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The net present value (NPV)-based resource constrained project scheduling problem (RCPSP) is a well-known scheduling problem in many industries, such as construction, software development, and manufacturing. Over the last five decades, although different approaches have been proposed to solve the problem, no single approach has been shown to achieve satisfactory performances with quality solutions for a wide range of problems. This study presents a hybrid immune genetic algorithm (IGA) to solve NPV-based RCPSPs. Hybridizing a genetic algorithm (GA) with an immune algorithm (IA) enhances the overall performance of their standalone components (i.e., only GA or IA). Performance of the proposed IGA is further improved by applying a variable insertion based local search (VINS) and forward-backward improvement (FBI). A restart mechanism is presented to the algorithm which induces diversity and helps to avoid becoming trapped in local optima. Moreover, an activity move rule (AMR) is implemented to shift the negative cash flow associated activities to further improve the NPV. Taguchi Design of Experiment (DOE) is conducted to investigate the impact of various parameters and to determine the appropriate set of parameters for the proposed IGA. The performances of the proposed algorithms are tested on 17,280 standard benchmark instances ranging from 25 to 100 activities. Comparison with the state-of-art algorithms through extensive numerical experiments reveal the effectiveness of the proposed algorithms. Overall, the proposed algorithm outperforms existing algorithms, particularly the projects with 0% and 100% negative cash flow associated activities, the 75-activity instances, and the projects with two resources usage in terms of a lower value of average percentage deviation.

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The estimation of stature is very important in forensic investigation, as it provides useful data that can narrow the pool of potentially matching identities. The purpose of this study was to develop formulae for the estimation of stature from footprint measurements in Bangladeshi adults. This study included 118 randomly selected men and 130 randomly selected women, all aged 18-50 years. From each participant, stature and six footprint measurements were taken by means of standard measurement techniques. Footprint measurements were found to be positively correlated with stature. Stature was estimated by using linear regression equations. The right T1 length in men (R: þ0.587, R 2 : 0.345) and the right T2 length in women (R: þ0.506, R 2 : 0.256) were the most reliable individual estimators of stature. However, when data were combined for both sexes, the right T2 length was identified as the most reliable estimator of stature, with higher values of R (þ0.792) and R 2 (0.627). In conclusion, human stature can be successfully estimated by using footprint measurements; this finding can be applied in forensic research and investigation.