Proposed Ranking System Research Articles

Mine design selection under uncertainty

Frequently, alternative technically feasible mine designs and production scenarios are available for decision makers to consider. The selection of a mine design is then based on estimating net present values of all possible, technically feasible mine plans so as to select the one with the maximum value. Conventional risk and sensitivity analysis are also performed as decision supporting tools. Given the multiple market and geological uncertainties associated with mining investments, as well as the management flexibility to respond to new future information, the sufficiency of conventional economic analysis tools based on the static discounted cash flow analysis may be questionable. This paper proposes a multicriteria ranking system for selection between alternative mine designs under uncertainty. The system is based on integrating multiple market and geological uncertainties as well as the operating flexibility to revise the ultimate pit limits using a Monte Carlo based real options valuation (ROV) model. To compare the methods, the article applies the proposed system, along with other three ranking methods, to rank possible mine designs for a copper and a gold mine. It has been found that, in addition to the higher value of the mine design selected by the proposed system, its average mis-ranking is significantly lower than the other three ranking methods. For the copper mine, a reduction in the average mis-ranking of 37–58% could be achieved when using the proposed ranking system, while for the gold mine case, a higher reduction of 51–64% in the average mis-ranking is obtained.

Unified dosimetry index (UDI): a figure of merit for ranking treatment plans

We have formulated a unified dosimetry index (UDI) that computes, for any given treatment plan, its deviations in terms of dose coverage, conformity, homogeneity, and dose gradient vis‐à‐vis an ideal plan (which we define as a dosimetry plan of perfect dose coverage, conformity, homogeneity, and step‐wise fall‐off to zero dose outside the planning target volume). In order to validate the UDI scoring system, 21 stereotactic cranial radiosurgery cases were evaluated retrospectively. The cases were planned on the BrainSCAN treatment planning system (BrainLAB, Feldkirchen, Germany) using 6 to 8 non‐coplanar static beams collimated with the micro multi‐leaf collimator (mMLC). We suggest a technique for creating a ranking system that can be utilized for plan evaluation and comparison between multiple plans. Under this system treatment plans are classified as “excellent”, “good”, “average”, or “poor”. The proposed ranking system can be utilized as a general guide for generating an optimal dosimetry plan for external beam radiation therapy.

A System for Ranking the Research Potential of 19th- and 20th- Century Farmstead Sites

There is a need to establish a systematic and objective process for evaluating the research potential of farmstead sites so that intelligent choices can be made in deciding which sites should be investigated and managed and which should not. We propose the use of a checklist of traits that measure a site's research potential. Each of the traits in the checklist is assigned a score based on its value in providing important data for analyzing a site. The proposed checklist is a tool for organizing our observations and permitting comparisons from one site to the next as well as one project to the next. Applying the proposed ranking system involves documentary research and some archaeological fieldwork. This checklist approach is put forward in the hopes of generating discussion and suggestions on how best to deal with these types of sites in terms of immediate needs of compliance with federal and state historic preservation laws and regulations.

Formulating an Ecosystem Approach to Environmental Protection

The U.S. Environmental Protection Agency (EPA) has embraced a new strategy of environmental protection that is place-driven rather than program-driven. This new approach focuses on the protection of entire ecosystems. To develop an effective strategy of ecosystem protection, however, EPA will need to: (1) determine how to define and delineate ecosystems and (2) categorize threats to individual ecosystems and priority rank ecosystems at risk. Current definitions of ecosystem in use at EPA are inadequate for meaningful use in a management or regulatory context. A landscape-based definition that describes an ecosystem as a volumetric unit delineated by climatic and landscape features is suggested. Following this definition, ecosystems are organized hierarchically, from megaecosystems, which exist on a continental scale (e.g., Great Lakes), to small local ecosystems.Threats to ecosystems can generally be categorized as: (1) ecosystem degradation (occurs mainly through pollution) (2) ecosystem alteration (physical changes such as water diversion), and (3) ecosystem removal (e.g., conversion of wetlands or forest to urban or agricultural lands). Level of threat (i.e., how imminent), and distance from desired future condition are also important in evaluating threats to ecosystems. Category of threat, level of threat, and "distance" from desired future condition can be combined into a three-dimensional ranking system for ecosystems at risk. The purpose of the proposed ranking system is to suggest a preliminary framework for agencies such as EPA to prioritize responses to ecosystems at risk.KEY WORDS: Ecosystem approach; Ecological risk assessment; Environmental protection; EPA

Intelligent ε-optimal path prediction for vehicular travel

This paper addresses the problem of predicting the path of a vehicle performing a transit mission. Such a mission proceeds from a start location to a goal location guided by an intelligent planning strategy. Given the history of a path from a start location to a current location, the objectives are: 1) to estimate the cost criterion guiding the travel; 2) to predict the goal location; and 3) to predict the future path leading to the predicted goal location. First, a cost criterion explaining the decision-making strategy of the observed vehicle is estimated using a correlation measure comparing the observed path data to optimal path search information. This correlation is expressed in terms of the tolerance /spl epsiv/ of an /spl epsiv/-optimal path. Next, a region of plausible goal locations is predicted assuming that the vehicle will proceed using either optimal decisions or /spl epsiv/-optimal decisions in the future. The predicted goal location of the vehicle is determined by selecting the point in the region of plausible goal locations that has the highest heuristic merit, as determined by a proposed ranking system. Finally, from auxiliary search information, the future path is predicted. This problem is generalized to predicting the future path of a point vehicle traveling in an arbitrary dimensional space. >