Kharazmi University

About: Kharazmi University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Membrane & Supply chain. The organization has 3395 authors who have published 5321 publications receiving 45645 citations. The organization is also known as: Tarbiat Moallem University of Tehran & Teacher Training University.

On the spatiality of geographic knowledge

Abstract: The present paper aims to explore how the principle of “spatiality” provides internal consistency and intrinsic unity to the science of geography. The main idea is that geography as a science has a...

Migraine headache and depression in adults: a systematic Review and Meta-analysis

Abstract: Migraines have adverse psychological consequences, one of which is the tendency towards depression. This study aimed to investigate the effects of migraine on adults’ depression in the form of a systematic review and meta-analysis Several scientific databases were searched using relevant keywords until the end of January 2018. After reviewing the titles and abstracts and the full texts, the researchers eventually included 16 eligible articles. Major and sub-group analyses were carried out using random effects. Publication bias was also measured. Pooled Odd Ratio (PR) = 1.95; and 95% confidence interval (CI) = CI = 1.61–2.35 were obtained in studying the effects of migraine on depression. This effect was equal to OR = 1.81 (95% CI = 1.20–2.72) in cohort studies, and OR = 2.00; 95% CI = 1.64–2.43 in cross sectional studies. The heterogeneity was high, and no publication bias was found. Migraine can play an important role in increasing the incidence of depression in affected patients. Therefore, identifying and ultimately treating the disease can be beneficial in reducing the negative psychological effects of this disease.

Hybrid flexible flowshop scheduling problem with unrelated parallel machines and limited waiting times

Abstract: This paper deals with the problem that reflects real-world situations adequately. Several constraints including unrelated machines, limited waiting times between every two successive processing operations and ready time of jobs are studied. These constraints and characteristics affect on some operations in a large number of companies. In recent researches, they have been tackled many times while so far have not been considered simultaneously. The aim of this paper is to model and solve the addressed problem by applying an efficient metaheuristic algorithm, entitled biogeography-based optimization (BBO). To assess the proposed BBO, two experiments are conducted and their results in terms of solutions quality, as well as computation efficiency compared against two popular algorithms, namely imperialist competitive algorithm and population-based simulated annealing. Due to the sensitivity of the values of parameters in the metaheuristic algorithms, a response surface methodology as a strength statistical tool is used to tune the parameters. The computational results show that the proposed BBO algorithm significantly outperforms the other foregoing algorithms.