Portfolio selection is one of the important problems encountered by any investor. The purpose of this paper is to solve a real stock portfolio selection problem in Iran. According to the uncertain environments in which financial decisions are made, most of the recent works in this field use fuzzy sets theory in order to incorporate these uncertainties into their analysis. The problem is to determine how to allocate a limited fund among the stocks of some pharmaceutical companies in Tehran stock exchange. For this purpose we apply two fuzzy analytic hierarchy process (FAHP) methods to this problem. Finally, the results obtained from the two methods are compared in terms of the solution quality.

In the classical inventory models, most of the time the issue of quality has not been considered.
However, in realistic environment, it can be observed that there may be some defective items in
an ordered lot, because of these defective items retailer incurs additional cost due to rejection,
repair and refund etc. Thus, inspection/screening of lot becomes indispensible in most of the
organizations. Moreover, it plays a very essential role when items are of deteriorating in nature.
Further, it is generally assumed that payment will be made to the supplier for the goods
immediately after receiving the consignment. Whereas, in practice, supplier does offers a
certain fixed period to the retailer for settling the account. During this period, supplier charges
no interest, but beyond this period interest is being charged as has been agreed upon. On the
other hand, retailer can earn interest on the revenue generated during this period. Keeping this
scenario in mind, an attempt has been made to formulate an inventory model for deteriorating
items with imperfect quality under permissible delay in payments. Results have been validated
with the help of a numerical example using Matlab7.0.1. Comprehensive sensitivity analysis
has also been presented.

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.

This article develops a supply chain coordination model with a single-vendor and a singlebuyer.
The vendor manufactures the product in lots and delivers to the buyer in equal
shipments. The vendor’s production process is not perfectly reliable. During a production run,
the process may shift from an in-control state to an out-of-control state at any random time and
produces some defective items. The buyer whose demand is assumed to be a linear function of
the on-hand inventory performs a screening process immediately after each replenishment.
Moreover, the buyer’s inventory is deteriorated at a constant rate over time. The vendor-buyer
coordination policy is determined by minimizing the average cost of the supply chain. It is
observed from the numerical study that channel coordination earns significant cost savings over
the non-coordinated policy.

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.

Lot streaming is a technique of splitting production lots into smaller sublots in a multi-stage
manufacturing systems so that operations of a given lot can be overlapped. This technique can
reduce manufacturing makespan and is an effective tool for time-based manufacturing strategy.
Several research articles appeared in literature to solve this problem and most of these studies
are limited to pure flowshop environments where there is only a single machine in each stage.
On the other hand, because of the applicability of hybrid flowshops in different manufacturing
settings, the scheduling of these types of shops is also extensively studied by several authors.
However, the issue of lot streaming in hybrid flowshop environment is not well studied. In this
paper, we aim to initiate research in bridging the gap between the research efforts in flowshop
lot streaming and hybrid flowshop scheduling. We present a comprehensive mathematical
model for scheduling flexible hybrid flowshop with lot streaming. Numerical example
demonstrated that lot streaming can result in larger makespan reduction in hybrid flowshop
where there is a limited research than in pure flowshop where research is abundant.

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

This paper presents an order level inventory system with time
dependent Weibull deterioration and ramp type demand rate where
production and demand are time dependent. The proposed model of this
paper considers economic order quantity under two different cases. The
implementation of the proposed model is illustrated using some
numerical examples. Sensitivity analysis is performed to show the
effect of changes in the parameters on the optimum solution.

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.