JOURNAL OF OPTIMIZATION IN INDUSTRIAL ENGINEERING (JOURNAL OF INDUSTRIAL ENGINEERING)
Year:2015 | Volume:8 | Issue:17|Papers Count:8

A multi objective Honey Bee Mating Optimization (HBMO) designed by online learning mechanism is proposed in this paper to optimize the double Fuzzy-Lead-Lag (FLL) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. The proposed double FLL stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the proposed multi objective optimization process. A multilayer adaptive network is employed to design the fuzzy logic controller with selflearning capability that does not require another controller to tune the fuzzy inference rules and membership functions. In the proposed online learning algorithm, two artificial neural networks are employed which this system makes the FLL stabilizer adaptive to changes in the operating conditions. Therefore, variation in the power system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter conventional controller. The effectiveness of the proposed stabilizer has been employed by simulation studies. The effectiveness of the proposed stabilizer is demonstrated on Two-Area Four-Machine (TAFM) power system under different loading conditions.

Centralizing and using proper transportation facilities cut down costs and traffic. Hub facilities concentrate on flows to cause economic advantage of scale and multimodal transportation helps use the advantage of another transporter. A distinctive feature of this paper is proposing a new mathematical formulation for a three-stage p-hub location routing problem with simultaneous pick-ups and deliveries on time. A few studies have been devoted to this problem; however, many people are still suffering from the problems of commuting in crowded cities. The proposed formulation controlled the tumult of each node by indirect fixed cost. Node-to-node traveling cost was followed by a vehicle routing problem between nodes of each hub. A couple of datasets were solved for small and medium scales by GAMS software. But, for large-scale instances, a meta-heuristic algorithm was proposed. To validate the model, datasets were used and the results demonstrated the performance suitability of the proposed algorithm.

The recent years have witnessed an increasing attention to the methods of multiple attribute decision making in solving the problems of the real world due to their shorter time of calculation and easy application. One of these methods is the ‘permutation method’ which has a strong logic in connection with ranking issues, but when the number of alternatives increases, solving problems through this method becomes NP-hard. So, meta-heuristic algorithm based on Tabu search is used to find optimum or near optimum solutions at a reasonable computational time for large size problems. This research is an attempt to apply the ‘permutation method’ to rank some countries of the West Asia and the North Africa based on the development criteria. Knowing the situation of each country as compared with other countries, particularly the respective neighbouring countries, is one of the most important standards for the assessment of performance and planning for the future activities.

In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the proposed approach suggests using an acceptable range for missing inputs and outputs, which is determined by the decision maker (DM). Then, applying the least favourable bounds of missing data along with using the proposed range is suggested in estimating the production frontier. A data set is used to illustrate the approach.

In order to implement the cellular manufacturing system in practice, some essential factors should be taken into account. In this paper, a new mathematical model for cellular manufacturing system considering different production factors including alternative process routings and machine reliability with stochastic arrival and service times in a dynamic environment is proposed. Also because of the complexity of the given problem, a Benders’ decomposition approach is applied to solve the problem efficiently. In order to verify the performance of proposed approach, some numerical examples are generated randomly in hypothetical limits and solved by the proposed solution approach. The comparison of the implemented solution algorithm with the conventional mixed integer linear and mixed integer non linear models verifies the efficiency of Benders’ decomposition approach especially in terms of computational time.

In this paper, a state feedback H¥ controller is proposed in order to design an active queue management (AQM) system based on congestion control algorithm for networks supporting TCP protocols. In this approach, the available link bandwidth is modeled as a timevariant disturbance. The purpose of this paper is to design a controller which is capable of achieving the queue size and can guarantee asymptotic stability in the presence of disturbance. An important feature of the proposed approach is that the performance of system, including the disturbance rejection and stability of closed-loop system, are guaranteed for all round-trip times that are less than a known value. The controller design is formulated in the form of some linear matrix inequalities, which can be efficiently solved numerically. The simulation results demonstrate the effectiveness of the proposed methods in comparison with the conventional methods.

Project selection is considered as an important problem in project management. It is multi-criteria in nature and is based on various quantitative and qualitative factors. The main purpose of this paper is to present a new rank-based method for project selection in outranking relation. According to this approach, decision alternatives were clustered in the concordance matrix and the discordance matrix through the ELECTRE model based on intuitionistic trapezoidal fuzzy numbers. Then, the two matrices were integrated and ranked using grey relational coefficients and the Minkowski space distance. The results of the model were compared with grey relational projection method with intuitionistic trapezoidal fuzzy number. To illustrate the proposed methodology, a case study was conducted to select National Iranian Oil Company projects.

Considering the increasingly high attention to quality, promoting the reliability of products during designing process has gained significant importance. In this study, we consider one of the current models of the reliability science and propose a non-linear programming model for redundancy allocation in the series-parallel systems according to the redundancy strategy and considering the assumption that the failure rate depends on the number of the active elements. The purpose of this model is to maximize the reliability of the system. Internal connection costs, which are the most common costs in electronic systems, are used in this model in order to reach the real-world conditions. To get the results from this model, we used meta-heuristic algorithms such as genetic algorithm and simulation annealing after optimizing their operators’ rates by using response surface methodology.