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Artificial-Intelligence

Problem Statement : Consider a city network where we need to route a set of electric vehicles which may require to be charged during its journey from some source to some destination. Let us assume that we have n cities (v1, v2, . . . , vn) and the distance between cities vi and vj be eij (if two cities are not connected directly then eij = โˆž and eij = eji). Assume that each city has a single charging station which can charge one EV at a time. Consider a set of k EVs namely P1, P2, . . . , Pk. For each EV the following information is provided โ€“

(a) Sr - source node

(b) Dr - destination node

(c) Br - battery charge status initially

(d) cr - charging rate for battery at a charging station (percent charged per unit time)

(e) dr - discharging rate of battery while traveling (distance travel per unit charge)

(f) Mr - maximum battery capacity

(g) sr - average traveling speed (distance per unit time).

Assume that all vehicles start their journey at t = 0 and Pr reaches it destination at t = Tr. We need to route all the vehicles from their respective sources to destinations such that max{Tr} is minimized.

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Follow the Instructions.txt file for installation and user manual.

Read the Report.txt file for the logic and algorithms used.

Test Case

Node-to-Node Distance

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Electric Vehicle Details

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Results obtained

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The results can be interpreted using the 'Output Interpretation' in the Instructions.txt For the given sample case it does predict the correct results for the given conditions.

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