Abstract: This paper presents the evaluation of performance (BSFC and BTE), combustion (Pmax) and emission (CO, NOx, HC and smoke opacity) parameters of karanja biodiesel in a single cylinder, four stroke, direct injection diesel engine by considering significant engine input parameters (blending ratio, compression ratio and load torque). Multi-objective optimization of performance, combustion and emission parameters is also carried out in a karanja biodiesel engine using hybrid RSM-NSGA-II technique. The pareto optimum solutions are predicted by running the hybrid RSM-NSGA-II technique. Each pareto optimal solution is having its own importance. Confirmation tests are also conducted at randomly selected few pareto solutions to check the authenticity of the results.
Abstract: Recently nature–inspired algorithms have widespread use throughout the tough and time consuming multi–objective scientific and engineering design optimization problems. In this paper, we present extended forms of firefly algorithm to find optimal Golomb ruler (OGR) sequences. The OGRs have their one of the major application as unequally spaced channel–allocation algorithm in optical wavelength division multiplexing (WDM) systems in order to minimize the adverse four–wave mixing (FWM) crosstalk effect. The simulation results conclude that the proposed optimization algorithm has superior performance compared to the existing conventional computing and nature–inspired optimization algorithms to find OGRs in terms of ruler length, total optical channel bandwidth and computation time.
Abstract: The influence of canned cycles and cutting parameters on hole quality in cryogenic drilling has been investigated experimentally and analytically. A three-level, three-parameter experiment was conducted by using the design-of-experiment methodology. The three levels of independent input parameters were the following: for canned cycles—a chip-breaking canned cycle (G73), a spot drilling canned cycle (G81), and a deep hole canned cycle (G83); for feed rates—0.2, 0.3, and 0.4 mm/rev; and for cutting speeds—60, 75, and 100 m/min. The selected work and tool materials were aluminum 6061-6T and high-speed steel (HSS), respectively. For cryogenic cooling, liquid nitrogen (LN2) was used and was applied externally. The measured output parameters were the three widely used quality characteristics of drilled holes—diameter error, circularity, and surface roughness. Pareto ANOVA was applied for analyzing the results. The findings revealed that the canned cycle has a significant effect on diameter error (contribution ratio 44.09%) and small effects on circularity and surface finish (contribution ratio 7.25% and 6.60%, respectively). The best results for the dimensional accuracy and surface roughness were achieved by G81. G73 produced the best circularity results; however, for dimensional accuracy, it was the worst level.
Abstract: Various retrofit techniques for reinforced concrete frame with infill wall have been steadily developed. Among those techniques, strengthening methodology based on diagonal FRP strips (FRP bracings) has numerous advantages such as feasibility of implementing without interrupting the building under operation, reduction of cost and time, and easy application. Considering the safety of structure and retrofit cost, the most appropriate retrofit solution is needed. Thus, the objective of this study is to suggest pareto-optimal solution for existing building using FRP bracings. To find pareto-optimal solution analysis, NSGA-II is applied. Moreover, the seismic performance of retrofit building is evaluated. The example building is 5-storey, 3-bay RC frames with infill wall. Nonlinear static pushover analyses are performed with FEMA 356. The criterion of performance evaluation is inter-story drift ratio at the performance level IO, LS, CP. Optimal retrofit solutions is obtained for 32 individuals and 200 generations. Through the proposed optimal solutions, we confirm the improvement of seismic performance of the example building.
Abstract: In this paper, we presented a Multi-Objective Random
Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based
on RDPSO and crowding distance sorting to improve the convergence
and distribution with less computation cost. MORDPSO-CD makes
the most of RDPSO to approach the true Pareto optimal solutions
fast. We adopt the crowding distance sorting technique to update and
maintain the archived optimal solutions. Introducing the crowding
distance technique into MORDPSO can make the leader particles
find the true Pareto solution ultimately. The simulation results reveal
that the proposed algorithm has better convergence and distribution.
Abstract: Energy has a prominent role for development of
nations. Countries which have energy resources also have strategic
power in the international trade of energy since it is essential for all
stages of production in the economy. Thus, it is important for
countries to analyze the weaknesses and strength of the system. On
the other side, international trade is one of the fields that are analyzed
as a complex network via network analysis. Complex network is one
of the tools to analyze complex systems with heterogeneous agents
and interaction between them. A complex network consists of nodes
and the interactions between these nodes. Total properties which
emerge as a result of these interactions are distinct from the sum of
small parts (more or less) in complex systems. Thus, standard
approaches to international trade are superficial to analyze these
systems. Network analysis provides a new approach to analyze
international trade as a network. In this network, countries constitute
nodes and trade relations (export or import) constitute edges. It
becomes possible to analyze international trade network in terms of
high degree indicators which are specific to complex networks such
as connectivity, clustering, assortativity/disassortativity, centrality,
etc. In this analysis, international trade of crude oil and coal which
are types of fossil fuel has been analyzed from 2005 to 2014 via
network analysis. First, it has been analyzed in terms of some
topological parameters such as density, transitivity, clustering etc.
Afterwards, fitness to Pareto distribution has been analyzed via
Kolmogorov-Smirnov test. Finally, weighted HITS algorithm has
been applied to the data as a centrality measure to determine the real
prominence of countries in these trade networks. Weighted HITS
algorithm is a strong tool to analyze the network by ranking countries
with regards to prominence of their trade partners. We have
calculated both an export centrality and an import centrality by
applying w-HITS algorithm to the data. As a result, impacts of the
trading countries have been presented in terms of high-degree
indicators.
Abstract: Concurrent planning of project scheduling and
material ordering has been increasingly addressed within last decades
as an approach to improve the project execution costs. Therefore, we
have taken the problem into consideration in this paper, aiming to
maximize schedules quality robustness, in addition to minimize the
relevant costs. In this regard, a bi-objective mathematical model is
developed to formulate the problem. Moreover, it is possible to
utilize the all-unit discount for materials purchasing. The problem is
then solved by the E-constraint method, and the Pareto front is
obtained for a variety of robustness values. The applicability and
efficiency of the proposed model is tested by different numerical
instances, finally.
Abstract: Milk is considered as an essential and complete food.
The present study was conducted at Milk Plant Mohali especially in
reference to the procurement section where the cash inflow was
maximum, with the objective to achieve higher productivity and
reduce wastage of milk. In milk plant it was observed that during the
month of Jan-2014 to March-2014 the average procurement of milk
was Rs. 4, 19, 361 liter per month and cost of procurement of milk is
Rs 35/- per liter. The total cost of procurement thereby equal to Rs.
1crore 46 lakh per month, but there was mismatch in procurementproduction
of milk, which leads to an average loss of Rs. 12, 94, 405
per month. To solve the procurement-production problem Quality
Control Tools like brainstorming, Flow Chart, Cause effect diagram
and Pareto analysis are applied wherever applicable. With the
successful implementation of Quality Control tools an average saving
of Rs. 4, 59, 445 per month is done.
Abstract: Lead time is a critical measure of a supply chain's
performance. It impacts both the customer satisfactions as well as the
total cost of inventory. This paper presents the result of a study on the
analysis of the customer order lead-time for a multinational company.
In the study, the lead time was divided into three stages respectively:
order entry, order fulfillment, and order delivery. A sample of size 2,425 order lines was extracted from the
company's records to use for this study. The sample data entails
information regarding customer orders from the time of order entry
until order delivery. Data regarding the lead time of each stage for
different orders were also provided. Summary statistics on lead time
data reveals that about 30% of the orders were delivered later than the
scheduled due date. The result of the multiple linear regression
analysis technique revealed that component type, logistics parameter,
order size and the customer type have significant impacts on lead
time. Data analysis on the stages of lead time indicates that stage 2
consumed over 50% of the lead time. Pareto analysis was made to
study the reasons for the customer order delay in each stage.
Recommendation was given to resolve the problem.
Abstract: ABC classification is widely used by managers for
inventory control. The classical ABC classification is based on Pareto
principle and according to the criterion of the annual use value only.
Single criterion classification is often insufficient for a closely
inventory control. Multi-criteria inventory classification models have
been proposed by researchers in order to consider other important
criteria. From these models, we will consider a specific model in
order to make a sensitive analysis on the composite score calculated
for each item. In fact, this score, based on a normalized average
between a good and a bad optimized index, can affect the ABC-item
classification. We will focus on items differently assigned to classes
and then propose a classification compromise.
Abstract: Modeling and forecasting dynamics of rainfall
occurrences constitute one of the major topics, which have been
largely treated by statisticians, hydrologists, climatologists and many
other groups of scientists. In the same issue, we propose, in the
present paper, a new hybrid method, which combines Extreme
Values and fractal theories. We illustrate the use of our methodology
for transformed Emberger Index series, constructed basing on data
recorded in Oujda (Morocco).
The index is treated at first by Peaks Over Threshold (POT)
approach, to identify excess observations over an optimal threshold u.
In the second step, we consider the resulting excess as a fractal object
included in one dimensional space of time. We identify fractal
dimension by the box counting. We discuss the prospect descriptions
of rainfall data sets under Generalized Pareto Distribution, assured by
Extreme Values Theory (EVT). We show that, despite of the
appropriateness of return periods given by POT approach, the
introduction of fractal dimension provides accurate interpretation
results, which can ameliorate apprehension of rainfall occurrences.
Abstract: This paper presents an evolutionary algorithm for
solving multi-objective optimization problems-based artificial neural
network (ANN). The multi-objective evolutionary algorithm used in
this study is genetic algorithm while ANN used is radial basis
function network (RBFN). The proposed algorithm named memetic
elitist Pareto non-dominated sorting genetic algorithm-based RBFN
(MEPGAN). The proposed algorithm is implemented on medical
diseases problems. The experimental results indicate that the
proposed algorithm is viable, and provides an effective means to
design multi-objective RBFNs with good generalization capability
and compact network structure. This study shows that MEPGAN
generates RBFNs coming with an appropriate balance between
accuracy and simplicity, comparing to the other algorithms found in
literature.
Abstract: Assembly line balancing problem is aimed to divide
the tasks among the stations in assembly lines and optimize some
objectives. In assembly lines the workload on stations is different
from each other due to different tasks times and the difference in
workloads between stations can cause blockage or starvation in some
stations in assembly lines. Buffers are used to store the semi-finished
parts between the stations and can help to smooth the assembly
production. The assembly line balancing and buffer sizing problem
can affect the throughput of the assembly lines. Assembly line
balancing and buffer sizing problems have been studied separately in
literature and due to their collective contribution in throughput rate of
assembly lines, balancing and buffer sizing problem are desired to
study simultaneously and therefore they are considered concurrently
in current research. Current research is aimed to maximize
throughput, minimize total size of buffers in assembly line and
minimize workload variations in assembly line simultaneously. A
multi objective optimization objective is designed which can give
better Pareto solutions from the Pareto front and a simple example
problem is solved for assembly line balancing and buffer sizing
simultaneously. Current research is significant for assembly line
balancing research and it can be significant to introduce optimization
approaches which can optimize current multi objective problem in
future.
Abstract: It was expected to benefit patient with hemiparesis after stroke by extensive arm rehabilitation, to partially regain forearm and hand function. This paper propose a robotic rehabilitation arm in assisting the hemiparetic patient to learn new ways of using and moving their weak arms. In this study, the robotic arm was driven by a somatosensory stimulated brain computer interface (BCI), which is a new modality BCI. The use of somatosensory stimulation is not only an input for BCI, but also a electrical stimulation for treatment of hemiparesis to strengthen the arm and improve its range of motion. A trial of this robotic rehabilitation arm was performed in a stroke patient with pure motor hemiparesis. The initial trial showed a promising result from the patient with great motivation and function improvement. It suggests that robotic rehabilitation arm driven by somatosensory BCI can enhance the rehabilitation performance and progress for hemiparetic patients after stroke.
Abstract: In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network.
The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters.
Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output.
This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc.
From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.
Abstract: Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering wind turbines. The main objective of the DFR is to minimize the deviation of the bus voltage. Since the DFR is a nonlinear optimization problem, we apply the Adaptive Modified Firefly Optimization (AMFO) approach to solve it. As a result of the conflicting behavior of the single- objective function, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The approach is tested on the IEEE 32-bus standard test system.
Abstract: This work is developed within IAEA Coordinated Research Program 1496, “Innovative methods in research reactor analysis: Benchmark against experimental data on neutronics and thermal-hydraulic computational methods and tools for operation and safety analysis of research reactors”.
The study investigates the capability of Code RELAP5/Mod3.4 to solve complex geometry complexity. Its results are compared to the results of PARET, a common code in thermal hydraulic analysis for research reactors, belonging to MTR-PC groups.
The WWR-SM reactor at the Institute of Nuclear Physics (INP) in the Republic of Uzbekistan is simulated using both PARET and RELAP5 at steady state. Results from the two codes are compared.
REALP5 code succeeded in solving the complex fuel geometry. The PARET code needed some calculations to obtain the final result. Although the final results from the PARET are more accurate, the small differences in both results makes using RELAP5 code recommended in case of complex fuel assemblies.
Abstract: The focus of this paper is to develop a technique
of solving a combined problem of determining Optimum Strata
Boundaries(OSB) and Optimum Sample Size (OSS) of each stratum,
when the population understudy isskewed and the study variable has
a Pareto frequency distribution. The problem of determining the OSB
isformulated as a Mathematical Programming Problem (MPP) which
is then solved by dynamic programming technique. A numerical
example is presented to illustrate the computational details of the
proposed method. The proposed technique is useful to obtain OSB
and OSS for a Pareto type skewed population, which minimizes the
variance of the estimate of population mean.
Abstract: This study presents an Expert System specially designed to be used with Multiobjective Evolutionary Algorithms (MOEAs) for the solution of the portfolio selection problem. The validation of the proposed hybrid System is done by using data sets from Hang Seng 31 in Hong Kong, DAX 100 in Germany and FTSE 100 in UK. The performance of the proposed system is assessed in comparison with the Non-dominated Sorting Genetic Algorithm II (NSGAII). The evaluation of the performance is based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it. The results show that the proposed hybrid system is efficient for the solution of this kind of problems.
Abstract: The data transmission between mobile hosts and base stations (BSs) in Mobile networks are often vulnerable to failure. So, efficient link connectivity, in terms of the services of both base stations and communication channels of the network, is required in wireless mobile networks to achieve highly reliable data transmission. In addition, it is observed that the number of blocked hosts is increased due to insufficient number of channels during heavy load in the network. Under such scenario, the channels are allocated accordingly to offer a reliable communication at any given time. Therefore, a reliability-based channel allocation model with acceptable system performance is proposed as a MOO problem in this paper. Two conflicting parameters known as Resource Reuse factor (RRF) and the number of blocked calls are optimized under reliability constraint in this problem. The solution to such MOO problem is obtained through NSGA-II (Non dominated Sorting Genetic Algorithm). The effectiveness of the proposed model in this work is shown with a set of experimental results.