Project procurement system selection model (with particular reference to Saudi Arabia)

In the last two decades there has been a significant change in the technical and economic conditions prevailing in the construction industry. The traditional methods of procuring projects are now inadequate to cope with these changes. To overcome the shortcomings of the traditional procurement methods, the construction industry has developed a large number of different procurement systems to secure the relationships between contract parties. The proliferation of different procurement systems has created the problem of how to determine the optimum procurement system for any particular project. A systematic approach for selection of the most appropriate system is now needed. Throughout this thesis an investigation has been made of many of the issues related to the successful formulation of a Project Procurement System Selection Model (PPSSM). The knowledge of client needs in the construction industry is essential to the project success. This research has considered the fundamental role played by client needs in the process of procurement selection. The various categories of procurement system that exist in the construction industry are presented in order to address the benefits and drawbacks of each system. A careful investigation of the rules and regulations governing the Saudi construction industry has contributed very significantly to the success of the model building. An examination of the existing models revealed a number of drawbacks which are discussed in detail in this research. A survey was conducted in Saudi Arabia with the aim of testing the PPSSM for effectiveness and efficiency and assisting the governmental agencies to select the most appropriate procurement system to implement their projects. For this purpose, a questionnaire was developed and divided into three major parts: Background information; Data needed to operate PPSSM; and Evaluation of the criteria used for the selection of the procurement system. The results of the first and third parts are generated from 100 responses to the questionnaire mailed to the 110 governmental agencies that represent the population of the study, giving an overall response of (91%). For the second part only thirty senior government agency officers were involved in order to maintain consistency of data needed to operate the PPSSM. The data were analyzed using computer statistical packages: Excel and Statistical Analysis System (SAS). On the basis of the synthesis process of the PPSSM, Saudi public clients have selected "design and build" as the most appropriate procurement system to procure their projects with an overall priority of 0.49. Design and manage, construction management, and management contracting ranked second, third and fourth with priorities of 0.258, 0.140 and 0.106 respectively. The outcome of the study demonstrates the effectiveness of PPSSM in helping the client in the construction industry to choose the right procurement system that best answers his needs and fulfills the project requirements

Considering scale within optimization procedures for water management decisions: Balancing environmental flows and human needs

A key issue in optimization model development is the selection of spatial and temporal scale representing the system. This study proposes a framework for reasoning about scale in this context, drawing on a review of studies applying multi-objective optimization for water management involving environmental flows. We suggest that scale is determined by the management problem, constrained by data availability, computational, and model capabilities. There is therefore an inherent trade-off between problem perception and available modelling capability, which can either be resolved by obtaining data needed or tailoring analysis to the data available. In the interest of fostering transparency in this trade-off process, this paper outlines phases of model development, associated decisions, and available options, and scale implications of each decision. The problem perception phase collects system information about objectives, limiting conditions, and management options. The problem formulation phase collects and uses data, information, and methods about system structure and behaviour.Joseph Guillaume received funding from an Australian Research Council Discovery Early Career Researcher Award (project no. DE190100317). Avril Horne received funding from Australian Research Council Discovery Early Career Researcher Award (project no. DE180100550

Project evaluation : a practical asset pricing method

This paper presents a practical approach to project evaluation using techniques of modern financial economics, with a sample application to oil development under a complex tax system. The method overcomes shortcomings of conventional DCF methods which are either imprecise about the relation between economic value and uncertainty, or are rigid and unrealistic in the required assumptions about how a project's risks (and therefore its value) are influenced by market conditions, the project physical structure, and tax and contract provisions. It is based on the formulation and estimation of an "information model" which represents the resolution over time of uncertainties underlying a project (oil prices in the examples shown). Oil prices are the underlying uncertainty in the examples shown. The project can then be valued using derivative asset valuation, which replicates the consequences of a complex asset by a traded portfolio of simpler assets (in our case, riskless bonds and future claims on oil). For ease of implementation, the method is designed to resemble current industry practice. The information model can be estimated using analysis and judgment similar to that applied in conventional evaluation. The formulation of decision alternatives, the selection of underlying uncertainties, and the design of a cash-flow model are the same as in standard DCF methods. Simulation and valuation results also can be represented in a familiar format. Restrictions must be placed on the "best" current asset pricing theory to achieve this convenient framework: the expected returns on the basic assets, which comprise the portfolios traded to replicate project cash flows, must be assumed to be known with certainty at the time of an evaluation.Supported by the MIT Center for Energy Policy Research, the Social Science and Humanities Research Council of Canada, the Natural Science and Engineering Research Council of Canada, the Imperial Oil University Research Grants Programme, the Central Research Fund, a Nova Faculty Fellowship, the Muir Research Fund and the Institute for Financial Research of the University of Alberta

Modelling in vitro digestion as strategy in developing tailored food for specific consumer population.

In recent years there has been an increasing interest of consumers for food with enhanced nutritional characteristics, spurring the industries to look for unconventional raw materials for novel food design. However, the use of alternative ingredients may result in interesting modifications in the quality as well as in the nutritional characteristics of the final product. Thus, the selection of ingredients is essential, since food formulation directly influences its structural features that in turns, may greatly affect the bioaccessibility, the digestibility and ultimately the bioavailability of some nutrients (e.g., starch and proteins). In this context, the in vitro digestion modelling shows great potential since it can provide useful information about the digestive fate of different food products throughout the human gastrointestinal tract. Considering these recent trends, the primary aims of this PhD research project were: 1) setting up a dynamic in vitro digestion apparatus able to mimic both the physical and the chemical conditions encountered in the human digestive system, thereby improving our understanding about the mechanism of food disintegration, the impact of food structure on nutrients bioaccessibility and the kinetics of nutrients hydrolysis; 2) using static and semi-dynamic in vitro digestion models to understand how food structure can affect nutrients bioaccessibility and digestibility. For this purpose, cereal-based products (pasta and bread) were used as model foods and the use of lentil flour as highly nutritious ingredient has been investigated

Petroleum Refinery Planning Under Uncertainty: A Multiobjective Optimization Approach with Economic and Operational Risk Management

record high of USD 147 per barrel according to the NYMEX exchange on June 2008. It is forecast to spiral upwards (withthe current graph trend) to a muchhigher price level. The current situation of fluctuating high petroleum crude oil prices is affecting the markets and industries worldwide by the uncertainty and volatility of the petroleum industry. As oil refining is the downstream of the petroleum industry, it is increasingly important for refineries to operate at an optimal level in the presence of volatility of crude oil prices. Downstream refineries must assess the potential impact that may affect its optimal profit margin byconsidering the costs of purchasing the raw material of crude oils and prices of saleable intermediates and products as well as production yields. With optimization, refinery will be able to operate at optimal condition. In this work, we have attempted to solve model formulation concerning the petroleum refinery planning under uncertainty. We use stochastic programming optimization incorporating the weighted sum method as well as the epsilon constraint method to solve the model formulation ofthe petroleum refinery planning under uncertainty. The objective of this research project is to formulate a deterministic model followed by a two stage stochastic programming model with recourse problem for a petroleum refinery planning. The two stage stochastic risk model is then reformulated using MeanAbsolute Deviation as the risk measure. After formulating the stochastic model using Mean Absolute Deviation, the problem is then investigated using the Pareto front solution of efficient frontier of the resulting multiobjective optimization problem by using the Weighted SumMethod as well as the e-constraint method in order to obtain the Pareto Optimal Curve which generates a wide selection of optimization solutions for our problem. The implementation of the multiobjective optimization problem is then automated to report the model solution by capturing the solution values using theGAMS looping system. Notethat some of the major parameters used throughout the formulated stochastic programming model include prices of the raw material crude oil and saleable products, market demands for products, andproduction yields. The main contribution on this work in die first part is to conduct a further study/research onthe implementation of the model formulation in Khor et al. (2008) where the model formulated by Khoret al. (2008) uses variance as the risk measure. The results obtain in the previous paper will be compared with the method in this paper that incorporates Mean Absolute Deviation as the risk measure. To further study the model formulated, the solution obtain is further enhanced using the Weighted Sum Method as well as the Epsilon constraint method to obtain the Pareto Optimal Curve generation. Hence, most of the exposition on the model formulation and solution algorithms are taken directly from the original paper so as to provide the readers with the most accurate information possible. v

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California