Hassen T. Dorrah

Using Ant Colony Optimization algorithm for solving project management problems

Network analysis provides an effective practical system for planning and controlling large projects in construction and many other fields. Ant Colony System is a recent approach used for solving path minimization problems. This paper presents the use of Ant Colony Optimization (ACO) system for solving and calculating both deterministic and probabilistic CPM/PERT networks. The proposed method is investigated for a selected case study in construction management. The results demonstrate that - compared to conventional methods - ACO can produce good optimal and suboptimal solutions.

Multi-objective linear optimization using fuzzy logic-based arithmetic and visual representations with forward and backward tracking

This paper presents a new formulation of multi-objective linear optimization using the fuzzy logic-based arithmetic (or visual) representations developed originally by Gabr and Dorrah [1–3]. A modified dual simplex algorithm is proposed as part of the formulation implementation procedure operating in a fully fuzzy environment. Moreover, forward and backward tracking mechanisms are incorporated with the above formulation for fuzzy level management and control. Cognitive mapping is used to describe the interaction between various parameters of the optimization formulation. Applications of the proposed approach to the multi-objective linear optimization of three stand alone electric pump stations of different sizes (Small, Medium and Large) are provided. The implementation is carried out using spread-sheet representation with the suggested optimization algorithms programmed in Visual Basic Applications (VBA). The results demonstrate the high efficacy and applicability of the fuzzy logic-based arithmetic and visual representations for real life problems operating in a fully fuzzy environment.

Stator fault estimation in induction motors using particle swarm optimization

The use of induction motors is extensive in industry. The working conditions of these motors make them subject to many faults. These faults must be detected in an early stage before they lead to catastrophic failures. This paper presents a scheme for detecting inter-turn faults in the stator windings of induction motors and estimating the fault severity. Detection of incipient inter-turn faults prevents further insulation failure. The proposed algorithm monitors the spectral content of stator currents to detect the fault. After the fault is detected and identified, a particle swarm approach is used to estimate the fault severity. The swarm estimator update is based on the error between the measured data and a complete model of the faulty motor. An experimental setup is used to validate the developed scheme and to implement an online fault detector.

New fuzzy logic-based arithmetic and visual representations for systems' modelling and optimization

This paper is directed towards developing two new fuzzy logic-based representations for systems modelling and optimization using the notion of the normalized fuzzy matrices. The first is the arithmetic type based on dual cell representation, expressed by replacing each parameter with a pair of parentheses, the first is the actual value and the second is corresponding fuzzy level, (Value, Fuzzy Level). The second is the visual type based on coloured cells representation expressed by replacing each parameter by its value and a coded (newly defined negative or positive colours based on the colour Hue circle) corresponding to its fuzzy level. For both cases, the theoretical foundations of the fuzzy logic algebra are presented for various cases of operations.

Development of fuzzy logic-based arithmetic and visual representations for solving quadratic programming in fully fuzzy environment

This paper presents the development of fuzzy logic representations using the notion of normalized fuzzy matrices developed by Gabr and Dorrah [1–4] for solving quadratic programming problems in a fully fuzzy environment. The first is the arithmetic type based on dual cell representation, expressed by replacing each parameter with a pair of parentheses, the first is the actual value and the second is corresponding fuzzy level, (Value, Fuzzy Level). The second is the visual type based on colored cells representation expressed by replacing each parameter by its value and coded (negative or positive) colors based on the color Hue circle corresponding to its fuzzy level. The quadratic programming problem formulation in its general form is developed in a fully fuzzy environment. A modified dual simplex method algorithm is depicted for the representation of the equivalent linear optimization problem. The problem is represented in a spreadsheet model with built-in programmed Visual Basic Applications macros. The proposed fuzzy logic algebra is then used in a straightforward manner inside this spreadsheet model. The fuzzy logic levels can be easily transferred at the end of the solution to equivalent uncertainties (each level is substituted by a corresponding actual mean and actual standard deviation). Finally, a numerical example is given to illustrate the efficacy of the developed formulations.

New approach for automatic control modeling and analysis using Arithmetic And Visual Fuzzy Logic-based Representations in fully fuzzy environment

The paper presents a new approach for the fuzzy modeling and analysis of automatic control systems in fully fuzzy environment. The proposed technique is based on the normalized fuzzy matrices and is an extension of the Arithmetic and Visual Fuzzy Logic-based Representations developed recently by Gabr and Dorrah. The approach is also suitable for determining the propagation of fuzziness in automatic control and dynamical systems where all system coefficients are expressed as fuzzy parameters. It is simply based on the assignment of corresponding fuzzy levels for parameters uncertainty that is made in a heuristic way circumventing the previous difficulties in assuming probabilistic or membership functions. Implementations of the approach are carried out for solving selected automatic control problems with parameters expressed in fully fuzzy environment. These problems cover fuzzy impulse response of systems, fuzzy Routh-Hurwitz stability criteria, fuzzy Controllability and Observability, and the stabilization of inverted pendulum through pole placement technique. The results demonstrated the robustness of the proposed formulation and illustrated in a systematic way how the system parameters fuzziness effect on output results can be effectively tracked for monitoring and control. Finally, it is pointed out that the suggested Arithmetic and Visual Fuzzy Logic-based Representations opens the door for a unified theory for fuzzy modeling, analysis and design of continuous and discrete automatic control systems operating in fully fuzzy environment.

Development of Fuzzy Logic-based Arithmetic and Visual Representations for systems' modelling and optimization of interconnected networks

The foundation of new Fuzzy Logic Level Arithmetic and Visual representations, normalized fuzzy matrices, and the principle of fuzzy logic-based level tracking were developed by Gabr and Dorrah for systems modelling and optimization during all stages of formulation and solution. This new concept has an unlimited scope of generalizations and extensions to many classes of problems and systems in various disciplines. In this paper, further development of the above concept to the modelling and multi-objective linear optimization of multi-stations operating interconnected network in a fully fuzzy environment is presented.

Multi-criteria optimization of the Egyptian hydropower and irrigation networks

The problem of multiobjective optimization of the hydropower generation versus irrigation releases applied to the Egyptian Network is addressed. A linear programming-based multi-criteria model is developed based on the described system constraints. Two objective functions are considered in the analysis. The first is to maximize the total sum of potential hydropower production of the whole system. The second is to minimize the total water release to the system, while satisfying the different demands imposed at various nodes. Using the generation technique for solution, the results have revealed several trade-off curves of both hydropower and irrigation uses. These scenarios are shown to be very effective planning tools that aid the decision maker in selecting the best suitable compromise for his need.<<ETX>>

A New Hybrid BFOA-PSO Optimization Technique for Decoupling and Robust Control of Two-Coupled Distillation Column Process

The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The papers main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller.

The multidimensional assignment problem with application

The mathematical foundation of a special class of the symmetric multidimensional assignment problem is presented as a generalization of the well-known two-dimensional assignment model. The formulation is based on constructing a multidimensional performance index for the problem, to be optimized such that one assignment is attained at each layer. A procedure is suggested for solving such a formulation, based on modifying its elements until one independent zero component is attained in each plane. A case study of the limestone selection in a quarry for cement manufacturing is furnished to demonstrate the efficacy of the approach. It is shown that the optimal strategy, based on multidimensional assignment formulation, is effective and tends to considerably reduce the resulting fluctuations in raw material quality.<<ETX>>