隨著共享經濟的快速發展，都市化的擴張加快，自用車輛的數量也隨之增加。都會區的停車空間的有效利用與分配已成一個重要的最佳化議題。本研究針對此議題，提出一種共享停車預約模型。該模型基於已知的停車供需資料，希望能透過錯時停車策略以最佳化停車資源的分配，從而提高其利用效率。與傳統的即時預訂系統相比，這種方法提前收集供需數據，以實現更高效的停車資源分配，進而有效紓解都會區的停車壓力，促進都市環境的可持續發展。
為了求解最佳的停車供需配對問題，本研究首先建構一個基本的整數規劃模式(BIP)，其限制式將排除可停在同一車位的兩兩停車需求間的衝突關係。BIP模式通常有大量限制式的問題，為解決此缺點，本研究將上述停車需求衝突關係建構成一網路圖，並透過圖論中的團(clique)推導出更精簡有效的限制式，進而提出一個進階的整數規劃模式(CIP)。儘管上述整數規劃模式能夠求得精確的最佳解，但在解決大規模問題仍然過於耗時。因此，本研究進一步設計三種求解演算法：貪婪演算法(GR)、粒子群演算法(PSO)、以及遺傳演算法(GA)，以在短時間內求得不錯的可行解，從而滿足現實中經常出現的停車需求指派。
經由大量數值測試後，本研究發現，簡化限制式後的CIP模式相較於基本BIP模式在計算時間上顯著縮短。在演算法表現方面，GR在小規模問題上提供了快速解決方案，但在大型測試資料上表現欠佳；PSO在中等規模問題上表現出色，但在處理大規模測試資料時需要較長計算時間才能收斂；GA通過使用基於位置的交配和插入突變，在中大規模測試資料上的求解展現穩健的表現。

This study addresses the critical issue of efficient utilization and allocation of parking spaces in urban areas, proposing a shared parking reservation model that optimizes the allocation of parking resources based on known parking supply and demand data. The model employs a staggered parking strategy to improve parking space utilization efficiency. To solve the optimal parking supply-demand matching problem, the study develops an integer linear programming model (BIP) and introduces a graph theory-based method to simplify the constraints to form a reduced model (CIP), significantly reducing computational complexity. Due to the limitations of exact solution methods, the study further designs three heuristic algorithms: Greedy Algorithm (GR), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).
Extensive numerical experiments demonstrate that the constraint simplification CIP model outperforms the original BIP model in terms of computational time. Among the heuristic algorithms, GR provides quick solutions for small-scale problems, PSO performs well on medium-scale problems but requires longer computation time for large-scale instances, and GA, using position-based crossover and insertion mutation, exhibits robust performance in solving medium to large-scale problems. The proposed methods effectively optimize parking resource allocation, contributing to the alleviation of parking pressures and the promotion of sustainable urban development. Future research directions include incorporating real-time data, integrating other relevant factors, and developing user-friendly interfaces to facilitate the adoption of shared parking systems.

黃郁授。2007。自適應慣性權重改良粒子群演算法之研究。資訊管理研究所，中原大學。
洪筠筑。2019。預約制共享停車位系統車位使用之最佳化排程問題。運輸與物流管理學系碩士論文，國立交通大學。
陳柏瑋。2015。停車位共享系統之穩健排程。工業管理研究所，國立台灣科技大學。
林珏廷。2022。以科技接受模型探討共享停車APP使用之因素。資訊管理研究所，東吳大學。
Belk, R. (2014).You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8), 1595-1600.
Bardhi, F., & Eckhardt, G. (2012). Access-Based Consumption: The Case of Car Sharing. Journal of Consumer Research, 39(4), 881-898.
Botsman. R., & Rogers, R. (2010)What’s Mine Is Yours: The Rise of Collaborative Consumption. New York: HarperCollins Publishers.
Bierwirth, C., Mattfeld, D. C., & Kopfer, H. (1996). On permutation representations for scheduling problems. In H.-M. Voigt, W. Ebeling, I. Rechenberg, & H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature — PPSN IV (pp. 310-318). Springer. https://doi.org/10.1007/3-540-61723-X_994
Chai, H., Ma, R., & Zhang, H. M. (2019). Search for parking: A dynamic parking and route guidance system for efficient parking and traffic management. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3792-3805.
Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73. https://doi.org/10.1109/4235.985692.
Chen, X., & Xu, H. (2021). Optimizing urban parking allocation with a predictive model and real-time data. Journal of Transportation Engineering, Part A: Systems, 147(2), 04021010. https://doi.org/10.1061/JTEPBS.0000506.
Dantzig, G. B. (1957). Discrete-Variable Extremum Problems. Operations Research, 5(2), 266-288. doi: doi:10.1287/opre.5.2.266
Dantzig, G. B. (1963). Linear Programming and Extensions.
Engelbrecht, A. (2012). Particle swarm optimization: Velocity initialization. In Proceedings of the 2012 IEEE World Congress on Computational Intelligence (pp. 1-8). Brisbane, Australia: IEEE.
Fisher, M. L. (2004). The Lagrangian relaxation method for solving integer programming problems. Management science, 50(12_supplement), 1861-1871.
Geissinger, A.,Laurell, C. & Sandström, C.(2018). Digital Disruption beyond Uber and Airbnb—Tracking the long tail of the sharing economy.Technological Forecasting & Social Change, Elsevier, 155(C).
Goldberg, D. E., & Lingle, R. (1985). Alleles, loci, and the traveling salesman problem. In J. J. Grefenstette (Ed.), Proceedings of an International Conference on Genetic Algorithms and Their Applications (pp. 154-159). Lawrence Erlbaum Associates.
Goldberg, D. E. (1989). "Genetic Algorithms in Search, Optimization, and Machine Learning." Addison-Wesley.
Held, M., & Karp, R. (1962). A Dynamic Programming Approach to Sequencing Problems. Journal of the Society for Industrial and Applied Mathematics, 10(1), 196-210. doi: 10.1137/0110015
Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press.
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.
Korte, B., & Vygen, J. (2018). Combinatorial optimization: Theory and algorithms (6th ed.). Springer.
Lawler, E. L., & Wood, D. E. (1966). Branch-and-Bound Methods: A Survey. Operations Research, 14(4), 699-719. doi: doi:10.1287/opre.14.4.699
Lee, J., Kim, S., & Park, Y. (2019). "A study on smart parking management system based on IoT." International Journal of Smart Home, 13(3), 35-42.
Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs. Springer.
Marini, F., & Walczak, B. (2015). Particle swarm optimization: A tutorial. Chemometrics and Intelligent Laboratory Systems, 149, 153-164.
Owyang, J., Samuel, A., & Grenville, A. (2014). Sharing is the New Buying: Winning in the Collaborative Economy. Retrieved May 21, 2019, from https://www.slideshare.net/jeremiah_owyang/sharingnewbuying
Pearson, D. (2005). A polynomial-time algorithm for the change-making problem. Operations Research Letters, 33(3), 231-234
Sundararajan, A. (2016). The sharing economy: The end of employment and the rise of crowd-based capitalism. MIT Press.
Schor, J (2016). Debating the Sharing Economy. Journal of Self-Governance & Management Economics, 4(3),7-22.
Stephany, A. (2015) The business of sharing: Making it in the new sharing economy. Basingstoke: Palgrave MacMillan
Shoup, D. C. (2006). Cruising for parking. Transport Policy, 13(6), 479-486.
Syswerda, G. (1991). "Scheduling optimization and the use of genetic algorithms." In Handbook of Genetic Algorithms (pp. 332-349). Van Nostrand Reinhold.
Woeginger, G. J. (2003). Exact algorithms for NP-hard problems: A survey. In M. Jünger, G. Reinelt, & G. Rinaldi (Eds.), Combinatorial optimization—Eureka, you shrink! (pp. 185-207). Springer. https://doi.org/10.1007/3-540-36478-1_17
Zhu, Y., Ye, X., Chen, J., Yan, X., & Wang, T. (2020). Impact of cruising for parking on travel time of traffic flow. Sustainability, 12(8), 3079. https://doi.org/10.3390/su12083079.