Testing life-cycle theory by computer simulation--I. Introduction of genetical structure

Optimization of scheduling scheme for self-driving vehicles by simulation algorithm

As a new logistics technology, self-driving electric vehicles not only improve freight efficiency but also promote energy saving and emissions reductions. Aiming at logistics technologies based on self-driving electric vehicles, planning the vehicle scheduling scheme as a whole reduces energy consumption and improves economic and environmental benefits. Targeting an actual freight problem based on a two-way single-lane road connecting the pickup and delivery points and including electric charging stations, this paper proposes a method for optimizing the scheduling scenario and parameters of self-driving vehicles through computer simulations. An optimization model based on dynamic programming is established, and an optimization simulation algorithm is designed to solve the model, effectively solving the overall planning problem of vehicle scheduling. The experimental results show that the model and algorithm have good universality. After specifying an appropriate road length, total number of vehicles, number of spare vehicle batteries, duration of freight transportation, and other necessary information, the simulation algorithm is executed and the optimal scheduling scheme and the total amount of freight transported are output. The efficiency of the algorithm is extremely high, requiring only 1.5 s to complete the whole simulation process of scheduling 150 vehicles for 1000 h over a road with a length of 10 km.

Testing life-cycle theory by computer simulation--II. Bet-hedging revisited

Analytical and computer models were used to reexamine bet-hedging, the reduction in fecundity that is evolutionarily advantageous in conditions of greater variation in juvenile survivorship or less variation in adult survivorship. The computer simulation models represent diploid one-locus genetic systems with semidominance. Schaffer's (1974) predictions proved remarkably robust when variations were symmetrical, and a simple modification allowed successful prediction for the asymmetries that occurred in the computer simulations when variations were large.