A fuzzy constraint satisfaction approach to achieving stability in dynamic constraint satisfaction problems.

by Wong, Yin Pong Anthony.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 101-107).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Constraint Satisfaction Problems --- p.2Chapter 1.2 --- Solution Stability in Dynamic Constraint Satisfaction Problems --- p.3Chapter 1.3 --- Motivation of the Research --- p.5Chapter 1.4 --- Overview of the Thesis --- p.5Chapter 2 --- Related Work --- p.7Chapter 2.1 --- Complete Search Algorithms --- p.7Chapter 2.1.1 --- DnAC-4 --- p.8Chapter 2.1.2 --- ac --- p.9Chapter 2.1.3 --- DnAC-6 --- p.9Chapter 2.2 --- Algorithms for Stability --- p.10Chapter 2.2.1 --- Bellicha --- p.10Chapter 2.2.2 --- Dynamic Dynamic Backtracking --- p.11Chapter 2.2.3 --- Wallace and Freuder --- p.12Chapter 2.2.4 --- Unimodular Probing --- p.13Chapter 2.2.5 --- Train Rescheduling --- p.14Chapter 2.3 --- Constrained Optimization Algorithms --- p.14Chapter 2.3.1 --- Guided Local Search --- p.14Chapter 2.3.2 --- Anytime CSA with Iterative Deepening --- p.15Chapter 2.4 --- A Real-life Application --- p.16Chapter 3 --- Background --- p.17Chapter 3.1 --- Fuzzy Constraint Satisfaction Problems --- p.17Chapter 3.2 --- Fuzzy GENET --- p.19Chapter 3.2.1 --- Network Architecture --- p.19Chapter 3.2.2 --- Convergence Procedure --- p.21Chapter 3.3 --- Deficiency in Fuzzy GENET --- p.24Chapter 3.4 --- Rectification of Fuzzy GENET --- p.26Chapter 4 --- Using Fuzzy GENET for Solving Stability Problems --- p.30Chapter 4.1 --- Modelling Stability Problems as FCSPs --- p.30Chapter 4.2 --- Extending Fuzzy GENET for Solving Stability Problems --- p.36Chapter 4.3 --- Experiments --- p.38Chapter 4.3.1 --- Dynamic CSP Generation --- p.39Chapter 4.3.2 --- Problems Using Hamming Distance Function --- p.41Chapter 4.3.2.1 --- Variation in Number of Variables --- p.42Chapter 4.3.2.2 --- Variation in Domain Size --- p.45Chapter 4.3.2.3 --- Variation in Density and Tightness --- p.47Chapter 4.3.3 --- Comparison in Using Different Thresholds --- p.47Chapter 4.3.4 --- Problems Using Manhattan Distance Function --- p.50Chapter 5 --- Enhancement of the Modelling Scheme --- p.56Chapter 5.1 --- Distance Bound --- p.56Chapter 5.2 --- Enhancement of Convergence Procedure --- p.57Chapter 5.3 --- Comparison with Optimal Solutions --- p.60Chapter 5.4 --- Comparison with Fuzzy GENET(dcsp) --- p.64Chapter 5.4.1 --- Medium-sized Problems --- p.64Chapter 5.4.2 --- The 150-10-15-15 Problem --- p.67Chapter 5.4.3 --- Variation in Density and Tightness --- p.73Chapter 5.4.4 --- Variation in Domain Size --- p.76Chapter 5.5 --- Analysis of Fuzzy GENET(dcsp2) --- p.94Chapter 6 --- Conclusion --- p.98Chapter 6.1 --- Contributions --- p.98Chapter 6.2 --- Future Work --- p.99Bibliography --- p.10

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Accelerating ant colony optimization by using local search

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.Cataloged from PDF version of thesis report.Includes bibliographical references (page 42-45).Optimization is very important fact in terms of taking decision in mathematics, statistics, computer science and real life problem solving or decision making application. Many different optimization techniques have been developed for solving such functional problem. In order to solving various problem computer Science introduce evolutionary optimization algorithm and their hybrid. In recent years, test functions are using to validate new optimization algorithms and to compare the performance with other existing algorithm. There are many Single Object Optimization algorithm proposed earlier. For example: ACO, PSO, ABC. ACO is a popular optimization technique for solving hard combination mathematical optimization problem. In this paper, we run ACO upon five benchmark function and modified the parameter of ACO in order to perform SBX crossover and polynomial mutation. The proposed algorithm SBXACO is tested upon some benchmark function under both static and dynamic to evaluate performances. We choose wide range of benchmark function and compare results with existing DE and its hybrid DEahcSPX from other literature are also presented here.Nabila TabassumMaruful HaqueB. Computer Science and Engineerin

Production planning of biopharmaceutical manufacture.

Multiproduct manufacturing facilities running on a campaign basis are increasingly becoming the norm for biopharmaceuticals, owing to high risks of clinical failure, regulatory pressures and the increasing number of therapeutics in clinical evaluation. The need for such flexible plants and cost-effective manufacture pose significant challenges for planning and scheduling, which are compounded by long production lead times, intermediate product stability issues and the high cost - low volume nature of biopharmaceutical manufacture. Scheduling and planning decisions are often made in the presence of variable product titres, campaign durations, contamination rates and product demands. Hence this thesis applies mathematical programming techniques to the planning of biopharmaceutical manufacture in order to identify more optimal production plans under different manufacturing scenarios. A deterministic mixed integer linear programming (MILP) medium term planning model which explicitly accounts for upstream and downstream processing is presented. A multiscenario MILP model for the medium term planning of biopharmaceutical manufacture under uncertainty is presented and solved using an iterative solution procedure. An alternative stochastic formulation for the medium term planning of biomanufacture under uncertainty based on the principles of chance constrained programming is also presented. To help manage the risks of long term capacity planning in the biopharmaceutical industry, a goal programming extension is presented which accounts for multiple objectives including cost, risk and customer service level satisfaction. The model is applied to long term capacity analysis of a mix of contractors and owned biopharmaceutical manufacturing facilities. In the final sections of this thesis an example of a commercial application of this work is presented, followed by a discussion on related validation issues in the biopharmaceutical industry. The work in this thesis highlighted the benefits of applying mathematical programming techniques for production planning of biopharmaceutical manufacturing facilities, so as to enhance the biopharmaceutical industry's strategic and operational decision-making towards achieving more cost-effective manufacture

Cognitive Maps

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Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems

Integration of constraint programming and linear programming techniques for constraint satisfaction problem and general constrained optimization problem.

Wong Siu Ham.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 131-138).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgments --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation for Integration --- p.2Chapter 1.2 --- Thesis Overview --- p.4Chapter 2 --- Preliminaries --- p.5Chapter 2.1 --- Constraint Programming --- p.5Chapter 2.1.1 --- Constraint Satisfaction Problems (CSP's) --- p.6Chapter 2.1.2 --- Satisfiability (SAT) Problems --- p.10Chapter 2.1.3 --- Systematic Search --- p.11Chapter 2.1.4 --- Local Search --- p.13Chapter 2.2 --- Linear Programming --- p.17Chapter 2.2.1 --- Linear Programming Problems --- p.17Chapter 2.2.2 --- Simplex Method --- p.19Chapter 2.2.3 --- Mixed Integer Programming Problems --- p.27Chapter 3 --- Integration of Constraint Programming and Linear Program- ming --- p.29Chapter 3.1 --- Problem Definition --- p.29Chapter 3.2 --- Related works --- p.30Chapter 3.2.1 --- Illustrating the Performances --- p.30Chapter 3.2.2 --- Improving the Searching --- p.33Chapter 3.2.3 --- Improving the representation --- p.36Chapter 4 --- A Scheme of Integration for Solving Constraint Satisfaction Prob- lem --- p.37Chapter 4.1 --- Integrated Algorithm --- p.38Chapter 4.1.1 --- Overview of the Integrated Solver --- p.38Chapter 4.1.2 --- The LP Engine --- p.44Chapter 4.1.3 --- The CP Solver --- p.45Chapter 4.1.4 --- Proof of Soundness and Completeness --- p.46Chapter 4.1.5 --- Compared with Previous Work --- p.46Chapter 4.2 --- Benchmarking Results --- p.48Chapter 4.2.1 --- Comparison with CLP solvers --- p.48Chapter 4.2.2 --- Magic Squares --- p.51Chapter 4.2.3 --- Random CSP's --- p.52Chapter 5 --- A Scheme of Integration for Solving General Constrained Opti- mization Problem --- p.68Chapter 5.1 --- Integrated Optimization Algorithm --- p.69Chapter 5.1.1 --- Overview of the Integrated Optimizer --- p.69Chapter 5.1.2 --- The CP Solver --- p.74Chapter 5.1.3 --- The LP Engine --- p.75Chapter 5.1.4 --- Proof of the Optimization --- p.77Chapter 5.2 --- Benchmarking Results --- p.77Chapter 5.2.1 --- Weighted Magic Square --- p.77Chapter 5.2.2 --- Template design problem --- p.78Chapter 5.2.3 --- Random GCOP's --- p.79Chapter 6 --- Conclusions and Future Work --- p.97Chapter 6.1 --- Conclusions --- p.97Chapter 6.2 --- Future work --- p.98Chapter 6.2.1 --- Detection of implicit equalities --- p.98Chapter 6.2.2 --- Dynamical variable selection --- p.99Chapter 6.2.3 --- Analysis on help of linear constraints --- p.99Chapter 6.2.4 --- Local Search and Linear Programming --- p.99Appendix --- p.101Proof of Soundness and Completeness --- p.101Proof of the optimization --- p.126Bibliography --- p.13