This paper represents the performance evaluation of a microcontroller based solar-powered auto-rickshaw which can be used extensively in Bangladesh for transportation. The battery operated traditional auto-rickshaw is charged by national grid whereas the battery bank of the proposed microcontroller based solar-powered auto-rickshaw is charged by solar power. A PIC microcontroller is programmed to control the duty cycle of the dc motor and thereby reduce the battery discharge time of the battery bank. The life cycle cost (LCC) analysis has been done and compared with the traditional battery operated autorickshaw. It has been found that LCC/kWh of the proposed rickshaw is minimum. The practical measured results reveal that the performance of the proposed rickshaw outperforms the conventional battery operated auto-rickshaw.
Traveling salesman, linear ordering, quadratic assignment, and flow shop scheduling are typical examples of permutation-based combinatorial optimization problems with real-life applications. These problems naturally represent solutions as an ordered permutation of objects. However, as the number of objects increases, finding optimal permutations is extremely difficult when using exact optimization methods. In those circumstances, approximate algorithms such as metaheuristics are a plausible way of finding acceptable solutions within a reasonable computational time. In this paper, we present a technique for clustering and discriminating ordered permutations with potential applications in developing neural network-guided metaheuristics to solve this class of problems. In this endeavor, we developed two different techniques to convert ordered permutations to binary-vectors and considered Adaptive Resonate Theory (ART) neural networks for clustering the resulting binary vectors. The proposed binary conversion techniques and two neural networks (ART-1 and Improved ART-1) are examined under various performance indicators. Numerical examples show that one of the binary conversion methods provides better results than the other, and Improved ART-1 is superior to ART-1. Additionally, we apply the proposed clustering and discriminating technique to develop a neural-network-guided Genetic Algorithm (GA) to solve a flow-shop scheduling problem. The investigation shows that the neural network-guided GA outperforms pure GA.
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