Abstract: One of the most important problems in production planning of flexible manufacturing system (FMS) is machine tool selection and operation allocation problem that directly influences the production costs and times .In this paper minimizing machining cost, set-up cost and material handling cost as a multi-objective problem in flexible manufacturing systems environment are considered. We present a 0-1 integer linear programming model for the multiobjective machine tool selection and operation allocation problem and due to the large scale nature of the problem, solving the problem to obtain optimal solution in a reasonable time is infeasible, Paretoant colony optimization (P-ACO) approach for solving the multiobjective problem in reasonable time is developed. Experimental results indicate effectiveness of the proposed algorithm for solving the problem.
Abstract: This work addresses the problem of optimizing
completely batch water-using network with multiple contaminants
where the flow change caused by mass transfer is taken into
consideration for the first time. A mathematical technique for
optimizing water-using network is proposed based on
source-tank-sink superstructure. The task is to obtain the freshwater
usage, recycle assignments among water-using units, wastewater
discharge and a steady water-using network configuration by
following steps. Firstly, operating sequences of water-using units are
determined by time constraints. Next, superstructure is simplified by
eliminating the reuse and recycle from water-using units with
maximum concentration of key contaminants. Then, the non-linear
programming model is solved by GAMS (General Algebra Model
System) for minimum freshwater usage, maximum water recycle and
minimum wastewater discharge. Finally, numbers of operating periods
are calculated to acquire the steady network configuration. A case
study is solved to illustrate the applicability of the proposed approach.
Abstract: A simple and easy algorithm is presented for a fast calculation of kernel functions which required in fluid simulations using the Smoothed Particle Hydrodynamic (SPH) method. Present proposed algorithm improves the Linked-list algorithm and adopts the Pair-Wise Interaction technique, which are widely used for evaluating kernel functions in fluid simulations using the SPH method. The algorithm is easy to be implemented without any complexities in programming. Some benchmark examples are used to show the simulation time saved by using the proposed algorithm. Parametric studies on the number of divisions for sub-domains, smoothing length and total amount of particles are conducted to show the effectiveness of the present technique. A compact formulation is proposed for practical usage.
Abstract: This paper proposes a bi-objective model for the
facility location problem under a congestion system. The idea of the
model is motivated by applications of locating servers in bank
automated teller machines (ATMS), communication networks, and so
on. This model can be specifically considered for situations in which
fixed service facilities are congested by stochastic demand within
queueing framework. We formulate this model with two perspectives
simultaneously: (i) customers and (ii) service provider. The
objectives of the model are to minimize (i) the total expected
travelling and waiting time and (ii) the average facility idle-time.
This model represents a mixed-integer nonlinear programming
problem which belongs to the class of NP-hard problems. In addition,
to solve the model, two metaheuristic algorithms including nondominated
sorting genetic algorithms (NSGA-II) and non-dominated
ranking genetic algorithms (NRGA) are proposed. Besides, to
evaluate the performance of the two algorithms some numerical
examples are produced and analyzed with some metrics to determine
which algorithm works better.
Abstract: In the recent past, there has been an increasing interest
in applying evolutionary methods to Knowledge Discovery in
Databases (KDD) and a number of successful applications of Genetic
Algorithms (GA) and Genetic Programming (GP) to KDD have been
demonstrated. The most predominant representation of the
discovered knowledge is the standard Production Rules (PRs) in the
form If P Then D. The PRs, however, are unable to handle
exceptions and do not exhibit variable precision. The Censored
Production Rules (CPRs), an extension of PRs, were proposed by
Michalski & Winston that exhibit variable precision and supports an
efficient mechanism for handling exceptions. A CPR is an
augmented production rule of the form:
If P Then D Unless C, where C (Censor) is an exception to the rule.
Such rules are employed in situations, in which the conditional
statement 'If P Then D' holds frequently and the assertion C holds
rarely. By using a rule of this type we are free to ignore the exception
conditions, when the resources needed to establish its presence are
tight or there is simply no information available as to whether it
holds or not. Thus, the 'If P Then D' part of the CPR expresses
important information, while the Unless C part acts only as a switch
and changes the polarity of D to ~D.
This paper presents a classification algorithm based on evolutionary
approach that discovers comprehensible rules with exceptions in the
form of CPRs.
The proposed approach has flexible chromosome encoding, where
each chromosome corresponds to a CPR. Appropriate genetic
operators are suggested and a fitness function is proposed that
incorporates the basic constraints on CPRs. Experimental results are
presented to demonstrate the performance of the proposed algorithm.
Abstract: In this paper, mathematical models for permutation flow shop scheduling and job shop scheduling problems are proposed. The first problem is based on a mixed integer programming model. As the problem is NP-complete, this model can only be used for smaller instances where an optimal solution can be computed. For large instances, another model is proposed which is suitable for solving the problem by stochastic heuristic methods. For the job shop scheduling problem, a mathematical model and its main representation schemes are presented.
Abstract: Motivated by Microsoft Co. Academic Program
initiative, the department of Information Technology in King Saud
University has adopted Microsoft products in three courses. The
initiative aimed at enhancing the abilities of the university graduates
and equipping them with skills that would help them in the job
market. A number of methods of collecting assessment data were
used to evaluate the course adoption initiative. Assessment data
indicated that the goal of the course adoption is being achieved and
that the students were much better prepared to design applications
and administrate networks.
Abstract: Proteins or genes that have similar sequences are likely to perform the same function. One of the most widely used techniques for sequence comparison is sequence alignment. Sequence alignment allows mismatches and insertion/deletion, which represents biological mutations. Sequence alignment is usually performed only on two sequences. Multiple sequence alignment, is a natural extension of two-sequence alignment. In multiple sequence alignment, the emphasis is to find optimal alignment for a group of sequences. Several applicable techniques were observed in this research, from traditional method such as dynamic programming to the extend of widely used stochastic optimization method such as Genetic Algorithms (GAs) and Simulated Annealing. A framework with combination of Genetic Algorithm and Simulated Annealing is presented to solve Multiple Sequence Alignment problem. The Genetic Algorithm phase will try to find new region of solution while Simulated Annealing can be considered as an alignment improver for any near optimal solution produced by GAs.
Abstract: This research proposes the change of damping coefficient regarding minimum displacement. From the mass with external forced and damper problem, when is the constant external forced transmitted to the understructure in the difference angle between 30 and 60 degrees. This force generates the vibration as general known; however, the objective of this problem is to have minimum displacement. As the angle is changed and the goal is the same; therefore, the damper of the system must be varied while keeping constant spring stiffness. The problem is solved by using nonlinear programming and the suitable changing of the damping coefficient is provided.
Abstract: With the prevalence of computer and development of information technology, Geographic Information Systems (GIS) have long used for a variety of applications in electrical engineering. GIS are designed to support the analysis, management, manipulation and mapping of spatial data. This paper presents several usages of GIS in power utilities such as automated route selection for the construction of new power lines which uses a dynamic programming model for route optimization, load forecasting and optimizing planning of substation-s location and capacity with comprehensive algorithm which involves an accurate small-area electric load forecasting procedure and simulates the different cost functions of substations.
Abstract: This article combines two techniques: data
envelopment analysis (DEA) and Factor analysis (FA) to data
reduction in decision making units (DMU). Data envelopment
analysis (DEA), a popular linear programming technique is useful to
rate comparatively operational efficiency of decision making units
(DMU) based on their deterministic (not necessarily stochastic)
input–output data and factor analysis techniques, have been proposed
as data reduction and classification technique, which can be applied
in data envelopment analysis (DEA) technique for reduction input –
output data. Numerical results reveal that the new approach shows a
good consistency in ranking with DEA.
Abstract: This paper presents a perturbation based search method
to solve the unconstrained binary quadratic programming problem.
The proposed algorithm was tested with some of the standard test
problems and the results are reported for 10 instances of 50, 100, 250,
& 500 variable problems. A comparison of the performance of the
proposed algorithm with other heuristics and optimization software is
made. Based on the results, it was found that the proposed algorithm
is computationally inexpensive and the solutions obtained match the
best known solutions for smaller sized problems. For larger instances,
the algorithm is capable of finding a solution within 0.11% of the
best known solution. Apart from being used as a stand-alone method,
this algorithm could also be incorporated with other heuristics to find
better solutions.
Abstract: In this article, a mathematical programming model
for choosing an optimum portfolio of investments is developed.
The investments are considered as investment projects. The
uncertainties of the real world are associated through fuzzy
concepts for coefficients of the proposed model (i. e. initial
investment costs, profits, resource requirement, and total available
budget). Model has been coded by using LINGO 11.0 solver. The
results of a full analysis of optimistic and pessimistic derivative
models are promising for selecting an optimum portfolio of
projects in presence of uncertainty.
Abstract: One major difficulty that faces developers of
concurrent and distributed software is analysis for concurrency based
faults like deadlocks. Petri nets are used extensively in the
verification of correctness of concurrent programs. ECATNets [2] are
a category of algebraic Petri nets based on a sound combination of
algebraic abstract types and high-level Petri nets. ECATNets have
'sound' and 'complete' semantics because of their integration in
rewriting logic [12] and its programming language Maude [13].
Rewriting logic is considered as one of very powerful logics in terms
of description, verification and programming of concurrent systems.
We proposed in [4] a method for translating Ada-95 tasking
programs to ECATNets formalism (Ada-ECATNet). In this paper,
we show that ECATNets formalism provides a more compact
translation for Ada programs compared to the other approaches based
on simple Petri nets or Colored Petri nets (CPNs). Such translation
doesn-t reduce only the size of program, but reduces also the number
of program states. We show also, how this compact Ada-ECATNet
may be reduced again by applying reduction rules on it. This double
reduction of Ada-ECATNet permits a considerable minimization of
the memory space and run time of corresponding Maude program.
Abstract: In this article, the design of a Supply Chain Network
(SCN) consisting of several suppliers, production plants, distribution
centers and retailers, is considered. Demands of retailers are
considered stochastic parameters, so we generate amounts of data via
simulation to extract a few demand scenarios. Then a mixed integer
two-stage programming model is developed to optimize
simultaneously two objectives: (1) minimization the fixed and
variable cost, (2) maximization the service level. A weighting method
is utilized to solve this two objective problem and a numerical
example is made to show the performance of the model.
Abstract: Our study is concerned with the development of an Emergency Medical Services (EMS) ambulance location and allocation model called the Time-based Ambulance Zoning Optimization Model (TAZ_OPT). This paper presents the framework of the study. The model is formulated using the goal programming (GP), where the goals are to determine the satellite locations of ambulances and the number of ambulances to be allocated at these locations. The model aims at maximizing the expected demand coverage based on probability of reaching the emergency location within targetted time, and minimizing the ambulance busyness likelihood value. Among the benefits of the model is the increased accessibility and availability of ambulances, thus, enhanced quality of the EMS ambulance services.
Abstract: The paper suggests for the first time the use of dynamic programming techniques for optimal risk reduction in the railway industry. It is shown that by using the concept ‘amount of removed risk by a risk reduction option’, the problem related to optimal allocation of a fixed budget to achieve a maximum risk reduction in the railway industry can be reduced to an optimisation problem from dynamic programming. For n risk reduction options and size of the available risk reduction budget B (expressed as integer number), the worst-case running time of the proposed algorithm is O (n x (B+1)), which makes the proposed method a very efficient tool
for solving the optimal risk reduction problem in the railway industry.
Abstract: This paper introduces a mixed integer programming model to find the optimum development plan for port Anzali. The model minimizes total system costs taking into account both port infrastructure costs and shipping costs. Due to the multipurpose function of the port, the model consists of 1020 decision variables and 2490 constraints. Results of the model determine the optimum number of berths that should be constructed in each period and for each type of cargo. In addition to, the results of sensitivity analysis on port operation quantity provide useful information for managers to choose the best scenario for port planning with the lowest investment risks. Despite all limitations-due to data availability-the model offers a straightforward decision tools to port planners aspiring to achieve optimum port planning steps.
Abstract: This article gives a short preview of the new software
created especially for palletizing process in automated production
systems. Each chapter of this article is about problem solving in
development of modules in Java programming language. First part
describes structure of the software, its modules and data flow
between them. Second part describes all deployment methods, which
are implemented in the software. Next chapter is about twodimensional
editor created for manipulation with objects in each
layer of the load and gives calculations for collision control. Module
of virtual reality used for three-dimensional preview and creation of
the load is described in the fifth chapter. The last part of this article
describes communication and data flow between control system of
the robot, vision system and software.
Abstract: In this manuscript, we discuss the problem of determining the optimum stratification of a study (or main) variable based on the auxiliary variable that follows a uniform distribution. If the stratification of survey variable is made using the auxiliary variable it may lead to substantial gains in precision of the estimates. This problem is formulated as a Nonlinear Programming Problem (NLPP), which turn out to multistage decision problem and is solved using dynamic programming technique.