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Showing 3 results for Fattahi

Parviz Fattahi, Seyed Mohammad Hassan Hosseini, Fariborz Jolai, Azam Dokht Safi Samghabadi,
Volume 25, Issue 1 (IJIEPR 2014)
Abstract

A three stage production system is considered in this paper. There are two stages to fabricate and ready the parts and an assembly stage to assembly the parts and complete the products in this system. Suppose that a number of products of different kinds are ordered. Each product is assembled with a set of several parts. At first the parts are produced in the first stage with parallel machines and then they are controlled and ready in the second stage and finally the parts are assembled in an assembly stage to produce the products. Two objective functions are considered that are: (1) to minimizing the completion time of all products (makespan), and (2) minimizing the sum of earliness and tardiness of all products (∑_i▒(E_i∕T_i ) . Since this type of problem is NP-hard, a new multi-objective algorithm is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with two prominent multi-objective genetic algorithms, i.e. NSGA-II and SPEA-II. The computational results show that performance of the proposed algorithms is good in both efficiency and effectiveness criterions.
Parviz Fattahi, Bahman Ismailnezhad,
Volume 27, Issue 2 (IJIEPR 2016)
Abstract

In this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. Since cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Moreover, full factorial and Taguchi methods are implemented to set crucial parameters in the solutions procedures. Deterministic method of branch and bound (B&B) algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm. The results indicate that proposed algorithms have better performance in quality of the metaheurstic algorithms final answer and solving time compared with the method of Lingo software’s B&B algorithm. The solution of two metaheurstic algorithms is compared by t test. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.


Parviz Fattahi, Sanaz Keneshloo, Fatemeh Daneshamooz, Samad Ahmadi,
Volume 30, Issue 1 (IJIEPR 2019)
Abstract

In this research a jobshop scheduling problem with an assembly stage is studied. The objective function is to find a schedule which minimizes completion time for all products. At first, a linear model is introduced to express the problem. Then, in order to confirm the accuracy of the model and to explore the efficiency of the algorithms, the model is solved by GAMS. Since the job shop scheduling problem with an assembly stage is considered as a NP-hard problem, a hybrid algorithm is used to solve the problem in medium to large sizes in reasonable amount of time. This algorithm is based on genetic algorithm and parallel variable neighborhood search. The results of the proposed algorithm are compared with the result of genetic algorithm. Computational results showed that for small problems, both HGAPVNS and GA have approximately the same performance. And in medium to large problems HGAPVNS outperforms GA.



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