Inventory optimization with (S, s) policy and partial backlog under stochastic discrete demand and lead time

This research presents the simulation sheet and Python for optimizing the inventory level and total annual cost with the sample data of demand and the information factor of products in the warehouse. Python is the programming language used to locate the (S, s) level on the model and calculate all the costs. First, the python applied the raw demand data to the demand during lead time to create the sample demand data, and then used the random number generator in excel to create the sample demand set. Create the simulation sheet, which contains all the model's information, factors, and cost components, and then write the Python code to find the average total cost using two methods of search: coordinate and diagonal search. The result is separate in 2 ways, which are the table chart and the visualization of cost component and customer service level, and the result contains 4 different cases, which are Fix W+ Variable LT, Fix W+ Constant LT, Not Fix W+ Variable LT, and Not Fix W+ Constant LT. Then we summarize which case is suitable for each product. The findings of this study will aid in the determination of inventory levels and annual costs in a warehouse environment for future research in more complex and realistic warehouse systems.