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资源说明：Uses a Genetic Algorithm to determine the optimal day-to-day procedures of a simulated business that must purchase, process, and sell finished goods.
Project Name: Optimal Business Time Partitioning Description: An agent will contain all the information about which actions to perform each day. Each day a total of 16 actions can be performed, with 8 different actions: Search for Raw Materials (RM), Risky Search for RMs, Produce Low-Quality Goods (LQG), Produce Medium-Quality Goods (MQG), Produce High-Quality Goods (HQG), Sell Goods, Sell Goods in Risky Environment, and Store Unused Goods (UG). After 100 days, all the agents in a generation will be assessed for their overall performance. A new generation of agents is spawned and the process repeated until end-conditions are met (time, generation count, or plataeu of performance). All unused raw materials must be stored by the end of the day. ######################## Technical Details of Algorithm*: > Performace will be rated based on trending revenue growth. > Highly Likely to change or other factors thrown in > Elite agents will be carried over to the next generation untouched, while all other agents will either be replaced with newly generated agents, agents of mixed chromosomes, or mutated agents. > Each chromosome will be composed of 16 3-bit strings to represent the division of time among the 8 different tasks. Crossover will involve taking 2+ parents and taking 1+ subsequent series of tasks and recombining them. Mutation will be the random application of bit-flipping. > Actions are encoded as all values between 000:111 > All "Risky" actions have predetermined and unchanging "Lucky Days". In this way, if there is a 50% chance for a risky investment to pay off, then exactly 50 of the 100 days will be "Lucky Days", and the others failures. The exact distribution of the lucky days is determined either as random placement, alternating, all early, all late, or a mix of any of the stated distribution methods. > Successful risky searches will give higher RMs at lower costs. > Successful risky sales will give higher returns. > Unsuccessful risky investments will simply have the opposite effect. > LQGs differ from MQGs and HQGs in the number of RMs consumed and the sale price obtained in the market. > During a sale action, LQ, MQ, and HQ goods will be sold (in decreasing amounts) > If an agent fails to store UGs by the end of the day then all UGs are lost during the next day. ######################## User Interface Details: > The environment will be completely alterable at the user level, with possible alterable variables such as: > Cost/Return on RMs and FGs > exchange rate of RMs to Finished Goods (FGs) > risky pay-offs and odds > number of parents during pollination > total number of agents per generation > total number of elites carried over > total number of "failed entrepeneurs" carried over > total number of randomly generated agents added each generation > total number of days in a performance assessment period > Possibly Others > Start, Stop, Restart, Step, Save com.Agent Information, Save Environment State, and Save Everything will all be actions the user can perform on a simulation
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