Hierarchical Vehicle Scheduling Research on Tide Bicycle-Sharing Traffic of Autonomous Transportation Systems

Algorithm 1

NGA for scheduling optimisation.

	Input: SchedulingArea SA, DistanceMatrix DM
	Output: SchedulingRoute SR, SchedulingVehicle SV, DeployedBikes DR
	Initialization: Generation Gen = 100, CrossoverRate CR = 0.8, MutationRate MR = 0.2, Population Pop ← ∅, Chromosome CH ← ∅, Fitness Fit = Null,
(1)	For all the SA do
(2)	Pop ← CH.generate (SA.Encoding)//Randomly Generation for Population (100)
(3)	End for
(4)	Fit ← Fit.Calculation//Calculate the Fitness
(5)	if Fit.change or Gen < 100 then
(6)	For all the CH do
(7)	CH.select//Chromosome Selection
(8)	CH.crossover//Chromosome Crossover
(9)	CH.mutate//Chromosome Mutation
(10)	Pop ← CH.NewGenerate//New Population
(11)	Fit ← Fit.NewCalculation
(12)	End for
(13)	End if
(14)	SR ← CH.Decoding
(15)	Return SR, SV, DR