Developing collaborative planning support tools for optimised farming in Western Australia

Land-use (farm) planning is a highly complex and dynamic process. A land-use plan can be optimal at one point in time, but its currency can change quickly due to the dynamic nature of the variables driving the land-use decision-making process. These include external drivers such as weather and produce markets, that also interact with the biophysical interactions and management activities of crop production.The active environment of an annual farm planning process can be envisioned as being cone-like. At the beginning of the sowing year, the number of options open to the manager is huge, although uncertainty is high due to the inability to foresee future weather and market conditions. As the production year reveals itself, the uncertainties around weather and markets become more certain, as does the impact of weather and management activities on future production levels. This restricts the number of alternative management options available to the farm manager. Moreover, every decision made, such as crop type sown in a paddock, will constrains the range of management activities possible in that paddock for the rest of the growing season.This research has developed a prototype Land-use Decision Support System (LUDSS) to aid farm managers in their tactical farm management decision making. The prototype applies an innovative approach that mimics the way in which a farm manager and/or consultant would search for optimal solutions at a whole-farm level. This model captured the range of possible management activities available to the manager and the impact that both external (to the farm) and internal drivers have on crop production and the environment. It also captured the risk and uncertainty found in the decision space.The developed prototype is based on a Multiple Objective Decision-making (MODM) - á Posteriori approach incorporating an Exhaustive Search method. The objective set used for the model is: maximising profit and minimising environmental impact. Pareto optimisation theory was chosen as the method to select the optimal solution and a Monte Carlo simulator is integrated into the prototype to incorporate the dynamic nature of the farm decision making process. The prototype has a user-friendly front and back end to allow farmers to input data, drive the application and extract information easily

A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT

Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. Results: Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for optimizations. The MOEA improvements were evaluated for example prostate cases with one target and two OARs. The modified MOEA dominated 11.3% of plans using a standard genetic algorithm package. By implementing domination advantage and protocol objectives, small diverse populations of clinically acceptable plans that were only dominated 0.2% by the Pareto front could be generated in a fraction of an hour. Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. It optimizes not only beamlet intensities but also objective function parameters on a patient-specific basis

Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment : A fuzzy multi-criteria genetic algorithm approach

Abstract: Optimizing system reliability in a fuzzy environment is complex due to the presence of imprecise multiple decision criteria such as maximizing system reliability and minimizing system cost. This calls for multi-criteria decision making approaches that incorporate fuzzy set theory concepts and heuristic methods. This paper presents a fuzzy multi-criteria nonlinear model, and proposes a fuzzy multi-criteria genetic algorithm (FMGA) for complex bridge system reliability design in a fuzzy environment. The algorithm uses fuzzy multi-criteria evaluation techniques to handle fuzzy goals, preferences, and constraints. The evaluation approach incorporates fuzzy preferences and expert choices of the decision maker in regards to cost and reliability goals. Fuzzy evaluation gives the algorithm flexibility and adaptability, yielding near-optimal solutions within short computation times. Results from computational experiments based on benchmark problems demonstrate that the FMGA approach is a more reliable and effective approach than best known algorithm, especially in a fuzzy multi-criteria environment

A Preference-guided Multiobjective Evolutionary Algorithm based on Decomposition

Multiobjective evolutionary algorithms based on decomposition (MOEA/Ds) represent a class of widely employed problem solvers for multicriteria optimization problems. In this work we investigate the adaptation of these methods for incorporating preference information prior to the optimization, so that the search process can be biased towards a Pareto-optimal region that better satisfies the aspirations of a decision-making entity. The incorporation of the Preference-based Adaptive Region-of-interest (PAR) framework into the MOEA/D requires only the modification of the reference points used within the scalarization function, which in principle allows a straightforward use in more sophisticated versions of the base algorithm. Experimental results using the UF benchmark set suggest gains in diversity within the region of interest, without significant losses in convergence

Using Optimality Theory and Reference Points to Improve the Diversity and Convergence of a Fuzzy-Adaptive Multi-Objective Particle Swarm Optimizer

Particle Swarm Optimization (PSO) has received increasing attention from the evolutionary optimization research community in the last twenty years. PSO is a metaheuristic approach based on collective intelligence obtained by emulating the swarming behavior of bees. A number of multi-objective variants of the original PSO algorithm that extend its applicability to optimization problems with conflicting objectives have also been developed; these multi-objective PSO (MOPSO) algorithms demonstrate comparable performance to other state-of-the-art metaheuristics. The existence of multiple optimal solutions (Pareto-optimal set) in optimization problems with conflicting objectives is not the only challenge posed to an optimizer, as the latter needs to be able to identify and preserve a well-distributed set of solutions during the search of the decision variable space. Recent attempts by evolutionary optimization researchers to incorporate mathematical convergence conditions into genetic algorithm optimizers have led to the derivation of a point-wise proximity measure, which is based on the solution of the achievement scalarizing function (ASF) optimization problem with a complementary slackness condition that quantifies the violation of the Karush-Kuhn-Tucker necessary conditions of optimality. In this work, the aforementioned KKT proximity measure is incorporated into the original Adaptive Coevolutionary Multi-Objective Swarm Optimizer (ACMOPSO) in order to monitor the convergence of the sub-swarms towards the Pareto-optimal front and provide feedback to Mamdani-type fuzzy logic controllers (FLCs) that are utilized for online adaptation of the algorithmic parameters. The proposed Fuzzy-Adaptive Multi-Objective Optimization Algorithm with the KKT proximity measure (FAMOPSOkkt) utilizes a set of reference points to cluster the computed nondominated solutions. These clusters interact with their corresponding sub-swarms to provide the swarm leaders and are also utilized to manage the external archive of nondominated solutions. The performance of the proposed algorithm is evaluated on benchmark problems chosen from the multi-objective optimization literature and compared to the performance of state-of-the-art multi-objective optimization algorithms with similar features

A RISK-INFORMED DECISION-MAKING METHODOLOGY TO IMPROVE LIQUID ROCKET ENGINE PROGRAM TRADEOFFS

This work provides a risk-informed decision-making methodology to improve liquid rocket engine program tradeoffs with the conflicting areas of concern affordability, reliability, and initial operational capability (IOC) by taking into account psychological and economic theories in combination with reliability engineering. Technical program risks are associated with the number of predicted failures of the test-analyze-and-fix (TAAF) cycle that is based on the maturity of the engine components. Financial and schedule program risks are associated with the epistemic uncertainty of the models that determine the measures of effectiveness in the three areas of concern. The affordability and IOC models' inputs reflect non-technical and technical factors such as team experience, design scope, technology readiness level, and manufacturing readiness level. The reliability model introduces the Reliability- As-an-Independent-Variable (RAIV) strategy that aggregates fictitious or actual hotfire tests of testing profiles that differ from the actual mission profile to estimate the system reliability. The main RAIV strategy inputs are the physical or functional architecture of the system, the principal test plan strategy, a stated reliability-bycredibility requirement, and the failure mechanisms that define the reliable life of the system components. The results of the RAIV strategy, which are the number of hardware sets and number of hot-fire tests, are used as inputs to the affordability and the IOC models. Satisficing within each tradeoff is attained by maximizing the weighted sum of the normalized areas of concern subject to constraints that are based on the decision-maker's targets and uncertainty about the affordability, reliability, and IOC using genetic algorithms. In the planning stage of an engine program, the decision variables of the genetic algorithm correspond to fictitious hot-fire tests that include TAAF cycle failures. In the program execution stage, the RAIV strategy is used as reliability growth planning, tracking, and projection model. The main contributions of this work are the development of a comprehensible and consistent risk-informed tradeoff framework, the RAIV strategy that links affordability and reliability, a strategy to define an industry or government standard or guideline for liquid rocket engine hot-fire test plans, and an alternative to the U.S. Crow/AMSAA reliability growth model applying the RAIV strategy

VISTA: a visual interactive method for solving MCDM problems

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 1994.Thesis (Master's) -- Bilkent University, 1994.Includes bibliographical references leaves 91-94.In this thesis, recognizing the need of interaction with DM (Decision Maker) in solving MCDM (Multiple Criteria Decision Making) problems, a practical interactive algorithm called VISTA (Visual Interactive Sequential Tradeoffs Algorithm) is developed, and a DSS (Decision Support System) is designed to assist DM to use judgement effectively. The algorithm operates by successively optimizing a chosen objective function while the remaining objectives are converted to constraining objectives by setting their satisficing values, one of which is parametrically varied. By plotting the maximum value of the main objective function versus the parameter varied, a tradeoff curve is constructed between the optimized and the parametrized objective, while assuring constraining objectives (satisficing values guaranteed). This tradeoff curve is presented to the DM, and the DM is asked to choose a compromise solution between these two objectives. This chosen point is used as the new satisficing value of the parametrized objective, and a new tradeoff curve is generated by parametrizing another constraining objective function’s right hand side and .so on. This interactive procedure is continued until the DM is satisfied with the current decision or some other termination criterion is met. Special features to facilitate the DM’s judgement (MRS (Marginal Rate of Substitution) Curve, Multiple Comparison Plots, Convergence Plots), and the start and the termination (Start, Terminate, a Hybrid Approach) of the algorithm are provided. Two example problems are worked out with VISTA to demonstrate the practicality of the algorithm. The model and the entire procedure are validated.Tabanoğlu, AslıhanM.S