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.

Assembly line balancing problem is an approach of assigning a set of tasks to an ordered
sequence of workstations. This assignment needs to be made in such a way that the
underlying precedence constraints are not violated and efficiency measures are optimized
subject to the restriction of the cycle time constraint. Research works, reported so far, mainly
deal with the minimization of balancing loss, subject to precedence constraints. Lack of
uniqueness in those optimum solutions and pressing demand to include system loss in the
objective function have led to the present work of minimization of both balancing and system
loss. As there is no standard measure for system loss, the current work proposes a measure
for system loss and offers solution to this bi-objective problem.

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.

In this paper, a single item deterministic inventory model with two separate warehouses called
owned warehouse/show-room (OW) and rented warehouse (RW) is developed. The proposed
model of this paper also considers a realistic assumption regarding the storage capacity of the
rented warehouse. Demand is a function of selling price, advertisement of an item and
displayed inventory level in OW. The stocks of RW are shipped to OW under bulk release
pattern where shortages are not allowed. We discuss different scenarios of the proposed model
to address relative size of stock dependency parameters and the capacity of owned warehouse.
For each scenario, the corresponding problem is formulated as a constrained mixed integer
nonlinear programming problem with three integer and two non-integer variables and a real
coded genetic algorithm (RCGA) is developed to solve the resulted problem. The proposed
model of the paper is also examined using some numerical examples and sensitivity analysis is
performed.

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.

The ordering strategy for a single period inventory model is the key to achieve success in the
competitive business environment. This article considers demand in a form of fuzzy number
and discusses the SPIM in which the retailer has the opportunity to reorder once during the
period. The entire period/season is divided into two slots and the reorder is to be made during
the mid-season after the early-season demand has been observed. The objective is to find the
expected optimal order quantity together with profit maximization. We illustrate the
implementation of the proposed model using a numerical example and explain that the explicit
consideration of this reordering opportunity could lead us to better results in terms of
profitability.

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.

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000–2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.