JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL
Year:2007 | Volume:3 | Issue:4|Papers Count:8

Vendor selection decisions are complicated by the fact that various conflicting multi-objective factors must be considered in the decision making process. The problem of vendor selection becomes still more complicated with the inclusion of incremental discount pricing schedule. Such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. This paper proposes a multi-component multiple vendor selection model with vendors offering quantity discounts. This problem is then evaluated using Genetic Algorithm with a case study approach. Combinatorial approach is used to group the vendors for selection and Genetic Algorithm to allocate the optimal order quantities for each vendor. 

There are different strategies for selecting stocks, and different investors use different strategies according to their risk tolerance or their expected rate of return. In this study, the profitability of a broad range of stock selection strategies in Tehran Stock Exchange over the period 1370-1383, has been examined, and it has been investigated whether the successful strategies in other countries are also successful in Iran or not. Although a lot of comprehensive studies have been done in the developed and in a considerable number of emerging markets, and successful strategies have been well documented in those countries, such studies have never been done in Tehran Stock Exchange. The sample is all the companies in Tehran Stock Exchange in the aforementioned period. Also, in order to evaluate different strategies, various portfolios have been formed for each year according to each strategy. Then, computing the return of winner portfolios, those strategies generating the maximum return in excess of market return, are presented. The evaluation of the performance of the strategies has been done regarding various diagnostics criteria like risk and return. The results show that value strategy is the most successful strategy in Iran and generates significant excess return, in contrast to growth, size, price momentum and fundamental strategies. In other words, the most successful strategy in Iran is the multivariate strategy which selects the stocks with high E/P, B/P, C/P, S/P and D/E. Moreover, as apposed to the developed markets and a considerable number of emerging markets, size and momentum strategies are not profitable ones in Tehran Stock Exchange and can not distinguish between profitable and unprofitable stocks.

In this paper, Shortest Path Design Problem (SPDP) in which the path is incident to all cells is considered. The bi-directional path is one of the known types of configuration of networks for Automated Guided Vehicles (AGV). To solve this problem, two algorithms are developed. For each algorithm an Integer Linear Programming (ILP) is determined. The objective functions of both algorithms are to find the shortest path. The path must be connected and incident to all cells at least in one edge or node. A simple Branch-and-Cut approach is used to solve the ILP models. Computational results show that the models easily can solve the problem with less than 45 cells using a commercial ILP solver.

Internet Traffic has grown exponentially over last few years due to provision of multiple class services through Internet backbone. With the explosive use of Internet, contemporary Internet routers are susceptible to overloads and their services deteriorate drastically and often cause denial of services. In this paper, an analysis is made how forecasting technique, routing algorithm and Genetic algorithm can be simultaneously applied for solving a multi-constrained routing problem in Quality of service (QoS) traffic in Internet. Also, a model is suggested for solving the above-mentioned problem. Simulation results show that the throughput of the given network is enhanced by implementing the model. It can also be seen that the average delay of packets flowing through the network comes down when the proposed model is employed for the network.

In today's dynamic market, organizations must be adaptive to market fluctuations. In addition, studies show that material-handling cost makes up between 20 and 50 percent of the total operating cost. Therefore, this paper considers the problem of arranging and rearranging, when there are changes in product mix and demand, manufacturing facilities such that the sum of material handling and rearrangement costs is minimized. This problem is called the dynamic plant layout problem (DPLP). In this paper, the authors develop a multi-start simulated annealing for DPLP. To compare the performance of meta-heuristics, data sets taken from literature are used in the comparison.

Nowadays, One-Dimensional Cutting Stock Problem (1D-CSP) is used in many industrial processes and recently has been considered as one of the most important research topic. In this paper, a metaheuristic algorithm based on the Simulated Annealing (SA) method is represented to minimize the trim loss and also to focus the trim loss on the minimum number of large objects. In this method, the ID-CSP is taken into account as Item-oriented and the authors have tried to minimize the trim loss concentration by using the simulated annealing algorithm and also defining a virtual cost for the trim loss of each stock. The solved sample problems show the ability of this algorithm to solve the ID-CSP in many cases.

Multivariate control charts such as Hotelling's T2 and X2 are commonly used for monitoring several related quality characteristics. These control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. The purpose of this article is to discuss some issues related to the G chart proposed by Levinson et al. [9] for detecting shifts in the process variance-covariance matrix. They use a G statistic which is distributed as a chi-square with p(p+1)/2 degrees of freedom where p denotes the number of variables under study. The authors show through simulation that the chi-square distribution only holds for certain cases. The results could be important to practitioners who use G chart for monitoring purposes.

This paper proposes a new approach for determining efficient DMUs in DEA models using inverse optimization and without solving any LPs. It is shown that how a two-phase algorithm can be applied to detect efficient DMUs. It is important to compare computational performance of solving the simultaneous linear equations with that of the LP, when computational issues and complexity analysis are at focus.