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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.


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. Source


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. Source


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. Source


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. Source


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. Source

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