Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling

In decision theory, the weighted sum model (WSM) is the best known Multi-Criteria Decision Analysis (MCDA) approach for evaluating a number of alternatives in terms of a number of decision criteria. Assigning weights is a difficult task, especially if the number of criteria is large and the criteria are very different in character. There are some problems in the real world which utilize conflicting criteria and mutual effect. In the field of automotive, the knocking phenomenon in internal combustion or spark ignition engines limits the efficiency of the engine. Power and fuel economy can be maximized by optimizing some factors that affect the knocking phenomenon, such as temperature, throttle position sensor, spark ignition timing, and revolution per minute. Detecting knocks and controlling the above factors or criteria may allow the engine to run at the best power and fuel economy. The best decision must arise from selecting the optimum trade-off within the above criteria. The main objective of this study was to proposed a new Non-Weighted Aggregate Evaluation Function (NWAEF) model for non-linear multi-objectives function which will simulate the engine knock behavior (non-linear dependent variable) in order to optimize non-linear decision factors (non-linear independent variables). This study has focused on the construction of a NWAEF model by using a curve fitting technique and partial derivatives. It also aims to optimize the nonlinear nature of the factors by using Genetic Algorithm (GA) as well as investigate the behavior of such function. This study assumes that a partial and mutual influence between factors is required before such factors can be optimized. The Akaike Information Criterion (AIC) is used to balance the complexity of the model and the data loss, which can help assess the range of the tested models and choose the best ones. Some statistical tools are also used in this thesis to assess and identify the most powerful explanation in the model. The first derivative is used to simplify the form of evaluation function. The NWAEF model was compared to Random Weights Genetic Algorithm (RWGA) model by using five data sets taken from different internal combustion engines. There was a relatively large variation in elapsed time to get to the best solution between the two model. Experimental results in application aspect (Internal combustion engines) show that the new model participates in decreasing the elapsed time. This research provides a form of knock control within the subspace that can enhance the efficiency and performance of the engine, improve fuel economy, and reduce regulated emissions and pollution. Combined with new concepts in the engine design, this model can be used for improving the control strategies and providing accurate information to the Engine Control Unit (ECU), which will control the knock faster and ensure the perfect condition of the engine

Interactive Decision Analysis in Energy Planning and Policy Assessment

In recent years, there has been considerable fruitful collaboration between the System and Decision Sciences (SDS) Program and the Energy Project at IIASA. This paper gives an overview of this joint work, which involves the use of methodological tools developed in SDS to analyze decision situations based on models constructed in the Energy Project. The paper starts with a study of the use of the earliest version of DIDASS in conjunction with the energy supply model MESSAGE. It then describes how construction of more advanced energy models such as MESSAGE II, SEMA (an Austrian energy model), and GATE (a model of gas trade in Europe) took place in parallel with the development of an interactive multiple-criteria LP-solver (IMM), which represents a first step towards the integration of modeling and optimization processes in the analysis of complex decision situations. We hope that such collaboration will continue to provide a driving force for advances in different areas of IIASA research

A multiple objective optimization approach to the decommissioning and dismantling of a nuclear power plant.

The complexity, relevance and critical nature of the decommissioning of nuclear power plants (NPP) are of great significance in today\u27s society. Following the catastrophe in Fukushima a shift in the general public\u27s perception of NPP took place throughout the world and in Europe in particular. In this dissertation interdisciplinary methods will be discussed to identify solutions which take into account the technological complexity and organizational issues involved in the dismantling and decommissioning process of NPP. Operations research, lean management, simultaneous engineering, cost analysis, multiple-objective optimization, project management, software tools are powerful concepts and methodologies when undertaking the dismantling and decommissioning process of NPP. Besides the presentation of a wide range of terminological and methodological definitions and technical terms based on the Literature Review, in the dissertation a framework for model development of a Multiple objective optimization problem (MOOP) will discussed focusing on empirical data from a virtual NPP. The theoretical foundation of the framework is at the intersection of two successful approaches used to describe and accomplish similar complex challenges, and the integration of state-of-the-art process approaches such as lean management. The procedural conception of the model is mainly leant on the OMEGA model (International Atomic Energy Agency (IAEA) (2008)). Mathematically the model is derived from Jones et. al. (1998). Finally the application of the model using different software tools (AIMMS, MATLAB, R and SPSS) will be presented. In conclusion the work will be put into a position to venture a critical outlook and discussion for the future of the decommissioning and dismantling processes of NPP. The main goal of this dissertation is to define the requirements for the optimization of three objectives: Minimizing the total project cost, reducing the safety hazard (risk) and managing project duration. Also a description of how the programming language R and the AIMMS program interfaces with the OMEGA application and how R will be used to solve the MOOP will be given. The software Microsoft Project will be leveraged in order to model this objective

Advances and applications in high-dimensional heuristic optimization

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or a high number of objectives, effectively limiting the range of scenarios to which they may be applied. This research introduces mechanisms to improve the runtime complexity of many multiobjective evolutionary algorithms, achieving state-of-the-art performance, as compared to many prominent methods from the literature. Further, the investigations here presented demonstrate the capability of multiobjective evolutionary algorithms in a complex, large-scale optimization scenario. Showcasing the approach’s ability to intelligently generate well-performing solutions to a meaningful optimization problem. These investigations advance the concept of multiobjective evolutionary algorithms by addressing a key limitation and demonstrating their efficacy in a challenging real-world scenario. Through enhanced computational efficiency and exhibited specialized application, the utility of this powerful heuristic strategy is made more robust and evident”--Abstract, page iv

Integrated water resources management under uncertainty: exploring interconnected technological, infrastructural and institutional solutions

2018 Summer.Includes bibliographical references.Rapidly growing populations in many of the world's semiarid regions intensify competition for increasingly scarce freshwater resources. Growing urban demands, land-use change, and a changing climate will further exacerbate regional vulnerability to water scarcity. The intensification of these trends creates several challenges for the future planning and management of water resources. In this work we employ the use of an integrated socioeconomic, hydrologic, and ecological modeling framework to quantify the effects of water rights allocation on a representative semiarid river basin. Through this framework we analyze the tradeoffs of several water management practices, institutional settings, and regional policies on municipal and agricultural sectors. Generally, the agent-based adoption of water management strategies can alleviate the harm of water scarcity while providing positive feedbacks to reducing municipal costs and increasing agricultural profit from production. Household adoption of xeriscaping is considered the most important technology to lower urban demands and offset the negative externalities of rural-to-urban water transfers. Additionally, an uninhibited water market leads to the most effective allocation of water rights, providing benefits to both rural and municipal communities. The future allocation of water rights under climatic, institutional, agricultural, and technological uncertainty shows significant sensitivity to fluctuations in water conveyance infrastructure costs. Such changes in infrastructure costs (i.e. 50% to 150%) can nearly double the expected costs of reliably supplying water to urban households. However, urban water supply planners can incentivize the adoption of water management practices to stabilize these costs. Further, required water purchases for land developers set by urban planners can be used as a key policy tool for keeping costs low. This work contributes to existing literature in integrated water resources management to help understand the effects of water scarcity and provide practical solutions for urban water planners in rapidly urbanizing semiarid regions

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

OPTIMIZATION OF RAILWAY TRANSPORTATION HAZMATS AND REGULAR COMMODITIES

Transportation of dangerous goods has been receiving more attention in the realm of academic and scientific research during the last few decades as countries have been increasingly becoming industrialized throughout the world, thereby making Hazmats an integral part of our life style. However, the number of scholarly articles in this field is not as many as those of other areas in SCM. Considering the low-probability-and-high-consequence (LPHC) essence of transportation of Hazmats, on the one hand, and immense volume of shipments accounting for more than hundred tons in North America and Europe, on the other, we can safely state that the number of scholarly articles and dissertations have not been proportional to the significance of the subject of interest. On this ground, we conducted our research to contribute towards further developing the domain of Hazmats transportation, and sustainable supply chain management (SSCM), in general terms. Transportation of Hazmats, from logistical standpoint, may include all modes of transport via air, marine, road and rail, as well as intermodal transportation systems. Although road shipment is predominant in most of the literature, railway transportation of Hazmats has proven to be a potentially significant means of transporting dangerous goods with respect to both economies of scale and risk of transportation; these factors, have not just given rise to more thoroughly investigation of intermodal transportation of Hazmats using road and rail networks, but has encouraged the competition between rail and road companies which may indeed have some inherent advantages compared to the other medium due to their infrastructural and technological backgrounds. Truck shipment has ostensibly proven to be providing more flexibility; trains, per contra, provide more reliability in terms of transport risk for conveying Hazmats in bulks. In this thesis, in consonance with the aforementioned motivation, we provide an introduction into the hazardous commodities shipment through rail network in the first chapter of the thesis. Providing relevant statistics on the volume of Hazmat goods, number of accidents, rate of incidents, and rate of fatalities and injuries due to the incidents involving Hazmats, will shed light onto the significance of the topic under study. As well, we review the most pertinent articles while putting more emphasis on the state-of-the-art papers, in chapter two. Following the discussion in chapter 3 and looking at the problem from carrier company’s perspective, a mixed integer quadratically constraint problem (MIQCP) is developed which seeks for the minimization of transportation cost under a set of constraints including those associating with Hazmats. Due to the complexity of the problem, the risk function has been piecewise linearized using a set of auxiliary variables, thereby resulting in an MIP problem. Further, considering the interests of both carrier companies and regulatory agencies, which are minimization of cost and risk, respectively, a multiobjective MINLP model is developed, which has been reduced to an MILP through piecewise linearization of the risk term in the objective function. For both single-objective and multiobjective formulations, model variants with bifurcated and nonbifurcated flows have been presented. Then, in chapter 4, we carry out experiments considering two main cases where the first case presents smaller instances of the problem and the second case focuses on a larger instance of the problem. Eventually, in chapter five, we conclude the dissertation with a summary of the overall discussion as well as presenting some comments on avenues of future work

Adaptive flood risk management under climate change uncertainty using real options and optimization

This is the peer reviewed version of the following article: oodward, M., Kapelan, Z. and Gouldby, B. (2014), Adaptive Flood Risk Management Under Climate Change Uncertainty Using Real Options and Optimization. Risk Analysis, 34: 75–92, which has been published in final form at 10.1111/risa.12088. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving: http://olabout.wiley.com/WileyCDA/Section/id-820227.html#termsIt is well recognized that adaptive and flexible flood risk strategies are required to account for future uncertainties. Development of such strategies is, however, a challenge. Climate change alone is a significant complication, but, in addition, complexities exist trying to identify the most appropriate set of mitigation measures, or interventions. There are a range of economic and environmental performance measures that require consideration, and the spatial and temporal aspects of evaluating the performance of these is complex. All these elements pose severe difficulties to decisionmakers. This article describes a decision support methodology that has the capability to assess the most appropriate set of interventions to make in a flood system and the opportune time to make these interventions, given the future uncertainties. The flood risk strategies have been explicitly designed to allow for flexible adaptive measures by capturing the concepts of real options and multiobjective optimization to evaluate potential flood risk management opportunities. A state-of-the-art flood risk analysis tool is employed to evaluate the risk associated to each strategy over future points in time and a multiobjective genetic algorithm is utilized to search for the optimal adaptive strategies. The modeling system has been applied to a reach on the Thames Estuary (London, England), and initial results show the inclusion of flexibility is advantageous, while the outputs provide decisionmakers with supplementary knowledge that previously has not been considered.Engineering and Physical Sciences Research Council (EPSRC)Department of Environment, Food and Rural Affairs/Environment Agency Joint Research Programme on Flood and Coastal DefenceUnited Kingdom Water Industry ResearchOffice of Public Works DublinNorthern Ireland Rivers Agenc

Application of Multi-Objective Optimization Techniques to Geotechnical Engineering Problems

This research work has its motivation in the ever-increasing use of computational methods in the areas of Civil Engineering. Parameter estimation has assumed a critical importance in predicting failure curves more accurately. Error-in-variables approach gives us a chance to predict simultaneously dependent and independent variables. A method like least square can take into account the error in only ‘x’ values and does not consider the error in values of ‘y’. The vector of unknown parameters ( , , ∅) can also be estimated by the EIV approach along with the variable data points. The failure criterion used is the MSDPu rock failure criterion which deals with failure of low porosity rocks and represents a multi-axial surface in stress space. The objective functions are modelled as a multi-objective optimization problem with the first function accounting for the error due to variables and the second function accounting for the error due to the model. Although, the optimization problem has increased dimension in case of EIV approach, it provides an efficient tool to predict the set of reconciled data and unknown parameters. NSGA-II is an efficient MOEAs developed by Deb et al. (2001) for multi-objective optimization which follows the principle of a fast elitist non-dominated sorting procedure. The two error functions hence formulated by the EIV method is efficiently minimized by the evolutionary algorithm with a little bit of parametric tuning. Estimating pile length for piles is quite difficult, and requires a good knowledge of the subsoil conditions. If the required conditions are formulated into objective functions along with constraint handling then optimized function of (d/L) against load bearing capacity can be found out by NSGA-II