Abstract: Background: A facility layout problem (FLP) is an NP-complete (non-deterministic polynomial) problem, for which is hard to obtain an exact optimal solution. FLP has been widely studied in various limited spaces and workflows. For example, cafeterias with many types of equipment for troops cause chaotic processes when dining. Objective: This article tried to optimize the layout of a troops’ cafeteria and to improve the overall efficiency of the dining process. Methods: First, the original cafeteria layout design scheme was analyzed from an ergonomic perspective and two new design schemes were generated. Next, three facility layout models were designed, and further simulation was applied to compare the total time and density of troops between each scheme. Last, an experiment of the dining process with video observation and analysis verified the simulation results. Results: In a simulation, the dining time under the second new layout is shortened by 2.25% and 1.89% (p
Abstract: The purpose of this paper is to develop a multi-product economic production quantity model under vendor management inventory policy and restrictions including limited warehouse space, budget, and number of orders, average shortage time and maximum permissible shortage. Since the “costs” cannot be predicted with certainty, it is assumed that data behave under uncertain environment. The problem is first formulated into the framework of a bi-objective of multi-product economic production quantity model. Then, the problem is solved with three multi-objective decision-making (MODM) methods. Then following this, three methods had been compared on information on the optimal value of the two objective functions and the central processing unit (CPU) time with the statistical analysis method and the multi-attribute decision-making (MADM). The results are compared with statistical analysis method and the MADM. The results of the study demonstrate that augmented-constraint in terms of optimal value of the two objective functions and the CPU time perform better than global criteria, and goal programming. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The contribution of this research is the use of random costs data in developing a multi-product economic production quantity model under vendor management inventory policy with several constraints.
Abstract: The smart antenna is the prominent technology that has become known in recent years to meet the growing demands of wireless communications. In an overcrowded atmosphere, its application is growing gradually. A methodical evaluation of the performance of Fixed Beamforming algorithms for smart antennas such as Multiple Sidelobe Canceller (MSC), Maximum Signal-to-interference ratio (MSIR) and minimum variance (MVDR) has been comprehensively presented in this paper. Simulation results show that beamforming is helpful in providing optimized response towards desired directions. MVDR beamformer provides the most optimal solution.
Abstract: The study presents the complexity of food safety dividing it into two layers. Beyond the basic layer of requirements, there is a more demanding higher level linked with quality and purity aspects. It would be important to give special prominence to both layers, given that massive illnesses are caused by foods even though officially licensed. Then the study discusses an exciting safety challenge stemming from the risks of genetically modified organisms (GMOs). Furthermore, it features legal case examples that illustrate how certain liability questions are solved or not yet decided in connection with the production of genetically modified crops. In addition, a special kind of land grabbing, more precisely land grabbing from non-GMO farming systems can also be noticed as well as a new phenomenon eroding food sovereignty. Coexistence, the state where organic, conventional, and GM farming systems are standing alongside each other is an unsuitable experiment that cannot be successful, because of biophysical reasons (such as cross-pollination). Agricultural and environmental lawyers both try to find the optimal solution. Agri-environmental measures are introduced as a special subfield of law maintaining also food safety. The important steps of agri-environmental legislation are aiming at the protection of natural values, the environmental media and strengthening food safety as well, practically the quality of agricultural products intended for human consumption. The major findings of the study focus on searching for the appropriate approach capable of solving the security and safety problems of food production. The most interesting concepts of the Hungarian national and EU food law legislation are analyzed in more detail with descriptive, analytic and comparative methods.
Abstract: Economic Load Dispatch (ELD) proves to be a vital optimization process in electric power system for allocating generation amongst various units to compute the cost of generation, the cost of emission involving global warming gases like sulphur dioxide, nitrous oxide and carbon monoxide etc. In this dissertation, we emphasize ramp rate constriction factor based particle swarm optimization (RRCPSO) for analyzing various performance objectives, namely cost of generation, cost of emission, and a dual objective function involving both these objectives through the experimental simulated results. A 6-unit 30 bus IEEE test case system has been utilized for simulating the results involving improved weight factor advanced ramp rate limit constraints for optimizing total cost of generation and emission. This method increases the tendency of particles to venture into the solution space to ameliorate their convergence rates. Earlier works through dispersed PSO (DPSO) and constriction factor based PSO (CPSO) give rise to comparatively higher computational time and less good optimal solution at par with current dissertation. This paper deals with ramp rate and constriction factor based well defined ramp rate PSO to compute various objectives namely cost, emission and total objective etc. and compares the result with DPSO and weight improved PSO (WIPSO) techniques illustrating lesser computational time and better optimal solution.
Abstract: This paper presents a design and optimization for a compliant gripper. The gripper is constructed based on the concept of compliant mechanism with flexure hinge. A passive force regulation mechanism is presented to control the grasping force a micro-sized object instead of using a sensor force. The force regulation mechanism is designed using the planar springs. The gripper is expected to obtain a large range of displacement to handle various sized objects. First of all, the statics and dynamics of the gripper are investigated by using the finite element analysis in ANSYS software. And then, the design parameters of the gripper are optimized via Taguchi method. An orthogonal array L9 is used to establish an experimental matrix. Subsequently, the signal to noise ratio is analyzed to find the optimal solution. Finally, the response surface methodology is employed to model the relationship between the design parameters and the output displacement of the gripper. The design of experiment method is then used to analyze the sensitivity so as to determine the effect of each parameter on the displacement. The results showed that the compliant gripper can move with a large displacement of 213.51 mm and the force regulation mechanism is expected to be used for high precision positioning systems.
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: Since the advent of the law 86/14 concerning the
exploitation of the national territory by foreign companies in
partnership with the Algerian oil and gas company, the problem of
hydrocarbons metering in the sharing production come out.
More generally, good management counting hydrocarbons can
provide data on the production wells, the field and the reservoir for
medium and long term planning, particularly in the context of the
management and field development.
In this work, we are interested in the transactional metering which
is a very delicate and crucial period in the current context of the new
hydrocarbon’s law characterized by assets system between the
various activities of Sonatrach and its foreign partners.
After a state of the art on hydrocarbons metering devices in
Algeria and elsewhere, we will decline the advantages and
disadvantages of each system, and then we describe the problem to
try to reach an optimal solution.
Abstract: Nature is a great source of inspiration for solving
complex problems in networks. It helps to find the optimal solution.
Metaheuristic algorithm is one of the nature-inspired algorithm which
helps in solving routing problem in networks. The dynamic features,
changing of topology frequently and limited bandwidth make the
routing, challenging in MANET. Implementation of appropriate
routing algorithms leads to the efficient transmission of data in
mobile ad hoc networks. The algorithms that are inspired by the
principles of naturally-distributed/collective behavior of social
colonies have shown excellence in dealing with complex
optimization problems. Thus some of the bio-inspired metaheuristic
algorithms help to increase the efficiency of routing in ad hoc
networks. This survey work presents the overview of bio-inspired
metaheuristic algorithms which support the efficiency of routing in
mobile ad hoc networks.
Abstract: This research proposes Bee Algorithm (BA) to
optimize Ready Mixed Concrete (RMC) truck scheduling problem
from single batch plant to multiple construction sites. This problem is
considered as an NP-hard constrained combinatorial optimization
problem. This paper provides the details of the RMC dispatching
process and its related constraints. BA was then developed to
minimize total waiting time of RMC trucks while satisfying all
constraints. The performance of BA is then evaluated on two
benchmark problems (3 and 5construction sites) according to
previous researchers. The simulation results of BA are compared in
term of efficiency and accuracy with Genetic Algorithm (GA) and all
problems show that BA approach outperforms GA in term of
efficiency and accuracy to obtain optimal solution. Hence, BA
approach could be practically implemented to obtain the best
schedule.
Abstract: Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Moreover, risks generally have trends and it also should be considered in risk treatment. Therefore, this paper provides the extension of the model proposed in the previous study. The original model supports the selection of measures by applying a combination of weighted average method and goal programming method for multi-objective analysis to find an optimal solution. The extended model includes the notion of weights to the risks, and the larger weight means the priority of the risk.
Abstract: The conventional GA combined with a local search
algorithm, such as the 2-OPT, forms a hybrid genetic algorithm(HGA)
for the traveling salesman problem (TSP). However, the geometric
properties which are problem specific knowledge can be used to
improve the search process of the HGA. Some tour segments (edges)
of TSPs are fine while some maybe too long to appear in a short tour.
This knowledge could constrain GAs to work out with fine tour
segments without considering long tour segments as often.
Consequently, a new algorithm is proposed, called intelligent-OPT
hybrid genetic algorithm (IOHGA), to improve the GA and the 2-OPT
algorithm in order to reduce the search time for the optimal solution.
Based on the geometric properties, all the tour segments are assigned
2-level priorities to distinguish between good and bad genes. A
simulation study was conducted to evaluate the performance of the
IOHGA. The experimental results indicate that in general the IOHGA
could obtain near-optimal solutions with less time and better accuracy
than the hybrid genetic algorithm with simulated annealing algorithm
(HGA(SA)).
Abstract: Both the minimum energy consumption and
smoothness, which is quantified as a function of jerk, are generally
needed in many dynamic systems such as the automobile and the
pick-and-place robot manipulator that handles fragile equipments.
Nevertheless, many researchers come up with either solely
concerning on the minimum energy consumption or minimum jerk
trajectory. This research paper proposes a simple yet very interesting
relationship between the minimum direct and indirect jerks
approaches in designing the time-dependent system yielding an
alternative optimal solution. Extremal solutions for the cost functions
of direct and indirect jerks are found using the dynamic optimization
methods together with the numerical approximation. This is to allow
us to simulate and compare visually and statistically the time history
of control inputs employed by minimum direct and indirect jerk
designs. By considering minimum indirect jerk problem, the
numerical solution becomes much easier and yields to the similar
results as minimum direct jerk problem.
Abstract: The equivalence class subset algorithm is a powerful
tool for solving a wide variety of constraint satisfaction problems and
is based on the use of a decision function which has a very high but
not perfect accuracy. Perfect accuracy is not required in the decision
function as even a suboptimal solution contains valuable information
that can be used to help find an optimal solution. In the hardest
problems, the decision function can break down leading to a
suboptimal solution where there are more equivalence classes than
are necessary and which can be viewed as a mixture of good decision
and bad decisions. By choosing a subset of the decisions made in
reaching a suboptimal solution an iterative technique can lead to an
optimal solution, using series of steadily improved suboptimal
solutions. The goal is to reach an optimal solution as quickly as
possible. Various techniques for choosing the decision subset are
evaluated.
Abstract: Both the minimum energy consumption and
smoothness, which is quantified as a function of jerk, are generally
needed in many dynamic systems such as the automobile and the
pick-and-place robot manipulator that handles fragile equipments.
Nevertheless, many researchers come up with either solely
concerning on the minimum energy consumption or minimum jerk
trajectory. This research paper considers the indirect minimum Jerk
method for higher order differential equation in dynamics
optimization proposes a simple yet very interesting indirect jerks
approaches in designing the time-dependent system yielding an
alternative optimal solution. Extremal solutions for the cost functions
of indirect jerks are found using the dynamic optimization methods
together with the numerical approximation. This case considers the
linear equation of a simple system, for instance, mass, spring and
damping. The simple system uses two mass connected together by
springs. The boundary initial is defined the fix end time and end
point. The higher differential order is solved by Galerkin-s methods
weight residual. As the result, the 6th higher differential order shows
the faster solving time.
Abstract: To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
Abstract: QoS Routing aims to find paths between senders and
receivers satisfying the QoS requirements of the application which
efficiently using the network resources and underlying routing
algorithm to be able to find low-cost paths that satisfy given QoS
constraints. The problem of finding least-cost routing is known to be
NP-hard or complete and some algorithms have been proposed to
find a near optimal solution. But these heuristics or algorithms either
impose relationships among the link metrics to reduce the complexity
of the problem which may limit the general applicability of the
heuristic, or are too costly in terms of execution time to be applicable
to large networks. In this paper, we concentrate an algorithm that
finds a near-optimal solution fast and we named this algorithm as
optimized Delay Constrained Routing (ODCR), which uses an
adaptive path weight function together with an additional constraint
imposed on the path cost, to restrict search space and hence ODCR
finds near optimal solution in much quicker time.
Abstract: Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Therefore, this paper provides a model which supports the selection of measures by applying multi-objective analysis to find an optimal solution. Additionally, a list of measures is also provided to make the selection easier and more effective without any leakage of measures.
Abstract: The Sphere Method is a flexible interior point algorithm for linear programming problems. This was developed mainly by Professor Katta G. Murty. It consists of two steps, the centering step and the descent step. The centering step is the most expensive part of the algorithm. In this centering step we proposed some improvements such as introducing two or more initial feasible solutions as we solve for the more favorable new solution by objective value while working with the rigorous updates of the feasible region along with some ideas integrated in the descent step. An illustration is given confirming the advantage of using the proposed procedure.
Abstract: QoS Routing aims to find paths between senders and
receivers satisfying the QoS requirements of the application which
efficiently using the network resources and underlying routing
algorithm to be able to find low-cost paths that satisfy given QoS
constraints. The problem of finding least-cost routing is known to be
NP hard or complete and some algorithms have been proposed to
find a near optimal solution. But these heuristics or algorithms either
impose relationships among the link metrics to reduce the complexity
of the problem which may limit the general applicability of the
heuristic, or are too costly in terms of execution time to be applicable
to large networks. In this paper, we analyzed two algorithms namely
Characterized Delay Constrained Routing (CDCR) and Optimized
Delay Constrained Routing (ODCR). The CDCR algorithm dealt an
approach for delay constrained routing that captures the trade-off
between cost minimization and risk level regarding the delay
constraint. The ODCR which uses an adaptive path weight function
together with an additional constraint imposed on the path cost, to
restrict search space and hence ODCR finds near optimal solution in
much quicker time.