scheduling System for Infectious Waste Collection

Most hospitals and clinics in Taiwan do not have on-site treatment facilities for their infectious waste and must rely on outside agencies for their collection and treatment. The problem of optimally routing and scheduling the collection of medical waste from a disperse group of facilities is recognized as a periodic vehicle routing problem. This study develops a computer system to solve the resulting optimization problem based on a two-phased approach proposed earlier. The first phase solves a standard vehicle routing problem to determine a set of individual routes for the collection vehicles. The second phase uses a mixed integer programming method to assign routes to particular days of the week. The computer system is user-friendly and consists of several Visual Basic programs while the geographical information system is incorporated to facilitate input and output interface and database management. An illustrative example for the infectious waste of 348 hospitals in the Tainan City area demonstrates the effectiveness of the system.     

A GIS-based Study for Optimizing the Total Emission Control Strategy in Lanzhou City

A geographic information system (GIS)-based approach with spatial analysis advantages was developed to optimize the total emission control scheme on air pollution in Lanzhou, combined with Models-3 modeling system and a linear programming model. GIS system is mainly used for establishing a multifactor assessment model to quantitatively divide environmental functional zone, and also used for selecting control sites in linear programming model. The results show that most of the urban area belongs to the second type of function, which is 122.8 km² occupying about 76.5% and the third type of about 32.9 km² occupying 20.5%. To reach the air quality up to the national standard in the entire control area, some large-emission factories need to cut emissions from 19% to 27%. For small but low-emission height sources, the cut may be as high 40%. The improvement of the ecological environment is urgently needed for controlling the background particle pollution in Lanzhou city.     

An Inexact Fuzzy-robust Two-stage Programming Model for Managing Sulfur Dioxide Abatement Under Uncertainty

In this study, an inexact fuzzy-robust two-stage programming (IFRTSP) method is developed for tackling multiple forms of uncertainties that can be expressed as discrete intervals, probabilistic distributions and/or fuzzy membership functions. The model can reflect economic penalties of corrective measures against any infeasibilities arising due to a particular realization of system uncertainties. Moreover, the fuzzy decision space can be delimited into a more robust one with the uncertainties being specified through dimensional enlargement of the original fuzzy constraints. A management problem in terms of regional air pollution control has been studied to illustrate the applicability of the proposed approach. Results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers identify desired pollution-abatement strategy with minimized system cost and maximized environmental efficiency.     

SLFP: a stochastic linear fractional programming approach for sustainable waste management

A stochastic linear fractional programming (SLFP) approach is developed for supporting sustainable municipal solid waste management under uncertainty. The SLFP method can solve ratio optimization problems associated with random information, where chance-constrained programming is integrated into a linear fractional programming framework. It has advantages in: (1) comparing objectives of two aspects, (2) reflecting system efficiency, (3) dealing with uncertainty expressed as probability distributions, and (4) providing optimal-ratio solutions under different system-reliability conditions. The method is applied to a case study of waste flow allocation within a municipal solid waste (MSW) management system. The obtained solutions are useful for identifying sustainable MSW management schemes with maximized system efficiency under various constraint-violation risks. The results indicate that SLFP can support in-depth analysis of the interrelationships among system efficiency, system cost and system-failure risk.