Abstract: We propose a multi-agent based utilitarian approach
to model and understand information flows in social networks that
lead to Pareto optimal informational exchanges. We model the
individual expected utility function of the agents to reflect the net
value of information received. We show how this model, adapted
from a theorem by Karl Borch dealing with an actuarial Risk
Exchange concept in the Insurance industry, can be used for social
network analysis. We develop a utilitarian framework that allows us
to interpret Pareto optimal exchanges of value as potential
information flows, while achieving a maximization of a sum of
expected utilities of information of the group of agents. We examine
some interesting conditions on the utility function under which the
flows are optimal. We illustrate the promise of this new approach to
attach economic value to information in networks with a synthetic
example.
Abstract: This paper considers a multi criteria cell formation
problem in Cellular Manufacturing System (CMS). Minimizing the
number of voids and exceptional elements in cells simultaneously are
two proposed objective functions. This problem is an Np-hard
problem according to the literature, and therefore, we can-t find the
optimal solution by an exact method. In this paper we developed two
ant algorithms, Ant Colony Optimization (ACO) and Max-Min Ant
System (MMAS), based on Data Envelopment Analysis (DEA). Both
of them try to find the efficient solutions based on efficiency concept
in DEA. Each artificial ant is considered as a Decision Making Unit
(DMU). For each DMU we considered two inputs, the values of
objective functions, and one output, the value of one for all of them.
In order to evaluate performance of proposed methods we provided
an experimental design with some empirical problem in three
different sizes, small, medium and large. We defined three different
criteria that show which algorithm has the best performance.
Abstract: The problem of mapping tasks onto a computational grid with the aim to minimize the power consumption and the makespan subject to the constraints of deadlines and architectural requirements is considered in this paper. To solve this problem, we propose a solution from cooperative game theory based on the concept of Nash Bargaining Solution. The proposed game theoretical technique is compared against several traditional techniques. The experimental results show that when the deadline constraints are tight, the proposed technique achieves superior performance and reports competitive performance relative to the optimal solution.
Abstract: This paper considers the problem of scheduling maintenance actions for identical aircraft gas turbine engines. Each one of the turbines consists of parts which frequently require replacement. A finite inventory of spare parts is available and all parts are ready for replacement at any time. The inventory consists of both new and refurbished parts. Hence, these parts have different field lives. The goal is to find a replacement part sequencing that maximizes the time that the aircraft will keep functioning before the inventory is replenished. The problem is formulated as an identical parallel machine scheduling problem where the minimum completion time has to be maximized. Two models have been developed. The first one is an optimization model which is based on a 0-1 linear programming formulation, while the second one is an approximate procedure which consists in decomposing the problem into several two-machine subproblems. Each subproblem is optimally solved using the first model. Both models have been implemented using Lingo and have been tested on two sets of randomly generated data with up to 150 parts and 10 turbines. Experimental results show that the optimization model is able to solve only instances with no more than 4 turbines, while the decomposition procedure often provides near-optimal solutions within a maximum CPU time of 3 seconds.
Abstract: In this paper, a mathematical model of human immunodeficiency
virus (HIV) is utilized and an optimization problem is
proposed, with the final goal of implementing an optimal 900-day
structured treatment interruption (STI) protocol. Two type of commonly
used drugs in highly active antiretroviral therapy (HAART),
reverse transcriptase inhibitors (RTI) and protease inhibitors (PI), are
considered. In order to solving the proposed optimization problem an
adaptive memetic algorithm with population management (AMAPM)
is proposed. The AMAPM uses a distance measure to control the
diversity of population in genotype space and thus preventing the
stagnation and premature convergence. Moreover, the AMAPM uses
diversity parameter in phenotype space to dynamically set the population
size and the number of crossovers during the search process.
Three crossover operators diversify the population, simultaneously.
The progresses of crossover operators are utilized to set the number
of each crossover per generation. In order to escaping the local optima
and introducing the new search directions toward the global optima,
two local searchers assist the evolutionary process. In contrast to
traditional memetic algorithms, the activation of these local searchers
is not random and depends on both the diversity parameters in
genotype space and phenotype space. The capability of AMAPM in
finding optimal solutions compared with three popular metaheurestics
is introduced.
Abstract: We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.
Abstract: An enhanced particle swarm optimization algorithm
(PSO) is presented in this work to solve the non-convex OPF
problem that has both discrete and continuous optimization variables.
The objective functions considered are the conventional quadratic
function and the augmented quadratic function. The latter model
presents non-differentiable and non-convex regions that challenge
most gradient-based optimization algorithms. The optimization
variables to be optimized are the generator real power outputs and
voltage magnitudes, discrete transformer tap settings, and discrete
reactive power injections due to capacitor banks. The set of equality
constraints taken into account are the power flow equations while the
inequality ones are the limits of the real and reactive power of the
generators, voltage magnitude at each bus, transformer tap settings,
and capacitor banks reactive power injections. The proposed
algorithm combines PSO with Newton-Raphson algorithm to
minimize the fuel cost function. The IEEE 30-bus system with six
generating units is used to test the proposed algorithm. Several cases
were investigated to test and validate the consistency of detecting
optimal or near optimal solution for each objective. Results are
compared to solutions obtained using sequential quadratic
programming and Genetic Algorithms.
Abstract: In the multi objective optimization, in the case when generated set of Pareto optimal solutions is large, occurs the problem to select of the best solution from this set. In this paper, is suggested a method to order of Pareto set. Ordering the Pareto optimal set carried out in conformity with the introduced distance function between each solution and selected reference point, where the reference point may be adjusted to represent the preferences of a decision making agent. Preference information about objective weights from a decision maker may be expressed imprecisely. The developed elicitation procedure provides an opportunity to obtain surrogate numerical weights for the objectives, and thus, to manage impreciseness of preference. The proposed method is a scalable to many objectives and can be used independently or as complementary to the various visualization techniques in the multidimensional case.
Abstract: Multilevel inverters supplied from equal and constant
dc sources almost don-t exist in practical applications. The variation
of the dc sources affects the values of the switching angles required
for each specific harmonic profile, as well as increases the difficulty
of the harmonic elimination-s equations. This paper presents an
extremely fast optimal solution of harmonic elimination of multilevel
inverters with non-equal dc sources using Tanaka's fuzzy linear
regression formulation. A set of mathematical equations describing
the general output waveform of the multilevel inverter with nonequal
dc sources is formulated. Fuzzy linear regression is then
employed to compute the optimal solution set of switching angles.
Abstract: Multiprocessor task scheduling is a NP-hard problem and Genetic Algorithm (GA) has been revealed as an excellent technique for finding an optimal solution. In the past, several methods have been considered for the solution of this problem based on GAs. But, all these methods consider single criteria and in the present work, minimization of the bi-criteria multiprocessor task scheduling problem has been considered which includes weighted sum of makespan & total completion time. Efficiency and effectiveness of genetic algorithm can be achieved by optimization of its different parameters such as crossover, mutation, crossover probability, selection function etc. The effects of GA parameters on minimization of bi-criteria fitness function and subsequent setting of parameters have been accomplished by central composite design (CCD) approach of response surface methodology (RSM) of Design of Experiments. The experiments have been performed with different levels of GA parameters and analysis of variance has been performed for significant parameters for minimisation of makespan and total completion time simultaneously.
Abstract: This article proposes an Ant Colony Optimization
(ACO) metaheuristic to minimize total makespan for scheduling a set
of jobs and assign workers for uniformly related parallel machines.
An algorithm based on ACO has been developed and coded on a
computer program Matlab®, to solve this problem. The paper
explains various steps to apply Ant Colony approach to the problem
of minimizing makespan for the worker assignment & jobs
scheduling problem in a parallel machine model and is aimed at
evaluating the strength of ACO as compared to other conventional
approaches. One data set containing 100 problems (12 Jobs, 03
machines and 10 workers) which is available on internet, has been
taken and solved through this ACO algorithm. The results of our
ACO based algorithm has shown drastically improved results,
especially, in terms of negligible computational effort of CPU, to
reach the optimal solution. In our case, the time taken to solve all 100
problems is even lesser than the average time taken to solve one
problem in the data set by other conventional approaches like GA
algorithm and SPT-A/LMC heuristics.
Abstract: With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution.
Abstract: This study presents a new approach based on Tanaka's
fuzzy linear regression (FLP) algorithm to solve well-known power
system economic load dispatch problem (ELD). Tanaka's fuzzy linear
regression (FLP) formulation will be employed to compute the
optimal solution of optimization problem after linearization. The
unknowns are expressed as fuzzy numbers with a triangular
membership function that has middle and spread value reflected on
the unknowns. The proposed fuzzy model is formulated as a linear
optimization problem, where the objective is to minimize the sum of
the spread of the unknowns, subject to double inequality constraints.
Linear programming technique is employed to obtain the middle and
the symmetric spread for every unknown (power generation level).
Simulation results of the proposed approach will be compared with
those reported in literature.
Abstract: An optimal solution for a large number of constraint
satisfaction problems can be found using the technique of
substitution and elimination of variables analogous to the technique
that is used to solve systems of equations. A decision function
f(A)=max(A2) is used to determine which variables to eliminate. The
algorithm can be expressed in six lines and is remarkable in both its
simplicity and its ability to find an optimal solution. However it is
inefficient in that it needs to square the updated A matrix after each
variable elimination. To overcome this inefficiency the algorithm is
analyzed and it is shown that the A matrix only needs to be squared
once at the first step of the algorithm and then incrementally updated
for subsequent steps, resulting in significant improvement and an
algorithm complexity of O(n3).
Abstract: The concept of flexible manufacturing is highly
appealing in gaining a competitive edge in the market by quickly
adapting to the changing customer needs. Scheduling jobs on flexible
manufacturing systems (FMSs) is a challenging task of managing the
available flexibility on the shop floor to react to the dynamics of the
environment in real-time. In this paper, an agent-oriented scheduling
framework that can be integrated with a real or a simulated FMS is
proposed. This framework works in stochastic environments with a
dynamic model of job arrival. It supports a hierarchical cooperative
scheduling that builds on the available flexibility of the shop floor.
Testing the framework on a model of a real FMS showed the
capability of the proposed approach to overcome the drawbacks of
the conventional approaches and maintain a near optimal solution
despite the dynamics of the operational environment.
Abstract: This paper presents an application of particle swarm
optimization (PSO) to the grounding grid planning which compares to
the application of genetic algorithm (GA). Firstly, based on IEEE
Std.80, the cost function of the grounding grid and the constraints of
ground potential rise, step voltage and touch voltage are constructed
for formulating the optimization problem of grounding grid planning.
Secondly, GA and PSO algorithms for obtaining optimal solution of
grounding grid are developed. Finally, a case of grounding grid
planning is shown the superiority and availability of the PSO
algorithm and proposal planning results of grounding grid in cost and
computational time.
Abstract: In this paper, the fuzzy linear programming formulation
of fuzzy maximal flow problems are proposed and on the basis of the
proposed formulation a method is proposed to find the fuzzy optimal
solution of fuzzy maximal flow problems. In the proposed method all
the parameters are represented by triangular fuzzy numbers. By using
the proposed method the fuzzy optimal solution of fuzzy maximal
flow problems can be easily obtained. To illustrate the proposed
method a numerical example is solved and the obtained results are
discussed.
Abstract: This study proposes a hybrid minimal repair policy
which combines periodic maintenance policy with age-based maintenance policy for a serial production system. Parameters of such policy are defined as and which indicate as hybrid minimal
repair time and planned preventive maintenance time
respectively . Under this hybrid policy, the system is
repaired minimally if it fails during , . A perfect repair is
conducted on the first failure after at any machines. At the same time, we take opportunity to advance the preventive maintenance of
other machines simultaneously. If the system is still operating
properly up to , then the preventive maintenance is carried out as its
predetermined schedule. For a given , we obtain the optimal value which minimizes the expected cost per time unit. Numerical
example is presented to illustrate the properties of the optimal solution.
Abstract: Several approaches such as linear programming, network
modeling, greedy heuristic and decision support system are well-known
approaches in solving irregular airline operation problem. This paper
presents an alternative approach based on Multi Objective Micro Genetic
Algorithm. The aim of this research is to introduce the concept of Multi
Objective Micro Genetic Algorithm as a tool to solve irregular airline
operation, combine and reroute problem. The experiment result indicated
that the model could obtain optimal solutions within a few second.
Abstract: The Resource-Constrained Project Scheduling
Problem (RCPSP) is concerned with single-item or small batch
production where limited resources have to be allocated to dependent
activities over time. Over the past few decades, a lot of work has
been made with the use of optimal solution procedures for this basic
problem type and its extensions. Brucker and Knust[1] discuss, how
timetabling problems can be modeled as a RCPSP. Authors discuss
high school timetabling and university course timetabling problem as
an example. We have formulated two mathematical formulations of
course timetabling problem in a new way which are the prototype of
single-mode RCPSP. Our focus is to show, how course timetabling
problem can be transformed into RCPSP. We solve this
transformation model with genetic algorithm.