On the optimal resource allocation in projects considering the time value of money

The optimal resource allocation in stochastic activity networks had been previously developed by applying three different approaches: Dynamic Programming (DP), an Electromagnetism Algorithm (EMA) and an Evolutionary Algorithm (EVA). This paper presents an extension to the initial problem considering the value of money over time. This extended problem was implemented using the Java programming language, an Object Oriented Language, following the approaches previously used (DP, EMA and EVA).Fundação para a Ciência e a Tecnologia (FCT

Feasibility analysis of using special purpose machines for drilling-related operations

This work focuses on special purpose machine tools (SPMs), providing a modular platform for performing drilling-related operations. One of the main challenges in using SPMs is selecting the most appropriate machine tool among many alternatives. This thesis introduces a feasibility analysis procedure developed to support decision-making through the assessment of the strengths and limitations of SPMs. To achieve this, technical and economic feasibility analyses, a sensitivity analysis, and an optimisation model were developed and a case study was provided for each analysis. The results indicated that although technical feasibility analysis leads decision-makers to select a feasible machine tool, complementary analyses are required for making an informed decision and improving profitability. Accordingly, a mathematical cost model was developed to perform economic and sensitivity analyses and investigate the profitability of any selected SPM configuration. In addition, an optimisation procedure was applied to the cost model in order to investigate the effect of process parameters and the SPM configuration on the decision-making. Finally, the developed analyses were then integrated into a model in a proper sequence that can evaluate whether the SPM is appropriate for producing the given part and achieving higher productivity. To validate this integrated model three different case studies were presented and results were discussed. The results showed that the developed model is a very useful tool in assisting manufacturers to evaluate the performance of SPMs in comparison with other alternatives considered from different perspectives

Ecological and Economic Effects of Applying the Future Agricultural Production Structure Model (FAPSMS): The Case Study of the Barička River Basin Sustainability

It is necessary to harmonize the needs of society in terms of agricultural production and land protection from various forms of degradation throughout sustainable land management. Assessing the justification of investment in sustainable management of land resources is an important step in this process. Consequently, an analysis of soil erosion risk was carried out in the suburban area of the morphological unit of the Barička river watershed, using the Revised Universal Soil Loss Equation (RUSLE) method, with the existing and projected structure of agricultural production according to the Future Agricultural Production Structure Model from the Aspect of Preserving Land Resources for Mountain Catchment Areas of Serbia (FAPSMS). The value of the existing and projected production structure from an economic aspect was also examined using dynamic economic methods. In order to assess the risk and uncertainty of investments, a sensitive analysis of dynamic methods was carried out. The results show that soil erosion losses are already below tolerance values with the existing production structure and that they could be reduced even more by applying the designed structure. Economic indicators show that the investment is justified and that it is more sensitive to changes in income

Investment decisions and sensitivity analysis: NPV-consistency of rates of return

Investment decisions may be evaluated via several different metrics/criteria, which are functions of a vector of value drivers. The economic significance and the reliability of a metric depend on its compatibility with the Net Present Value (NPV). Traditionally, a metric is said to be NPV-consistent if it is coherent with NPV in signalling value creation. This paper makes use of Sensitivity Analysis (SA) for measuring coherence between rates of return and NPV. In particular, it introduces a new, stronger definition of NPV-consistency that takes into account the influence of value drivers on the metric output. A metric is strongly NPV-consistent if it signals value creation and the ranking of the value drivers in terms of impact on the output is the same as that provided by the NPV. The degree of (in)coherence is calculated with Spearman's (1904) correlation coefficient and Iman and Conover's (1987) top-down coefficient. We focus on the class of AIRRs (Magni 2010, 2013) and show that the average Return On Investment (ROI) enjoys strong NPV-consistency under several (possibly all) methods of Sensitivity Analysis

Dilute Sulfuric Acid Pretreatment of Switchgrass in Microwave Reactor for Biofuel Conversion: An Investigation of Yields, Kinetics,

Lignocellulosic materials provide a raw material source for biofuel conversion and offer several advantages over fossil fuels- usage of a renewable resource, reduced greenhouse emissions, a decreased dependence on foreign oil, and stimulation of the agricultural sector. However, a primary technological challenge in converting lignocellulosic biomass into fuel is overcoming the recalcitrance of its matrix to enzymatic hydrolysis. To overcome these problems for chemical processing, naturally occurring cellulose biomass must be pretreated before it can be further processed using enzymatic hydrolysis or bioconversion. The goal of this work was to develop a model that predicts the glucose yield (pretreatment and enzymatic digestibility) of dilute acid pretreated switchgrass as a function of pretreatment process conditions (acid loading, 0-1.5 vol%, temperature, 165-195oC, and residence time, 1-10 min). This project was the first study that used a multivariable design experimental series to directly compare the pretreatment effectiveness (product yield, biomass composition and appearance, pH, etc) of using conventional and microwave heated reactors. Microwave-pretreated switchgrass afforded up to a 100% higher total glucose yield (combined pretreatment and enzymatic-hydrolysis liquor yields) at equivalent pretreatment severity and at one tenth of the reaction time, relative to conventional pretreatment. Under best pretreatment conditions of 0.75 vol% acid, 195oC, 1 min residence time, 99% glucose yield and 99% hemicellulose removal were achieved. Kinetic parameters were estimated for the cellulose and xylan hydrolysis reactions in the pretreatment liquor and the solid residue. The kinetic model gave an average correlation coefficient of 0.93 for all reactions. In addition, the combined severity factors (CSF) were also determined for each experiment. Highest observed enzymatic glucose yield corresponded to a CSF of 1.7. A mass and energy balance, and economic analysis based on production scale was developed for both reactor systems. The microwave pretreatment process theoretically yielded 48% more ethanol relative to the conventional process. For microwave pretreatment to be commercially viable, two criteria must be met. One, the cost for largescale continuous microwave reactors would need to be significantly lower than current estimates. And second, higher solids content must be used (\u3e20 wt% in the slurry) to maximize output

Hybrid Fuzzy-Bayesian Dynamic Decision Support Tool for Resource-Based Scheduling of Construction Projects

Title from PDF of title page viewed September 7, 2017Dissertation advisor: Ceki HalmenVitaIncludes bibliographical references (pages 153-165)Thesis (Ph.D.)--School of Computing and Engineering and Bloch School of Management. University of Missouri--Kansas City, 2017This dissertation proposes a flexible and intelligent decision support tool for scheduling and resource allocation of construction projects. A hybrid Fuzzy-Bayesian scheduling network and a new optimization model and solution approach have been developed to assess the combinatory effect of different risk factors on scheduling and optimize the time-cost tradeoff. Developed decision support tool employs interval-valued fuzzy numbers and Bayesian networks to dynamically quantify uncertainty and predict project performance during its make span. Using interval-valued fuzzy numbers makes the model more flexible and intelligent comparing to conventional fuzzy risk assessment models through incorporating the decision makers` confidence degree. The linguistic assessments of experts regarding the likelihood and severity of increase or decrease in task duration and cost when influenced by different risk factors are used to generate a set of duration and cost prior-probability distributions. A learning dynamic Bayesian scheduling network is developed to probabilistically combine the prior-probability distributions with initial activity duration estimates and update them as new evidence in form of actual activity data feed into the network. This model also predicts project performance at any point of time during its execution. Optimization model explicitly considers variation of time-cost tradeoff relationship during project execution and complex payment terms to maximize the project net present value (NPV). A sequential solution approach is proposed to combine a procedure for updating time-cost tradeoff data, and mixed integer linear programming (MILP) methods to obtain optimal project crashing and scheduling solutions that is adaptive to the current project status and crew productivity. Capability of proposed model in quantifying uncertainty at initial phases of project where project performance data are scarce, learning from data and predicting project performance, considering financial aspects of scheduling through optimal resource allocation and providing useful and clear advice to managers are advantages of developed decision support tool over already existing approaches.Introduction -- Literature review -- Methodology -- Case study and model validation -- Conclusion and recommendations -- Appendix. Detailed Fuzzy Weighted Average Calculations for a-cut = 0 Based on the Max-Min Paired Elimination Algorit

Maximizing the net present value of a project under uncertainty

We address the maximization of a project’s expected net present value when the activity durations and cash flows are described by a discrete set of alternative scenarios with associated occurrence probabilities. In this setting, the choice of scenario-independent activity start times frequently leads to infeasible schedules or severe losses in revenues. We suggest to determine an optimal target processing time policy for the project activities instead. Such a policy prescribes an activity to be started as early as possible in the realized scenario, but never before its (scenario-independent) target processing time. We formulate the resulting model as a global optimization problem and present a branch-and-bound algorithm for its solution. Extensive numerical results illustrate the suitability of the proposed policy class and the runtime behavior of the algorithm

Maximizing the net present value of a project under uncertainty

We address the maximization of a project's expected net present value when the activity durations and cash flows are described by a discrete set of alternative scenarios with associated occurrence probabilities. In this setting, the choice of scenario-independent activity start times frequently leads to infeasible schedules or severe losses in revenues. We suggest to determine an optimal target processing time policy for the project activities instead. Such a policy prescribes an activity to be started as early as possible in the realized scenario, but never before its (scenario-independent) target processing time. We formulate the resulting model as a global optimization problem and present a branch-and-bound algorithm for its solution. Extensive numerical results illustrate the suitability of the proposed policy class and the runtime behavior of the algorithm.Project scheduling Net present value Optimization under uncertainty

Maximizing the net present value of a project under uncertainty: The value of activity delays

status: publishe

Maximizing the net present value of a project under uncertainty: The value of activity delays

status: publishe