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Showing 3 results for Hosseini-Nasab

Amir-Mohammad Golmohammadi, Mahboobeh Honarvar, Hasan Hosseini-Nasab, Reza Tavakkoli-Moghaddam,
Volume 29, Issue 2 (IJIEPR 2018)

The fundamental function of a cellular manufacturing system (CMS) is based on definition and recognition of a type of similarity among parts that should be produced in a planning period. Cell formation (CF) and cell layout design are two important steps in implementation of the CMS. This paper represents a new nonlinear mathematical programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the proposed model, machines are considered unreliable with a stochastic time between failures. The objective function calculates the costs of inter and intra-cell movements of parts and the cost due to the existence of exceptional elements (EEs), cell reconfigurations and machine breakdowns. Due to the problem complexity, the presented mathematical model is categorized in NP-hardness; thus, a genetic algorithm (GA) is used for solving this problem. Several crossover and mutation strategies are adjusted for GA and parameters are calibrated based on Taguchi experimental design method. The great efficiency of the proposed GA is then demonstrated via comparing with particle swarm optimization (PSO) and the optimum solution via GAMS considering several small/medium and large-sized problems. 

Amir-Mohammad Golmohammadi, Mahboobeh Honarvar, Guangdong Guangdong, Hasan Hosseini-Nasab,
Volume 30, Issue 4 (IJIEPR 2019)

There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.
Hasan Hosseini-Nasab, Hamid Hasanzadeh,
Volume 31, Issue 0 (IJIEPR 2020)

The number of natural disasters and people affected by them has been increasing in recent years. The field of optimization is a significant element of a relief operation and has been extensively studied so far, especially during the last two decades. The design of a relief logistic network as a strategic decision and the relief distribution as an operational decision are the most important activities for disaster operation management before and after a disaster occurs. In the proposed mathematical model, pre-disaster decisions are determined according to the post-disaster decisions in a multi-stage stochastic problem. Then a well-known approach called branch and fixed coordination are applied to optimize the proposed model. The computational results confirm that the proposed approach has proper performance for disaster management in a multi-stage stochastic problem.

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