Update README.md

README.md

@@ -365,8 +365,8 @@ In addition to being able to solve standard problems, 🤠GreedRL can also model
 
 #### Instant Delivery Service
 
-> Instant Delivery Service are widespread in order dispatching systems of the supply chain, courier delivery services and elsewhere.
-> Orders are generated in real-time. A number of vehicles are scheduled to serve orders from pickup locations to delivery locations while respecting vehicle capacity and service time constraints. The objective is to dynamically assign each order to the most appropriate vehicle so that the overall transportation cost (e.g., overall distances) cound be minimized.
+> Instant Delivery Service are widespread in order dispatching systems of courier delivery services ([Ele.me](https://www.ele.me/), [Meituan](https://waimai.meituan.com/), [UUPaotui](https://www.uupt.com/index.htm), etc).
+> Orders are generated in real-time. A number of vehicles are scheduled to serve orders from pickup locations to delivery locations while respecting vehicle capacity. The objective consists in minimizing both total delivery time and overtime penalty.
 
 
 <details>
@@ -462,7 +462,7 @@ class Objective:
 
 #### Order Batching Problem
 > The Order Batching Problem is an optimization problem which occurs in a warehouse consists of designing a set of picking batches, such that each customer order (composed by a list of items) is assigned to exactly one batch,
-> each batch have to be collected by a single picker without exceeding a capacity limit, and the total distance traveled by the pickers is minimal.
+> and each batch has to be collected by a single picker. The objective consists in minimizing both total batching cost (a weighted sum of used numbers of areas, roadways and items) and penalty for exceeding loading limits of pickers.
 
 <details>
     <summary>Order Batching Problem</summary>