The problem of portfolio optimization has always been a key concern for investors. This paper addresses a realistic portfolio optimization problem with floor, ceiling, and cardinality constraints. This problem is a mixed integer quadratic programming where traditional optimization methods fail to find the optimal solution, efficiently. The present paper develops a new hybrid approach based on an improved particle swarm optimization (PSO) and a modified simulated annealing (SA) to find the cardinality constrained efficient frontier. The proposed algorithm benefits from simple and easy characteristics of PSO with an adaptation of inertia weights and constriction factor. In addition, incorporating an SA procedure into IPSO helps escaping from local optima and improves the precision of convergence. Computational results on benchmark problems with up to 225 assets signify that our proposed algorithm exceeds not only the standard PSO but also the other heuristic algorithms previously presented to solve the cardinality constrained portfolio problem.

Performance evaluation plays an important role in determining faults and difficulties of any
organization as well as attempting to increase capabilities and improve activities. Data
envelopment analysis (DEA), as a non-parametric method, has been one of the most important
and significant management tools for measuring output or efficiency. In this paper, we propose
a method to utilize balanced score card (BSC) as a tool for designing performance evaluation
indices of an organization. The integrated BSC-DEA has been applied as an empirical case for
a major private bank organization and the results are analyzed.

Job shop scheduling (JSS) problem has been one of the most interesting research issues
in the literature during the recent years. JSS problem has been studied in different forms
of deterministic, fuzzy, and stochastic at different depths. The idea of robust
optimization (ROP), on the other hand, has earned a particular value to become a
popular subject of the breakthrough for problem solving affairs amongst the researchers.
Based on the emerged opportunity for illustrating a new area of search, a robust JSS
problem is proposed as a challenge to this boundary of knowledge. The proposed
method is capable of handling the perturbation which exists amongst the processing
times. In fact, in many real world job scheduling problems, a small change in the
processing times, not only causes a non-optimal solution, but also the infeasibility of the
final solution may also occur. The proposed robust method could guarantee that, a small
deviation of the processing times does not affect the feasibility. The implementation of
the proposed method is illustrated using some numerical examples and the outcomes of
the investigation are discussed

In this paper, we consider a multi-period integrated supplier selection and order lot
sizing problem where a single buyer plans to purchase a single product in multiple
periods from several qualified suppliers who are able to provide the required product
with the needed quality in a timely manner. Product price and order cost differs
among different suppliers. Buyer’s demand for the product is deterministic and varies
for different time periods. The problem is to determine how much product from which
supplier must be ordered in each period such that buyer’s demand is satisfied without
violating some side constraints. We have developed a mathematical programming
model to deal with this problem, and proposed a forward dynamic programming
approach to obtain optimal solutions in reasonable amount of time even for large scale
problems. Finally, a numerical example is conducted in which solutions obtained from
the proposed dynamic programming algorithm is compared with solutions from the
branch-and-bound algorithm. Through the numerical example we have shown the
efficiency of our algorithm.

One of the important factors for the success of a bank industry is to monitor their customers & apos;
behavior and their point-of-sale (POS). The bank needs to know its merchants & apos; behavior to find
interesting ones to attract more transactions which results in the growth of its income and
assets. The recency, frequency and monetary (RFM) analysis is a famous approach for
extracting behavior of customers and is a basis for marketing and customer relationship
management (CRM), but it is not aligned enough for banking context. Introducing RF*M* in
this article results in a better understanding of groups of merchants. Another artifact of RF*M*
is RF*M* scoring which is applied in two ways, preprocessing the POSs and assigning
behavioral meaningful labels to the merchants’ segments. The class labels and the RF*M*
parameters are entered into a rule-based classification algorithm to achieve descriptive rules of
the clusters. These descriptive rules outlined the boundaries of RF*M* parameters for each
cluster. Since the rules are generated by a classification algorithm, they can also be applied for
predicting the behavioral label and scoring of the upcoming POSs. These rules are called
behavioral rules.

The permutation method of multiple attribute decision making has two significant deficiencies:
high computational time and wrong priority output in some problem instances. In this paper, a
novel permutation method called adjusted permutation method (APM) is proposed to
compensate deficiencies of conventional permutation method. We propose Tabu search (TS)
and particle swarm optimization (PSO) to find suitable solutions at a reasonable computational
time for large problem instances. The proposed method is examined using some numerical
examples to evaluate the performance of the proposed method. The preliminary results show
that both approaches provide competent solutions in relatively reasonable amounts of time
while TS performs better to solve APM.

In this paper, we establish an economic production quantity (EPQ) based inventory model by
considering various types of non-perfect products. We classify products in four groups of
perfect, imperfect, defective but reworkable and non-reworkable defective items. The demand
is a power function of price and marketing expenditure and production unit cost is considered to
be a function of lot size. The objective of this paper is to determine lot size, marketing
expenditure, selling price, set up cost and inventory holding cost, simultaneously. The problem
is modeled as a nonlinear posynomial geometric programming and an optimal solution is
derived. The implementation of the proposed method is demonstrated using a numerical
example and the sensitivity analysis is also performed to study the behavior of the model.

At the computational point of view, a fuzzy system has a layered structure, similar to an
artificial neural network (ANN) of the radial basis function type. ANN learning algorithms can
be employed for optimization of parameters in a fuzzy system. This neuro-fuzzy modeling
approach has preference to explain solutions over completely black-box models, such as ANN.
In this paper, we implement the design of experiment (DOE) technique to identify the
significant parameters in the design of adaptive neuro-fuzzy inference systems (ANFIS) for
stock price prediction.

This is a study on the effects of multilevel selection (MLS) theory in optimizing numerical
functions. Based on this theory, a Multilevel Evolutionary Optimization algorithm (MLEO) is
presented. In MLEO, a species is subdivided in cooperative populations and then each
population is subdivided in groups, and evolution occurs at two levels so called individual and
group levels. A fast population dynamics occurs at individual level. At this level, selection
occurs among individuals of the same group. The popular genetic operators such as mutation
and crossover are applied within groups. A slow population dynamics occurs at group level. At
this level, selection happens among groups of a population. The group level operators such as
regrouping, migration, and extinction-colonization are applied among groups. In regrouping
process, all the groups are mixed together and then new groups are formed. The migration
process encourages an individual to leave its own group and move to one of its neighbour
groups. In extinction-colonization process, a group is selected as extinct, and replaced by
offspring of a colonist group. In order to evaluate MLEO, the proposed algorithms were used
for optimizing a set of well known numerical functions. The preliminary results indicate that
the MLEO theory has positive effect on the evolutionary process and provide an efficient way
for numerical optimization.

The maximal covering location problem (MCLP) seeks to locate a predefined number of
facilities in order to maximize the number of covered demand points. In a classical sense,
MCLP has three main implicit assumptions: all or nothing coverage, individual coverage, and
fixed coverage radius. By relaxing these assumptions, three classes of modelling formulations
are extended: the gradual cover models, the cooperative cover models, and the variable radius
models. In this paper, we develop a special form of MCLP which combines the characteristics
of gradual cover models, cooperative cover models, and variable radius models. The proposed
problem has many applications such as locating cell phone towers. The model is formulated as
a mixed integer non-linear programming (MINLP). In addition, a simulated annealing
algorithm is used to solve the resulted problem and the performance of the proposed method is
evaluated with a set of randomly generated problems.