2021/12/16 by Pranavi Pathakota, Pathakota, Pranavi, Kunwar Zaid +11
Business, Management and Accounting · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scheduling and Optimization Algorithms #Supply Chain and Inventory Management #Vehicle Routing Optimization Methods
paper · pdf · doi:10.48550/arxiv.2112.08736
openalex publication_date 2021/12/16 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
We describe a novel decision-making problem developed in response to the demands of retail electronic commerce (e-commerce). While working with logistics and retail industry business collaborators, we found that the cost of delivery of products from the most opportune node in the supply chain (a quantity called the cost-to-serve or CTS) is a key challenge. The large scale, high stochasticity, and large geographical spread of e-commerce supply chains make this setting ideal for a carefully designed data-driven decision-making algorithm. In this preliminary work, we focus on the specific subproblem of delivering multiple products in arbitrary quantities from any warehouse to multiple customers in each time period. We compare the relative performance and computational efficiency of several baselines, including heuristics and mixed-integer linear programming. We show that a reinforcement learning based algorithm is competitive with these policies, with the potential of efficient scale-up in the real world.