自2020年全球爆發COVID-19疫情以來，全球網購業績皆有大幅明顯增長，其中影響最大的產業之一就是物流業。目前已有多家本土與外資電商自建物流系統與車隊，除了在地化的物流中心，更提升到跨國的貨物配送。因此，物流效率己成為銷售不可或缺的重要一環。自建物流車隊的優點除了能擁有配送的控制權，更能提高企業品牌價值。然而在配送效率上卻可能不如專業物流的問題，本論文即針對自建的物流車隊如何提高配送效率為研究重點，希望能發展有系統的處理方式以降低成本提高獲利及客戶滿意度。本研究中以某燈具買賣公司J公司的南部分公司為例，研究在碼頭與車輛不對等、配送時程需及時之情況下，以整數規劃建構配送員總工時及總成本最小化的數學模式，該模式雖可求得精確最佳解但耗時甚久，因此本研究再設計高效的貪婪演算法，先計算不錯的物流配送路線排程，再推導對應的貨車碼頭指派與排序，並利用三種鄰域搜尋演算法進一步改善所得解之品質，進而提升企業的獲利與提高客戶之滿意度。以實際歷史訂單資料進行測試後，確認本研究所提出的求解方法可減少車輛的使用，並減少配送人員配送超時的情況，逹成總成本最低的配置決策目標。

This study addresses the challenges faced by in-house logistics fleets in maintaining efficiency and customer satisfaction. Using the southern branch of J Company, a lighting company, as a case study, the research develops a mathematical model using integer programming to optimize delivery routes and dock assignments. The model considers the imbalance between docks and vehicles, timely delivery schedules, and the minimization of employee work hours and costs.Due to the complexity of the mathematical model, the study proposes a greedy algorithm to generate initial solutions and employs three neighborhood search algorithms to improve solution quality. The algorithms are tested using historical order data from J Company at three different scales: small, medium, and large. The results demonstrate that the proposed method effectively reduces vehicle usage, overtime for delivery personnel, and total delivery time. For small-scale orders, the optimized solution achieves a 7.3% reduction in total delivery time compared to the initial solution. Medium-scale orders show an 11% reduction, while large-scale orders exhibit a 5% reduction in total delivery time and a 12% improvement in vehicle utilization.The study highlights the potential for cost savings and improved customer satisfaction through the implementation of systematic optimization methods in in-house logistics management. Future research directions include integrating real-time traffic information, enhancing algorithm efficiency, refining cargo volume calculations, and considering customer waiting times to further improve the practicality and effectiveness of the proposed approach.

Altabeeb, A. M., Mohsen, A. M., Abualigah, L., & Ghallab, A. (2021). Solving capacitated vehicle routing problem using cooperative firefly algorithm. Applied Soft Computing, 108, 107403.
Anggodo, Y. P., Ariyani, A. K., Ardi, M. K., & Mahmudy, W. F. (2017). Optimization of multi-trip vehicle routing problem with time windows using genetic algorithm. Journal of Environmental Engineering and Sustainable Technology, 3(2), 92-97.
Baker, E. K., & Schaffer, J. R. (1986). Solution improvement heuristics for the vehicle routing and scheduling problem with time window constraints. American Journal of Mathematical and Management Sciences, 6(3-4), 261-300.
Chen, J. F. (2006). Approaches for the vehicle routing problem with simultaneous deliveries and pickups. Journal of the Chinese Institute of Industrial Engineers, 23(2), 141-150.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1), 80-91.
Escobar-Falcón, L. M., Álvarez-Martínez, D., Granada-Echeverri, M., Escobar, J. W., & Romero-Lázaro, R. A. (2016). A matheuristic algorithm for the three-dimensional loading capacitated vehicle routing problem (3L-CVRP). Revista Facultad de Ingeniería Universidad de Antioquia, (78), 09-20.
Fenton, A. (2016). The bees algorithm for the vehicle routing problem. arXiv preprint arXiv:1605.05448.
Gendreau, M., Iori, M., Laporte, G., & Martello, S. (2006). A tabu search algorithm for a routing and container loading problem. Transportation Science, 40(3), 342-350.
Goel, R., & Maini, R. (2021). Improved multi-ant-colony algorithm for solving multi-objective vehicle routing problems. Scientia Iranica, 28(6), 3412-3428.
Hashimoto, H., Yagiura, M., & Ibaraki, T. (2008). An iterated local search algorithm for the time-dependent vehicle routing problem with time windows. Discrete Optimization, 5(2), 434-456.
İlhan, İ. L. H. A. N. (2021). An improved simulated annealing algorithm with crossover operator for capacitated vehicle routing problem. Swarm and Evolutionary Computation, 64, 100911.
Józefowska, J., Pawlak, G., Pesch, E., Morze, M., & Kowalski, D. (2018). Fast truck-packing of 3D boxes. Engineering Management in Production and Services, 10(2), 29-40.
Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Küçük, M., & Yildiz, S. T. (2022). Constraint programming-based solution approaches for three-dimensional loading capacitated vehicle routing problems. Computers & Industrial Engineering, 171, 108505.
Mari, F., Mahmudy, W. F., & Santoso, P. B. (2018). An improved simulated annealing for the capacitated vehicle routing problem (CVRP). Jurnal Ilmiah Kursor, 9(3).
Pan, B., Zhang, Z., & Lim, A. (2021). Multi-trip time-dependent vehicle routing problem with time windows. European Journal of Operational Research, 291(1), 218-231.
Praveen, V., Keerthika, P., Sivapriya, G., Sarankumar, A., & Bhasker, B. (2022). Vehicle routing optimization problem: a study on capacitated vehicle routing problem. Materials Today: Proceedings, 64, 670-674.
Yalcin, G. D., & Erginel, N. (2022). An Adapted Fuzzy Multi-Objective Programming Algorithm for Vehicle Routing. Universal Journal of Operations and Management, 56-74.
Zhong, Y., & Cole, M. H. (2005). A vehicle routing problem with backhauls and time windows: a guided local search solution. Transportation Research Part E: Logistics and Transportation Review, 41(2), 131-144.