在現今網路蓬勃發展下，不同的零售業形態日益增加，以線下為主的傳統零售企業逐漸增加線上通路，而以線上為主的新興零售企業也開始與線下零售商展開合作，希望給予顧客更多元的購買通路，為了在不同通路提供顧客無縫的購物體驗，越來越多的零售企業開始積極朝向全通路零售發展。開展全通路零售後整合線上、線下，物流服務應當成為其首要探討問題，另外在網購盛行的同時也增加了顧客退貨的需求，故逆向物流問題在零售業中也越趨重要。
本研究介紹了全通路零售之同時收送貨車輛途程問題(Vehicle Routing Problem with Simultaneous Pick-up and Delivery in Omni-channel Retailing; OCVRP-SPD)，問題目標為最小化總行駛距離。本研究在全通路零售配送系統下結合SPD ，問題中各節點可具有多種產品的送貨和收貨需求，零售商店每日由倉庫配送補充店內庫存，而線上顧客需求則由零售商店內的可用存貨所滿足，並將產品從零售商店分發給線上顧客，顧客與可滿足其需求之零售商店配對並由同一車輛服務兩者，找出車輛最佳路徑。本研究延伸全通路零售問題並擴展成兩個問題，首先加以考量同時收送貨的需求，即零售商和顧客皆可以將產品退回到倉庫，最後再進一步探討從倉庫直接配送給顧客的可能性，根據兩個問題，本研究生成可適用於OCVRP-SPD的題庫並開發了兩個數學規劃模型與適應性大規模鄰域搜尋演算法(Adaptive Large Neighborhood Search; ALNS) ，使用兩者來求解此問題。最後，通過實驗結果分析路徑成本的影響並探討本研究所提出之ALNS在求解OCVRP-SPD上之效率及所帶來的效益。

With mobile technology today, different retail formats have increasingly evolved. Traditional offline retailers have included online channels to serve online customers via their own websites and social media. However, in an effort to offer seamless services within different channels, more and more retailers have ventured into omni-channel retailing. Omni-channel retailing reduces friction in customers' transactions including purchases and customer returns - a reverse logistics issue that is gaining urgency in online retail. This thesis presents the Vehicle Routing Problem with Simultaneous Pickup and Delivery in Omni-channel Retailing (OCVRP-SPD). The classical SPD problem in VRP is a cost-minimizing optimization model with customer nodes having both pickup and delivery demands of multiple products. All deliveries to the customer nodes are fulfilled from the warehouse while all pickups along the route are delivered back to the warehouse. Here, the SPD problem is extended and explored under an Omni-channel format with those features: (1) offline stores are used as fulfillment centers to fulfill online customer orders, (2) the warehouse may also be used as fulfillment centers for some online customer orders, and (3) retail store and customers can return the product to the warehouse which should be picked up along the route.
According to the problem, this study generates the instance for OCVRP-SPD. Develops two mathematical models using AMPL/GUROBI to solve and an Adaptive Large Neighborhood Search (ALNS) algorithm to solve the problem too. In the end, explore results via numerical comparative the impact of optimal routes and costs.

Abdulkader, M. M. S., Gajpal, Y., & ElMekkawy, T. Y. (2018). Vehicle routing problem in omni-channel retailing distribution systems. International Journal of Production Economics, 196, 43-55.
Baxendale, S., Macdonald, E. K., & Wilson, H. N. (2015). The impact of different touchpoints on brand consideration. Journal of Retailing, 91(2), 235-253.
Bhatti, A., Akram, H., Basit, H. M., Khan, A. U., Raza, S. M., & Naqvi, M. B. (2020). E-commerce trends during COVID-19 Pandemic. International Journal of Future Generation Communication Networking, 13(2), 1449-1452.
Bianchessi, N., & Righini, G. (2007). Heuristic algorithms for the vehicle routing problem with simultaneous pick-up and delivery. Computers & Operations Research, 34(2), 578-594.
Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing: MIT Cambridge.
Chen, J.-F., & Wu, T.-H. (2006). Vehicle routing problem with simultaneous deliveries and pickups. Journal of the Operational Research Society, 57(5), 579-587.
Crispim, J., & Brandão, J. (2005). Metaheuristics applied to mixed and simultaneous extensions of vehicle routing problems with backhauls. Journal of the Operational Research Society, 56(11), 1296-1302.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80-91.
Dell’Amico, M., Righini, G., & Salani, M. (2006). A branch-and-price approach to the vehicle routing problem with simultaneous distribution and collection. Transportation Science, 40(2), 235-247.
Dethloff, J. (2001). Vehicle routing and reverse logistics: the vehicle routing problem with simultaneous delivery and pick-up. OR-Spektrum, 23(1), 79-96.
eMarketer. (2021). Global Ecommerce Forecast 2021. Retrieved from https://www.emarketer.com/content/global-ecommerce-forecast-2021
Goeke, D., & Schneider, M. (2015). Routing a mixed fleet of electric and conventional vehicles. European Journal of Operational Research, 245(1), 81-99.
Guerrero-Lorente, J., Gabor, A. F., & Ponce-Cueto, E. (2020). Omnichannel logistics network design with integrated customer preference for deliveries and returns. Computers & Industrial Engineering, 144, 106433.
Hemmelmayr, V. C., Cordeau, J.-F., & Crainic, T. G. (2012). An adaptive large neighborhood search heuristic for two-echelon vehicle routing problems arising in city logistics. Computers & Operations research, 39(12), 3215-3228.
Herhausen, D., Binder, J., Schoegel, M., & Herrmann, A. (2015). Integrating bricks with clicks: retailer-level and channel-level outcomes of online–offline channel integration. Journal of Retailing, 91(2), 309-325.
Kumar, V., Anand, A., & Song, H. (2017). Future of retailer profitability: An organizing framework. Journal of Retailing, 93(1), 96-119.
Min, H. (1989). The multiple vehicle routing problem with simultaneous delivery and pick-up points. Transportation Research Part A: General, 23(5), 377-386.
Nagy, G., & Salhi, S. d. (2005). Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries. European Journal of Operational Research, 162(1), 126-141.
Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., & Verhoef, P. C. (2006). Challenges and opportunities in multichannel customer management. Journal of Service Research, 9(2), 95-112.
Paul, J., Agatz, N., Spliet, R., & De Koster, R. (2019). Shared capacity routing problem− An omni-channel retail study. European Journal of Operational Research, 273(2), 731-739.
Ropke, S., & Pisinger, D. (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40(4), 455-472.
Rowell, J. (2013). Omni-channel retailing. Romanian Distribution Committee Magazine, 4(2), 12-15.
Salhi, S., & Nagy, G. (1999). A cluster insertion heuristic for single and multiple depot vehicle routing problems with backhauling. Journal of the Operational Research Society, 50(10), 1034-1042.
Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problems. Paper presented at the International Conference on Principles and Practice of Constraint Programming.
Subramanian, A., Uchoa, E., Pessoa, A. A., & Ochi, L. S. (2011). Branch-and-cut with lazy separation for the vehicle routing problem with simultaneous pickup and delivery. Operations Research Letters, 39(5), 338-341.
Toth, P., & Vigo, D. (2002). An overview of vehicle routing problems. The Vehicle Routing Problem, 1-26.
Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174-181.
Wang, C., Mu, D., Zhao, F., & Sutherland, J. W. (2015). A parallel simulated annealing method for the vehicle routing problem with simultaneous pickup–delivery and time windows. Computers & Industrial Engineering, 83, 111-122.
Yu, V. F., Jewpanya, P., Redi, A. P., & Tsao, Y.-C. (2021). Adaptive neighborhood simulated annealing for the heterogeneous fleet vehicle routing problem with multiple cross-docks. Computers & Operations Research, 129, 105205.