The selection of appropriate alternative in the industrial environment is an important but, at the same time, a complex and difficult problem because of the availability of a wide range of alternatives and similarity among them. Therefore, there is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations. In this paper, a hybrid decision making method of graph theory and matrix approach (GTMA) and analytical hierarchy process (AHP) is proposed. Three examples are presented to illustrate the potential of the proposed GTMA-AHP method and the results are compared with the results obtained using other decision making methods.

Random variates play key role in any simulation system and there are different algorithms to generate random variates. One of the best algorithms for generating random variates is uniform fractional part algorithm. The algorithm has high performance in terms of efficiency, speed and simplicity. Although the algorithm has useful results, it is an approximate algorithm. In this article, the approximate form of the algorithm has been studied, and some suggestions have also been presented. Through acceptance-rejection approach and hat and squeeze function, the approximate algorithm is transformed to near exact algorithm. The proposed model of this paper has been examined and compared with the traditional one and the preliminary results indicate that it performs better than the other existing algorithms.

In this research, the integrated inventory models are developed for price–sensitive stock– dependent demand and delay in payments are permissible. Two level trade credit police in the vendor–buyer and buyer–customer is considered. An easy–to–use solution algorithm is derived for the integrated models to determine the buyer’s optimal pricing and ordering strategy. A negotiation scenario is incorporated to distribute the extra profit between the vendor and buyer. A numerical example and sensitivity analysis are given to validate the proposed models. It is observed that the total joint profit of the integrated system can increase even if the price discount is offered to the buyer in proposed models.

The hub location problem appears in a variety of applications, including airline systems, cargo delivery systems and telecommunication network design. Hub location problems deal with finding the location of hub facilities and the allocation of demand nodes to these located hub facilities. In this paper, a new model for the capacitated single allocation hub covering location problem is presented. Instead of using capacity constraints to limit the amount of flow received by the hubs, the second objective function is introduced to minimize service times in the hubs. The service time in the hubs includes the waiting time of received flows in a queue and the time to get services. Due to the NP-hardness of the problem, a new weight-based multi-objective imperialist competitive algorithm (MOICA) is designed to find near-optimal solutions. To validate the performance of the proposed algorithm, the solutions obtained by the MOICA are compared by the exact solutions of the mathematical programming model.

This paper discusses the mathematical modeling and performance optimization of CO2 cooling system of a fertilizer plant using genetic algorithm. The fertilizer plant comprises of various systems viz. shell gasification and carbon recovery, desulphurization, co-shift conversion, decarbonation- CO2 cooling, CO2 removal, nitrogen wash and ammonia synthesis, etc. One of the most important functionaries of a fertilizer plant is CO2 cooling system. The CO2 cooling system of a fertilizer plant has five main subsystems, arranged in series. We propose a mathematical model, which considers exponential distribution for the probable failures and repairs. We also use probabilistic approach and derive differential equations based on Markov birth-death process. These equations are then solved using normalizing conditions to determine the steady state availability of the CO2 cooling system. The performance of each subsystem of CO2 cooling system of a fertilizer plant is also optimized using genetic algorithm. The results of the proposed model of this paper is useful to the plant management for the timely execution of proper maintenance decisions and hence to enhance the system performance.

This paper proposes a mathematical model for retailer’s optimal inventory policy for deteriorating items under the conditions of permissible delay in payment in supply chain coordination. In this paper, supplier offers full trade credit to his/her retailer and the retailer, in turn, provides the partial trade credit to his/her customer. This research article helps the retailer determine the optimal cost, more accurately. The primary objective is to minimize the retailer’s cost and to determine the optimal inventory policy for retailer. The results of this article generalize some already published results by incorporating deterioration on inventory items. We use some numerical examples to analyze the effects of various parameters on retailer’s inventory policy.

In this paper, a multi products single machine economic production quantity model with
discrete delivery is developed. A unique cycle length is considered for all produced items with
an assumption that all products are manufactured on a single machine with a limited capacity.
The proposed model considers different items such as production, setup, holding, and
transportation costs. The resulted model is formulated as a mixed integer nonlinear
programming model. Harmony search algorithm, extended cutting plane and particle swarm
optimization methods are used to solve the proposed model. Two numerical examples are used
to analyze and to evaluate the performance of the proposed model.

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.