【正文】 arking tariffs The answer is obviously―Yes‖.Drivers paying lower parking tariffs could be disabled and senior citizens,people who reserve parking space few days in advance,or HOV paying higher tariffs could be solo drivers,long term parking drivers,or drivers showing up and asking for parking without making reservation in ,there is a lot of possible parking pricing strategies. The stochastic nature of reservation generation and cancellation,the stochastic nature of driver showup during reserved time slot,variety of parking tariffs,and the need to respond to drivers` requests in real time,indicate that the management of parking garage revenues represents a plex problem. In the past 30 years a relatively large number of papers have been devoted to different aspects of the airline seat inventory control problem The model proposed in this paper is highly inspired by the developed airline yield management stochastic and/or deterministic models. Let us assume that we have few different parking simplest reservation system(similar to some airline reservation systems in the past)could be―distinct tariff class parking space inventories‖((a)),indicating separate parking spaces in the garage for each tariff this case,once the parking space is assigned to a tariff class,it may be booked only in that tariff class or else remains are certain advantages,as well as certain disadvantages in the case of distinct parking space this case users paying lower tariffs would be relatively well―protected‖.In other words,this system would pay a lot of attention to the disabled person,senior citizens,people who reserve parking space few days in advance,and HOV disadvantage of the distinct parking space inventories is the fact that very often some parking spaces assigned to lower tariff users would be empty even the higher tariff users demand is very other words,it would be possible to reject some drivers even all parking spaces in garage are not occupied. 5. Intelligent parking space inventory control system The plexity of the problem,and the uncertainty of different parameters lead us to the conclusion that it is practically impossible to solve the problem neural works and fuzzy systems are―intelligent‖systems since they have the ability to―learn from experience‖.The fuzzy system is not the learning mechanism,per the other hand,an expanding number of fuzzy systems are being generated based on numerical this paper,we have acquired numerical data by previously discovering optimal solutions to various such a way,fuzzy rule base was derived from―the smartest possible decisions‖.Fuzzy system proposed here embodies a learning mechanism,since it allows for continuous monitoring of parking requests and occasional updating of the fuzzy rule initial assumption in this research is that it is possible to develop an―intelligent‖parking space inventory control system that makes realtime decisions for each driver other words,the paper assumes that it is possible to develop a system that will recognize a situation characterized by the number of reservations made by individual driver classes and the number of canceled reservations at a certain moment in in other intelligent systems,the―intelligent‖parking space inventory control system should be able to generalize,adapt,and learn based on new knowledge and new information. The developed―intelligent‖system is based on fuzzy results reached during the past several years(Wang and Mendel,1992)have indicated that fuzzy logic systems are universal approximators and this explains why fuzzy logic systems are so successful in engineering applications. 6. The algorithm to create intelligent parking spaces inventory control system The fuzzy rules of the―intelligent‖reservation system are generated based on the procedures proposed by Wang and Mendel(1992). The algorithm to create the fuzzy system developed in this paper consists of the following steps: Step 1:Based on a large number of parking activities in garage in question,create the cumulatives(Ei(t),Di(t)). Step 2:Formulate a corresponding integer programming problem and find the optimal solution for each generated―scenario‖. Step 3:Based on the statistical data resulting from Steps 1 and 2,use the Wang–Mendel`s algorithm to generate the fuzzy rule fuzzy rule base corresponds to one tariff class. 7. Results obtained using the intelligent parking space inventory control system The developed model was tested on ten（ 10） different numerical examples considered differed in parking size,requests` arrival distribution and service （ parking time） the examples have in mon the following values： Dt=300seconds；tetb=8hours； m=3。（ f） adding new capacity is expensive,diffcult or impossible。(b)that the parking fees should increase and/or decrease few times during a day. 3. Parking problems and revenue management systems：Analogies with some other industries Airline,industry,hotels,carrental,rail,cruise,healthcare,broadcastindustry,energy,industry,golf,equipment rental,restaurant,and other industries are utilizing revenue management concepts when sellingtheir management could be described as a group of different scientific techniques of managing the pany revenue when trying to deliver the right product to the right client at the right price at the right roots of the revenue management are in the airline basic characteristics of the industries to which different revenue management concepts were successfully applied are:（ a） variable demand over time。Uncertainty modeling。 司機是否會接受停車預定系統(tǒng)和稅收管理系統(tǒng)是未知的。在極端的情況下，不是根據(jù)市場細分模式和停車費免費提供，理論上只有最富有的人才會開車。 市場細分是這樣一種情況，不同的司機愿意為相同的東西付不同的價錢。 正如我們先前指出，任何停車定價策略的主要作用是在一定的時間階段內減少車輛旅行的總數(shù)量。確切的說，這種模型告訴每一個請求，要么：“是的，你可以進入停車”，或者：“不，這個時間你不能停車”。 表 3 FL 解決方案與 IP解決方案和 FIFO 解決方案的比較 圖 2 FL 解決方案和 IP