Budget Availability Research Articles

Decision support system for water infrastructure planning under budget uncertainty: A case study from Thailand

: This paper proposes a decision support system (DSS) for long‐term investment planning of water resources infrastructure taking into account the uncertainty inherent in budget availability. The DSS adopts integer goal programming (IGP) to first select the projects that best contribute to the social, economic, and environmental goals of the project areas. Stochastic programming (SP) then selects the budget level that has the highest expected returns for all possible states of the system. The developed DSS recommended planning for a high budget level in the Chiang Mai case study area, which is consistent with the level of funding that was allocated and distributed over the 25‐year analysis period. Uncertainty in cost estimation was also found to play a major role in project selection.

National health budgets for expensive orphan drugs: Gaucher disease in Israel as a model

Drugs for orphan diseases are often disproportionately costly, although the patient population is by definition small. In Israel, with a high percentage of Ashkenazi Jews and therefore many patients with Gaucher disease, the expense of enzyme replacement therapy for all patients would be prohibitive. For this reason, with approval of enzyme replacement therapy in Israel, the low-dose regimen (30 U/kg/month), less than one quarter of the manufacturer's original recommended dosage, was instituted as the starting regimen. A Gaucher Committee of medical experts under the auspices of the Ministry of Health determines eligibility for enzyme replacement therapy based on criteria of disease severity. At the advent of 2006, 184 patients in Israel receive enzyme replacement therapy, about one third of all known Israeli patients with Gaucher disease. The national budget provides capitation for each patient to each Sick Fund via a health care basket for severe/expensive treatments. After nearly nine years, the benefits of these innovations include availability of budget for patients requiring enzyme replacement therapy, evidence-based data that low dose is safe and effective, and that untreated mildly affected patients generally continue a benign disease trajectory, but if necessary, have recourse to enzyme replacement therapy, that is, patient care is never compromised. Questionnaires of satisfaction with this system highlight good outcome scores. For countries with limited resources, the use of an impartial committee of experts is recommended, as is long-term surveillance of all patients; maintenance protocols or even drug vacations as enzyme-treated patients approach normalization of disease parameters should be considered.

Neutronic and thermo-mechanic calculations for the design of the TRADE spallation target

The spallation target of the TRADE Project—at present in stand-by waiting for further decisions on budget availability—is based on the concept of a solid target cooled by forced convection. The solid target approach presents a maximum power density limitation due to the high thermal stresses which are generated by the huge differential temperatures in the material. These at their turn stem from the proton energy loss in the volume of the target material and the water cooling on the external surface. The maximum operating beam power and the proton beam profile were imposed by experimental and geometrical considerations; the peak power density of the target material (Tantalum) turned out to be about 8E09 W/m 3. The power generation of the reference configuration of the spallation target was accurately calculated by the code MCNPX, which allows to simulate individual particle tracks in 3-D geometry, starting from the accelerated 140 MeV protons down to all the relevant secondary particles. The temperature distribution and the stress and strain maps were calculated by the thermo-mechanical ANSYS code. The main calculation results are reported. The elastic-plastic analysis of the target material is performed in terms of maximum allowable deformation, fatigue and progressive plastic deformation.

Phosphorus budget and phosphorus availability in soils under organic and conventional farming

The aim of this work was to assess to which extent organic farming practices would affect the accumulation of total and available phosphorus (P) in a cropped soil in comparison to conventional practices. In order to achieve this, soil samples were taken from a long-term field trial comparing a non-fertilised control (NON), two conventionally cultivated treatments (MIN, CON), and two organically cultivated treatments (ORG, DYN). Soil samples were taken from each treatment at two depths (0-20 and 30-50 cm) before starting the field trial (1977) and at the end of every three crop rotations (1984, 1991 and 1998). They were then analysed for total P (Pt), total inorganic P (Pi), total organic P (Po) and isotopically exchangeable Pi. After 21 years, the average P input-output budget reached -20.9 kg P ha−1 a−1 for NON, -7.8 for DYN, -5.7 for ORG, -5.0 for MIN and +3.8 for CON. Total P, Pi as well as the amount of Pi isotopically exchangeable within 1 minute (E1) were positively correlated to the P budget. Comparison between P budget and Pt in the top- and subsoils of the fertilised treatments suggested a net transfer of P from the 0–20 to the 30–50 cm layers between 13 and 26 kg P ha−1 a−1during the first rotation and between 3 and 12 kg P ha−1 a−1during the second rotation. During the third rotation a net upward movement of P from the subsurface to the topsoil ranging between 3.7 and 10.5 kg P ha−1 a−1was estimated. In the topsoil, E1decreased from an initial value of 12 mg P kg−1 to 11 in CON, 8 in MIN, 6 in ORG, 5 in DYN and 2 in NON after 21 years. In the subsoil, E1 increased from an initial value of 2 mg P kg−1 to 4 in MIN, ORG, DYN and NON and to 6 in CON. These results show that, with the exception of NON, all treatments had still an adequate level of available P after 21 years of trial and that, in this low to moderately P sorbing soil, an equilibrated input-output budget allows to maintain P availability at a constant level. In the organic systems, yields have so far partly been attained at the expense of soil reserves or residual P from earlier fertiliser applications.

Capital Budgeting with Multiple Criteria and Multiple Decision Makers: A Fuzzy Approach

A capital budgeting model with multiple criteria and multiple decision makers (MCMDM) is more likely to provide realistic solutions than linear or goal programming models. This paper adopts a fuzzy approach to solve MCMDM capital budgeting problems. This approach is based on two fundamental human cognitive processes: (i) all decision makers who are involved in the capital budgeting problem have goal setting and compromise behavior for seeking multiple criteria, and (ii) each decision maker has a preference for the budget availability level. A solution procedure is proposed to systematically identify a fuzzy optimal selection of possible projects that can not only reach the best compromise value for the multiple criteria, but also use the best budget availability level according to the multiple decision makers’ preferences. The optimal selection can help the firm make a realistic decision regarding its strategic investment. A comparison study of the fuzzy approach with other approaches shows the advantages of using the fuzzy approach.

Optimal System Design with Multiple Decision Makers and Possible Debt: A Multicriteria De Novo Programming Approach

This paper applies multicriteria de novo programming to formulate and solve problems of system design that involve multiple decision makers and a possible debt. In the framework of the system design model, each involved decision maker has his or her own preference for the budget availability level associated with multicriteria under consideration. If the possible debt occurs in the design time, the model allows flexibility for decision makers to borrow additional money from a bank with a fixed interest rate so as to keep the production process feasible. A contingency plan therefore can be constructed to deal with the debt situation. A solution procedure is developed to design the optimal system with a certain range of budget availability levels. Numerical examples are used to illustrate the procedure.

Using decomposition in large-scale highway network design with a quasi-optimization heuristic

The highway network design problem deals with the selection of links from a base network to facilitate the flow of vehicles from origins to destinations. A proper selection of links requires a balance between minimization of travel costs from origins to destinations and minimization of costs incurred in building or improving links in the network. Link construction costs are usually minimized as a part of the objective or constrained by budget availability. National or regional highway network design problems require excessive amounts of computing time, if solved to optimality. This paper presents a variation of the Modified Quasi-Optimization (MQO) heuristic developed by Dionne and Florian (1979). The proposed algorithm solves a large network design problem by decomposing it in a sequence of smaller problems. Additional savings in computation time are achieved by limiting the search in the MQO heuristic to a well-designed set of paths for each OD pair. These paths are generated to suit the network design problem and differ from the K-shortest paths for the OD pairs. The combined use of decomposition and a limited set of paths allows the proposed heuristic to address realistic network design problems. Numerical experience with a problem involving 6563 nodes and 9800 two-ways links is reported.

Universal Pavement Distress Evaluator Based on Fuzzy Sets

Setting priorities for pavement maintenance and rehabilitation depends on the availability of a universal scale for assessing the condition of every element in the network. The condition of a pavement section has traditionally been assessed by several condition indexes. The present serviceability index (PSI) is one common evaluator used to describe the functional condition with respect to ride quality. Pavement condition index is another index commonly used to describe the extent of distress on a pavement section. During the decision-making process, both classes of indexes are needed to evaluate the overall status of a pavement section in comparison to other sections in the network. Traditionally, regression techniques were used for the development of functions that relate condition indexes to the information recorded in the pavement management database. This approach produces mathematical functions that are limited to a particular database. The functions so developed may also suffer from inaccuracies due to errors in data collection and recording. There is a need for a more generalized approach for the evaluation of pavement conditions to enable efficient management of large transportation networks. The development of a universal measure capable of formally assessing the condition of a pavement section within the universe of pavement conditions is described. This is accomplished by the fusion of a set of fuzzy membership functions that describe different parameters in the database with the perception of each parameter’s significance. The model output is the fuzzy distress index (FDI), which combines the extent of structural distress with traditional performance parameters such as roughness to describe the overall status of the pavement section. The behavior of FDI over time is examined for a random sample of pavement sections and is compared with trends in the corresponding PSI values (PSI was used only because it was readily available in the database). The results indicate that the flexible, universal FDI is a consistent and accurate measure of the overall pavement condition. The set of generated membership functions describing the different extents of every distress type can be easily standardized over the 50 states, allowing the model to be implemented on any pavement at any location. Also, the parameter weights used in the assessment may be easily adjusted (increased or decreased) to reflect changes in maintenance policies or budget availability at the local, state, or national decision-making level. Moreover, the concept allows for the omission of any number of parameters that might not be available in a particular pavement management database.

Multiobjective water resources investment planning under budgetary uncertainty and fuzzy environment

An integration of Stochastic Dynamic Programming (SDP) and Fuzzy Integer Goal Programming (FIGP) modelling framework is proposed to handle problems of multiobjective-multicriteria sequential decision making under budgetary and socio-technical uncertainties inherent in water resources investment planning. In the proposed SDP model probabilities of the funding levels in any time period that are generated using a subjective model are employed to handle budgetary fluctuations. This subjective model consists of historical data as a basic rate, functional relationships among inter-related parameters of the SDP model, scenarios of future budget availability, and subjective inputs elicited from a group of decision makers through a collective opinion technique. Use of the SDP model primarily yields an optimal investment planning policy that recognizes the possibility that actual funding received maybe less than the anticipated and therefore the projects being implemented under the anticipated budget would be interrupted. Economic return of each level of investment decision together with its associated project portfolio is determined by the FIGP model under the environment of imprecise (fuzzy) budget limits and goals, and is then used in the SDP model to establish the sequential optimal policy. This second level optimization model can be used to handle other types of socio-economic uncertainty and multiobjective issues in the overall approach by considering explicitly socio-economic measures and imprecision in the specification of parameters of input data.

Multiobjective water resources investment planning under budgetary and socio-technical uncertainties

An integration of stochastic dynamic programming (SDP) and integer goal programming (IGP) modeling framework is proposed to handle problems of multiobjective-multicriteria sequential decision making under budgetary and socio-technical uncertainties inherent in water resources investment planning. In the proposed SDP model, probabilities of the funding levels in any time period that are generated using a subjective model are employed to handle budgetary fluctuations. This subjective model consists of historical data as a basic rate, functional relationships among inter-related parameters of the SDP model, scenarios of future budget availability, and subjective inputs elicited from a group of decision makers through a collective opinion technique. Application of the SDP model primarily yields an optimal investment planning policy that recognizes the possibility that actual funding received maybe less than that anticipated, and therefore the projects being implemented under the anticipated budget would be interrupted. In this integrated model, economic return of each level of investment decision together with its associated project portfolio is determined by the IGP model based on goals and criteria preferences with explicit consideration to socio-technical uncertainty. >

An Evaluation of Cost of Production Information Usage by County Agents

AbstractCounty agents receive cost of production information primarily from state extension services and then disseminate it to agricultural producers. A survey gathered data on agent usage of this information. A Poisson regression analysis using count data was performed to determine the factors influencing the number of times county agents directly referred to published cost of production (enterprise budget) information in a year. The agent's understanding of budget information use in management decisions, the availability of budgets, and his/her receiving the budgets in multiple forms (e.g., sheets, booklets, or software) had significant positive impacts on the use of budgets by the agent.

Modelling car availability, modal split and trip distribution by Monte-Carlo simulation: A short way to integrated models

The model used a Monte-Carlo algorithm to simulate modal split and trip distribution as an interconnected decision process at the individual level. Using census data, the individuals of a planning region are classified into “situation groups”, which represent significantly different decision situations with respect to socio-demographic position and territorial location. According to the theoretical framework, which emphasises mobility constraints instead of preferences, household structure and sex (time budget and car availability constraints) and work place characteristics (location constraints) are the key variables. For each situation group, “decision profiles” are empirically determined; these describe car availability, travel time constraints and work place distribution. Modal split and trip distribution are simulated as an interconnected individual decision process, which is stochastically determined by the location of the individual and the decision profile of his situation group. The algorithm is very sensitive and flexible and extremely well suited to electronic processing. An extended and modified version is currently being used in the preparation of regional transporation plans for several German city regions.

Goal Programming Model for Water Quality Planning

The broad goals of the Federal Water Pollution Control Act Amendments (PL 92-500) and the regional nature of water quality plans require that multiple objectives be considered in planning. Goal programming is an extension of linear (or integer) programming where the objective is to minimize the deviations from a set of goals, subject to system constraints. The model is applied to an example river basin system where the planning goals are the quality levels of four constituents required for three desired beneficial water used in five stream reaches, and the budget availability in four municipal regions for financing wastewater treatment. The model solution indicates the combination of treatment levels and costs that minimize deviations from user quality and budgetary goals for all stream reaches. Values of deviation variables in the solution indicate the water quality levels achieved. A comparison with the minimum cost solution for meeting stream standards identifies the costs and tradeoffs of achieving higher user quality goals.

Thirteenth annual meeting of the optical society of America

In this paper, a model based on Artificial Neural Network (ANN) has been applied to real estate appraisal. Moreover, an evaluation of ANN performances in estimating the sale price of residential properties has been carried out. Artificial Neural Networks (ANNs) are useful in modelling input-output relationships learning directly from observed data. This capability can be very useful in complex systems like the real estate ones where motivations, tastes and budget availability often do not follow rational behaviours. This study also analyses the impact of such key environmental conditions that represent a problem related to many industrial cities where pollution and landscaping consequences affect the real estate market and residential location choices. We have considered a set of asking price's houses collected in the urban area of Taranto (Italy) where the biggest European steel factory and the 2nd industrial harbour are located.