Abstract: In this paper, a multi products single machine economic order 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.

Abstract: 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.

Abstract: 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.

Abstract: 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) methods to find the cardinality constrained efficient frontier. The proposed algorithm benefits 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.

Abstract: This article develops a supply chain coordination model with a single-vendor and a single-buyer. The vendor manufactures the product in lots and delivers to the buyer in equal shipments. However, 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 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.

Abstract: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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: One of the important factors for the success of a bank industry is to monitor their customers' behavior and their point-of-sale (POS). The bank needs to know its merchants' 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In this paper, an artificial neural network (ANN) model is developed to determine the optimum level of finished goods inventory as a function of product demand, setup, holding, and material costs. The model selects a feed-forward back-propagation ANN with four inputs, ten hidden neurons and one output as the optimum network. The model is tested with a manufacturing industry data and the results indicate that the model can be used to forecast finished goods inventory level in response to the model parameters. Overall, the model can be applied for optimization of finished goods inventory for any manufacturing enterprise in a competitive business environment.

Abstract: 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.