Abstract: In the past, there was a lot of excellent research studies conducted on topics related to supplier selection. Because the considered factors of supplier selection are complicated and difficult to be quantified, most researchers deal supplier selection issues by qualitative approaches. Compared to qualitative approaches, quantitative approaches are less applicable in the real world. This study tried to apply the quantitative approach to study a supplier selection problem with considering operation cost and delivery reliability. By those factors, this study applies Normalized Normal Constraint Method to solve the dual objectives mixed integer program of the supplier selection problem.
Abstract: Moving industries consume numerous resources and dispose masses of used packaging materials. Proper sorting, recycling and disposing the packaging materials is necessary to avoid a sever pollution disaster. This research paper presents a conceptual model to propose sustainable truck rental operations instead of the regular one. An optimization model was developed to select the locations of truck rental centers, collection sites, maintenance and repair sites, and identify the rental fees to be charged for all routes that maximize the total closed supply chain profits. Fixed costs of vehicle purchasing, costs of constructing collection centers and repair centers, as well as the fixed costs paid to use disposal and recycling centers are considered. Operating costs include the truck maintenance, repair costs as well as the cost of recycling and disposing the packing materials, and the costs of relocating the truck are presented in the model. A mixed integer model is developed followed by a simulation model to examine the factors affecting the operation of the model.
Abstract: This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3n enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3n integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results.
Abstract: This study extends the use of the Drainage Area Regionalization (DAR) method in generating synthetic data and calibrating PyTOPKAPI stream yield for an ungauged basin at a daily time scale. The generation of runoff in determining a river yield has been subjected to various topographic and spatial meteorological variables, which integers form the Catchment Characteristics Model (CCM). Many of the conventional CCM models adapted in Africa have been challenged with a paucity of adequate, relevance and accurate data to parameterize and validate the potential. The purpose of generating synthetic flow is to test a hydrological model, which will not suffer from the impact of very low flows or very high flows, thus allowing to check whether the model is structurally sound enough or not. The employed physically-based, watershed-scale hydrologic model (PyTOPKAPI) was parameterized with GIS-pre-processing parameters and remote sensing hydro-meteorological variables. The validation with mean annual runoff ratio proposes a decent graphical understanding between observed and the simulated discharge. The Nash-Sutcliffe efficiency and coefficient of determination (R²) values of 0.704 and 0.739 proves strong model efficiency. Given the current climate variability impact, water planner can now assert a tool for flow quantification and sustainable planning purposes.
Abstract: In this paper, zero-one inflated negative binomial distribution is considered, along with some of its structural properties, then its parameters were estimated using the method of moments. It is found that the method of moments to estimate the parameters of the zero-one inflated negative binomial models is not a proper method and may give incorrect conclusions.
Abstract: In this paper, an encryption algorithm is proposed for real-time image encryption. The scheme employs a dual chaotic generator based on a three dimensional (3D) discrete Lorenz attractor. Encryption is achieved using non-autonomous modulation where the data is injected into the dynamics of the master chaotic generator. The second generator is used to permute the dynamics of the master generator using the same approach. Since the data stream can be regarded as a random source, the resulting permutations of the generator dynamics greatly increase the security of the transmitted signal. In addition, a technique is proposed to mitigate the error propagation due to the finite precision arithmetic of digital hardware. In particular, truncation and rounding errors are eliminated by employing an integer representation of the data which can easily be implemented. The simple hardware architecture of the algorithm makes it suitable for secure real-time applications.
Abstract: We consider a network design problem which has
shortest routing restriction based on the values determined by the
installed facilities on each arc. In conventional multicommodity
network design problem, a commodity can be routed through any
possible path when the capacity is available. But, we consider
a problem in which the commodity between two nodes must be
routed on a path which has shortest metric value and the link
metric value is determined by the installed facilities on the link.
By this routing restriction, the problem has a distinct characteristic.
We present an integer programming formulation containing the
primal-dual optimality conditions to the shortest path routing. We
give some computational results for the model.
Abstract: The fractional–order proportional integral (FOPI) controller tuning rules based on the fractional calculus for the cascade control system are systematically proposed in this paper. Accordingly, the ideal controller is obtained by using internal model control (IMC) approach for both the inner and outer loops, which gives the desired closed-loop responses. On the basis of the fractional calculus, the analytical tuning rules of FOPI controller for the inner loop can be established in the frequency domain. Besides, the outer loop is tuned by using any integer PI/PID controller tuning rules in the literature. The simulation study is considered for the stable process model and the results demonstrate the simplicity, flexibility, and effectiveness of the proposed method for the cascade control system in compared with the other methods.
Abstract: Supplier selection problem is one of the important issues of supply chain problems. Two categories of methodologies include qualitative and quantitative approaches which can be applied to supplier selection problems. However, due to the complexities of the problem and lacking of reliable and quantitative data, qualitative approaches are more than quantitative approaches. This study considers operational cost and supplier’s reliability factor and solves the problem by using a quantitative approach. A mixed integer programming model is the primary analytic tool. Analyses of different scenarios with variable cost and reliability structures show that the effectiveness of this approach to the supplier selection problem.
Abstract: By using fixed point theorems for a class of
generalized concave and convex operators, the positive solution of
nonlinear fractional differential equation with integral boundary
conditions is studied, where n ≥ 3 is an integer, μ is a parameter
and 0 ≤ μ < α. Its existence and uniqueness is proved, and an
iterative scheme is constructed to approximate it. Finally, two
examples are given to illustrate our results.
Abstract: This paper explains the educational timetabling problem, a type of scheduling problem that is considered as one of the most challenging problem in optimization and operational research. The university examination timetabling problem (UETP), which involves assigning a set number of exams into a set number of timeslots whilst fulfilling all required conditions, has been widely investigated. The limitation of available timeslots and resources with the increasing number of examinations are the main reasons in the difficulty of solving this problem. Dynamical change in the examination scheduling system adds up the complication particularly in coping up with the demand and new requirements by the communities. Our objective is to investigate these demands and requirements with subjects taken from Universiti Malaysia Terengganu (UMT), through questionnaires. Integer linear programming model which reflects the preferences obtained to produce an effective examination timetabling was formed.
Abstract: Course timetabling problems occur every semester in a university which includes the allocation of resources (subjects, lecturers and students) to a number of fixed rooms and timeslots. The assignment is carried out in a way such that there are no conflicts within rooms, students and lecturers, as well as fulfilling a range of constraints. The constraints consist of rules and policies set up by the universities as well as lecturers’ and students’ preferences of courses to be allocated in specific timeslots. This paper specifically focuses on the preferences of the course timetabling problem in one of the public universities in Malaysia. The demands will be considered into our existing mathematical model to make it more generalized and can be used widely. We have distributed questionnaires to a number of lecturers and students of the university to investigate their demands and preferences for their desired course timetable. We classify the preferences thus converting them to construct one mathematical model that can produce such timetable.
Abstract: We investigate the large scale of networks in the
context of network survivability under attack. We use appropriate
techniques to evaluate and the attacker-based- and the defenderbased-
network survivability. The attacker is unaware of the operated
links by the defender. Each attacked link has some pre-specified
probability to be disconnected. The defender choice is so that to
maximize the chance of successfully sending the flow to the
destination node. The attacker however will select the cut-set with
the highest chance to be disabled in order to partition the network.
Moreover, we extend the problem to the case of selecting the best p
paths to operate by the defender and the best k cut-sets to target by
the attacker, for arbitrary integers p,k>1. We investigate some
variations of the problem and suggest polynomial-time solutions.
Abstract: A sign pattern is a matrix whose entries belong to the set
{+,−, 0}. An n-by-n sign pattern A is said to allow an eventually
positive matrix if there exist some real matrices A with the same
sign pattern as A and a positive integer k0 such that Ak > 0 for all
k ≥ k0. It is well known that identifying and classifying the n-by-n
sign patterns that allow an eventually positive matrix are posed as two
open problems. In this article, the tree sign patterns of small order
that allow an eventually positive matrix are classified completely.
Abstract: The Com-Poisson (CMP) model is one of the most
popular discrete generalized linear models (GLMS) that handles
both equi-, over- and under-dispersed data. In longitudinal context,
an integer-valued autoregressive (INAR(1)) process that incorporates
covariate specification has been developed to model longitudinal
CMP counts. However, the joint likelihood CMP function is
difficult to specify and thus restricts the likelihood-based estimating
methodology. The joint generalized quasi-likelihood approach
(GQL-I) was instead considered but is rather computationally
intensive and may not even estimate the regression effects due
to a complex and frequently ill-conditioned covariance structure.
This paper proposes a new GQL approach for estimating the
regression parameters (GQL-III) that is based on a single score vector
representation. The performance of GQL-III is compared with GQL-I
and separate marginal GQLs (GQL-II) through some simulation
experiments and is proved to yield equally efficient estimates as
GQL-I and is far more computationally stable.
Abstract: For the last decade, researchers have started to focus
their interest on Multicast Group Key Management Framework. The
central research challenge is secure and efficient group key
distribution. The present paper is based on the Bit model based
Secure Multicast Group key distribution scheme using the most
popular absolute encoder output type code named Gray Code. The
focus is of two folds. The first fold deals with the reduction of
computation complexity which is achieved in our scheme by
performing fewer multiplication operations during the key updating
process. To optimize the number of multiplication operations, an
O(1) time algorithm to multiply two N-bit binary numbers which
could be used in an N x N bit-model of reconfigurable mesh is used
in this proposed work. The second fold aims at reducing the amount
of information stored in the Group Center and group members while
performing the update operation in the key content. Comparative
analysis to illustrate the performance of various key distribution
schemes is shown in this paper and it has been observed that this
proposed algorithm reduces the computation and storage complexity
significantly. Our proposed algorithm is suitable for high
performance computing environment.
Abstract: The agenda of showing the scheduled time for
performing certain tasks is known as timetabling. It is widely used in
many departments such as transportation, education, and production.
Some difficulties arise to ensure all tasks happen in the time and
place allocated. Therefore, many researchers invented various
programming models to solve the scheduling problems from several
fields. However, the studies in developing the general integer
programming model for many timetabling problems are still
questionable. Meanwhile, this thesis describes about creating a
general model which solves different types of timetabling problems
by considering the basic constraints. Initially, the common basic
constraints from five different fields are selected and analyzed. A
general basic integer programming model was created and then
verified by using the medium set of data obtained randomly which is
much similar to realistic data. The mathematical software, AIMMS
with CPLEX as a solver has been used to solve the model. The model
obtained is significant in solving many timetabling problems easily
since it is modifiable to all types of scheduling problems which have
same basic constraints.
Abstract: Concurrent planning of project scheduling and
material ordering can provide more flexibility to the project
scheduling problem, as the project execution costs can be enhanced.
Hence, the issue has been taken into account in this paper. To do so, a
mixed-integer mathematical model is developed which considers the
aforementioned flexibility, in addition to the materials quantity
discount and space availability restrictions. Moreover, the activities
duration has been treated as decision variables. Finally, the efficiency
of the proposed model is tested by different instances. Additionally,
the influence of the aforementioned parameters is investigated on the
model performance.
Abstract: In this article, we deal with a variant of the classical
course timetabling problem that has a practical application in many
areas of education. In particular, in this paper we are interested in
high schools remedial courses. The purpose of such courses is to
provide under-prepared students with the skills necessary to succeed
in their studies. In particular, a student might be under prepared in
an entire course, or only in a part of it. The limited availability
of funds, as well as the limited amount of time and teachers at
disposal, often requires schools to choose which courses and/or which
teaching units to activate. Thus, schools need to model the training
offer and the related timetabling, with the goal of ensuring the
highest possible teaching quality, by meeting the above-mentioned
financial, time and resources constraints. Moreover, there are some
prerequisites between the teaching units that must be satisfied. We
first present a Mixed-Integer Programming (MIP) model to solve
this problem to optimality. However, the presence of many peculiar
constraints contributes inevitably in increasing the complexity of
the mathematical model. Thus, solving it through a general-purpose
solver may be performed for small instances only, while solving
real-life-sized instances of such model requires specific techniques
or heuristic approaches. For this purpose, we also propose a heuristic
approach, in which we make use of a fast constructive procedure
to obtain a feasible solution. To assess our exact and heuristic
approaches we perform extensive computational results on both
real-life instances (obtained from a high school in Lecce, Italy) and
randomly generated instances. Our tests show that the MIP model is
never solved to optimality, with an average optimality gap of 57%.
On the other hand, the heuristic algorithm is much faster (in about the
50% of the considered instances it converges in approximately half of
the time limit) and in many cases allows achieving an improvement
on the objective function value obtained by the MIP model. Such an
improvement ranges between 18% and 66%.
Abstract: This paper presents a rank correlation curve. The
traditional correlation coefficient is valid for both continuous
variables and for integer variables using rank statistics. Since
the correlation coefficient has already been established in rank
statistics by Spearman, such a calculation can be extended to
the correlation curve.
This paper presents two survey questions. The survey
collected non-continuous variables. We will show weak to
moderate correlation. Obviously, one question has a negative
effect on the other. A review of the qualitative literature
can answer which question and why. The rank correlation
curve shows which collection of responses has a positive
slope and which collection of responses has a negative slope.
Such information is unavailable from the flat, ”first-glance”
correlation statistics.