Qazvin, Iran

Qazvin Islamic Azad University is a private research university based in Qazvin, Iran. It is one of the largest and most reputable branches of the Islamic Azad University.Sometimes, QIAU is referred by other phrases: Islamic Azad University of Qazvin or Islamic Azad University, Ghazvin branch. Wikipedia.

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Mohtashami A.,Islamic Azad University at Qazvin
Computers and Industrial Engineering | Year: 2015

An important factor for efficiently managing the supply chain is to efficiently control the physical flow of the supply chain. For this purpose, many companies try to use efficient methods to increase customer satisfaction and reduce costs. Cross docking is a good method to reduce the warehouse space requirements, inventory management costs, and turnaround times for customer orders. This paper proposes a novel dynamic genetic algorithm-based method for scheduling vehicles in cross docking systems such that the total operation time is minimized. In this paper, it is assumed that a temporary storage is placed at the shipping dock and inbound vehicles are allowed to repeatedly enter and leave the dock to unload their products. In the proposed method of this paper two different kinds of chromosome for inbound and outbound trucks are proposed. In addition, some algorithms are proposed including initialization, operational time calculation, crossover and mutation for inbound and outbound trucks, independently. Moreover a dynamic approach is proposed for performing crossover and mutation operation in genetic algorithm. In order to evaluate the performance of the proposed algorithm of this paper, various examples are provided and analyzed. The computational results reveal that the proposed algorithm of this paper performs better than two well-known works of literature in providing solutions with shorter operation time. © 2015 Elsevier Ltd. All rights reserved.

Pourvaziri H.,Islamic Azad University at Qazvin | Naderi B.,Kharazmi University
Applied Soft Computing Journal | Year: 2014

Due to inherent complexity of the dynamic facility layout problem, it has always been a challenging issue to develop a solution algorithm for this problem. For more than one decade, many researchers have proposed different algorithms for this problem. After reviewing the shortcomings of these algorithms, we realize that the performance can be further improved by a more intelligent search. This paper develops an effective novel hybrid multi-population genetic algorithm. Using a proposed heuristic procedure, we separate solution space into different parts and each subpopulation represents a separate part. This assures the diversity of the algorithm. Moreover, to intensify the search more and more, a powerful local search mechanism based on simulated annealing is developed. Unlike the available genetic operators previously proposed for this problem, we design the operators so as to search only the feasible space; thus, we save computational time by avoiding infeasible space. To evaluate the algorithm, we comprehensively discuss the parameter tuning of the algorithms by Taguchi method. The perfectly tuned algorithm is then compared with 11 available algorithms in the literature using well-known set of benchmark instances. Different analyses conducted on the results, show that the proposed algorithm enjoys the superiority and outperformance over the other algorithms. © 2014 Elsevier B.V.

Rahmati S.H.A.,Islamic Azad University at Qazvin | Zandieh M.,Shahid Beheshti University
International Journal of Advanced Manufacturing Technology | Year: 2012

Biogeography-based optimization (BBO) algorithm is a new kind of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. In this paper, the BBO algorithm is developed for flexible job shop scheduling problem (FJSP). It means that migration operators of BBO are developed for searching a solution area of FJSP and finding the optimum or near-optimum solution to this problem. In fact, the main aim of this paper was to provide a new way for BBO to solve scheduling problems. To assess the performance of BBO, it is also compared with a genetic algorithm that has the most similarity with the proposed BBO. This similarity causes the impact of different neighborhood structures being minimized and the differences among the algorithms being just due to their search quality. Finally, to evaluate the distinctions of the two algorithms much more elaborately, they are implemented on three different objective functions named makespan, critical machine work load, and total work load of machines. BBO is also compared with some famous algorithms in the literature. © 2011 Springer-Verlag London Limited.

Vahdani B.,Islamic Azad University at Qazvin
Applied Soft Computing Journal | Year: 2014

Numerous manufacturing companies are taking advantage of material handling systems due to their flexibility, reliability, safety and contribution to the increase of productivity. However, several uncertain parameters such as types of cost, availability of vehicle etc., influence the performance of the material handling system greatly. In recent years, robust optimization has proven to be an effective methodology permitting overcoming uncertainty in optimization models. Robust optimization models work well even when probabilistic knowledge of the phenomenon is incomplete. This paper thus proposes two new zero-one programming (ZOP) models for vehicle positioning in multi-cell automated manufacturing system. Uncertain parameters in these models include cost parameters, travel time between each pair of centers of cells and location of machines, average time required for performing all transports from location of machines and availability of the vehicle. Then, the robust counterpart of the proposed ZOP models is presented by using the recent extensions in robust optimization theory. Eventually, to verify the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic ZOP model for different test problems. © 2014 Elsevier B.V.

Vahdani B.,Islamic Azad University at Qazvin | Zandieh M.,Shahid Beheshti University
Computers and Industrial Engineering | Year: 2010

Cross-docking is a logistics technique that minimizes the storage and order picking functions of a warehouse while still allowing it to serve its receiving and shipping functions. The idea is to transfer shipments directly from incoming to outgoing trailers without storage in between. In this paper we apply five meta-heuristic algorithms: genetic algorithm (GA), tabu search (TS), simulated annealing (SA), electromagnetism-like algorithm (EMA) and variable neighbourhood search (VNS) to schedule the trucks in cross-dock systems such that minimize total operation time when a temporary storage buffer to hold items temporarily is located at the shipping dock. A design procedure is developed to specify and adjust significant parameters for GA, TS, SA, EMA and VNS. The proposed procedure is based on the response surface methodology (RSM). Two different types of objective functions are considered to develop multiple objective decision making model. For the purpose of comparing meta-heuristics, makespan and CPU time are considered as two response variables representing effectiveness and efficiency of the algorithms. Based on obtained results, VNS is recommended for scheduling trucks in cross-docking systems. Also, since for real size problems, it is not possible to reach optimum solution, a lower bound is presented to evaluate the resultant solutions. © 2009.

Mohtashami A.,Islamic Azad University at Qazvin
International Journal of Advanced Manufacturing Technology | Year: 2014

This paper proposes a multi-objective mathematical formulation and a hybrid approach to solve buffer sizing and machine allocation problems simultaneously in unreliable production and assembly lines. This paper unlike prior researches assumes that time-dependent parameters of production systems are generally distributed (e.g., uniform, normal, gamma, etc.) and not only deterministic or exponential. This paper proposes a multi-objective mixed binary integer non-linear mathematical model to solve the problem of buffer sizing and machine allocation. The proposed mathematical model is capable of purchasing new machines (candidate) and also selling old machines (current available). In other words, this model compares the candidate machines to current available machines in each station based on different aspects and is capable to replace the current machines with candidate machines or to sell some of the current machines without replacement. To solve the mentioned problem, a new formulation for dealing with multi-objectiveness of the problem is proposed. This formulation generates a series of non-dominated solutions, and also, it is capable of generating a non-dominated solution between two adjacent non-dominated solutions determined by decision maker. A hybrid genetic algorithm (HGA) with a new dynamic mutation probability is proposed to solve the model. Since the proposed mathematical model and the proposed solution method are novel, the proposed HGA is compared to simple genetic algorithm and non-dominated sorting genetic algorithm (NSGA-II). The computational results indicate the effectiveness of the proposed HGA. © 2014, Springer-Verlag London.

Mohtashami A.,Islamic Azad University at Qazvin
Applied Soft Computing Journal | Year: 2014

This paper proposes a new method to derive the priority vector from fuzzy pairwise comparison matrices. Unlike several known methods, the proposed method derives crisp weights from consistent and inconsistent fuzzy comparison matrices. Therefore, the crisp weights obviate the need of additional aggregation and ranking procedures. To derive the priority vector, a Modified Fuzzy Logarithmic Least Square Model (MFLLSM) is proposed. In order to solve the MFLLSM, a framework based on genetic algorithm is proposed. In the proposed framework, a heuristic algorithm of population initialization, a heuristic algorithm for simulating fuzzy numbers and a heuristic algorithm of fitness evaluation are proposed. The solution of the prioritization problem requires finding priorities such that their ratio approximately satisfies the initial judgments. Computational results reveal the superiority of the proposed method in comparison with five well known methods of literature from the viewpoint of satisfaction of initial judgments by the obtained priority vector. It is shown by ten different examples that the deviation of the priorities ratio from initial judgments in the proposed method is less than five existing methods of literature. In addition, unlike several methods of literature, the proposed method considers fuzzy judgments represented by both triangular and trapezoidal fuzzy numbers. Furthermore, the proposed method for the first time considers judgments represented by triangular shaped fuzzy numbers and trapezoidal shaped fuzzy numbers which are discussed in the paper. © 2014 Elsevier B.V.

Hosseini M.-S.,Islamic Azad University at Qazvin | Eftekhari-Moghadam A.-M.,Islamic Azad University at Qazvin
Applied Soft Computing Journal | Year: 2013

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach. © 2012 Elsevier B.V. All rights reserved.

Malvandi A.,Islamic Azad University at Qazvin | Ganji D.D.,Babol Noshirvani University of Technology
European Journal of Mechanics, B/Fluids | Year: 2015

The present paper is a theoretical investigation on effects of nanoparticle migration and asymmetric heating on forced convective heat transfer of alumina/water nanofluid in microchannels in presence of a uniform magnetic field. Walls are subjected to different heat fluxes; qt″ for top wall and qb″ for bottom wall, and because of non-adherence of the fluid-solid interface due to the microscopic roughness in microchannels, Navier's slip boundary condition is considered at the surfaces. A two-component heterogeneous mixture model is used for nanofluid with the hypothesis that Brownian motion and thermophoretic diffusivities are the only significant slip mechanisms between solid and liquid phases. Assuming a fully developed flow and heat transfer, the basic partial differential equations including continuity, momentum, and energy equations have been reduced to two-point ordinary boundary value differential equations and solved numerically. It is revealed that nanoparticles eject themselves from heated walls, construct a depleted region, and accumulate in the core region, but more likely to accumulate near the wall with lower heat flux. Also, the non-uniform distribution of nanoparticles causes velocities to move toward the wall with a higher heat flux and enhances heat transfer rate there. In addition, inclusion of nanoparticles in a very strong magnetic field and slip velocity at the walls has a negative effect on performance. © 2015 Elsevier Masson SAS. All rights reserved.

Naderi B.,Islamic Azad University at Qazvin | Salmasi N.,Sharif University of Technology
European Journal of Industrial Engineering | Year: 2012

This paper focuses on the flow shop sequence dependent group scheduling (FSDGS) problem with minimisation of total completion time as the criterion (Fm|f mls, prmu, S plk|ΣC j). The research problem is formulated in form of two different mixed integer linear programming (MILP) models. Comparing with the latest MILP model for the proposed problem in the literature, the complexity size of the proposed models are significantly reduced. One of the proposed mathematical models is so effective that even medium-sized instances (problems up to 60 jobs in all groups) are solved to optimality in a reasonable amount of time. Moreover, a metaheuristic hybridising genetic and simulated annealing algorithm, called GSA, is proposed to solve the problems heuristically. All the results and analyses show the high performance of the proposed mathematical models as well as the proposed metaheuristic algorithm compared to the available ones in literature. Copyright © 2012 Inderscience Enterprises Ltd.

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