In this current study, prevalence of lameness was detected and its changes during different parities, Days in Milk (DIM) and milk production were studies. In addition, effects of lameness on Open Days (OD) and Service per Conception (S/C) were studied. Three dairy farms on three scales (1: Large, approximately 900 milking cows, 2: Medium, approximately 100 milking cows and 3: Small, approximately 20 milking cows) were watched for lameness in 2005-2006. Locomotion Scoring (LS) by Sprecher method (1-5 point scale) has been done by videoing of the animals at the exit of the milking parlor. Videos were reviewed by two expert and mean of the each score used as score of the animal, cows with scores 1 and 2 recorded as non-lame and 3, 4 and 5 as lame cows. The average score of the lameness in autumn and spring recorded as 2.47 and 2.73, respectively that was higher significantly in spring. LS has been increased significantly by increasing parity and DIM, as highest scores were recorded in parity 4 and DIM 240-300. No significant differences between lame and non-lame cows were recorded in according to their milk production. The highest (percent in lame cows) scores were recorded in high producing cows. No significant difference in milk production has been recorded in different LS. However the average production of milk in lame cows were 1.08 L day(-1) less that non-lame cows. The average OD of the lame cows was significantly longer (52 days) than non-lame cows. Lame cows needed significantly higher service/conception (one) than non-lame cows. Median of OD and S/C has been increased by LS.

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This study aims to present a novel Self-regulating and Intelligence Meta-Heuristic-Fuzzy approach (As Methodological Contribution) for integrated and optimal Human Resource Allocation (HRA) in normal and critical conditions at SMEs (As Conceptual Contribution). In this research, a mathematical model of human resource allocation problem is presented, and then Sugeno Fuzzy Inference (SFI) model is used in the tasks rate adjustment layer. The SFI model is the main part of developing Gray Wolf Optimization (GWO) algorithm to reach the integrated and optimal allocation of available human resources under self-regulating attribute in the novel approach. The novel approach has tested and compared to the best researches using data previous researches and by the top five proposed methods in the researches (Includes: SGA, PRS, SRS, MIP, HM) based on three methods of evaluating the quality of solutions (GA-FSGS, MP-FSGS, GA-SGS). The results showed that increase of Ω from 15,000 to 25,000, and HM and SGA clearly performed better than other previous cases in the larger B100 and B200 datasets. Also, it is verified that the method had better results compare to all previous solving methods, and the quality of the solutions have been the best.

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