# Multi-Agent Simulation Environment for Logistics Warehouse Design Based on Self-Contained Agents
## 5 Experiment to Compare Development Costs
### Purpose of Experiment
Therefore, a novel automated logistics warehouse uses an AGV picking system. Instead of people walking to the shelves, AGVs carry the shelves to the people, and the people pick items from the shelves then put the items into the sorting bucket near them. * iCarry AGV Transport System: In a logistics warehouse, it is necessary to carry items from area to area to process the logistics tasks. This AGV transport system can carry objects by having AGVs move in a pre-defined path. * Automatic Storage System: In a logistics warehouse, storing items in a buffer is sometimes efficient because the picking task does not always finish right before the shipping time. This system stores items so that the items can be retrieved when the rest of the shipping process is ready. * WMS: In a logistics warehouse, there is almost always a WMS that has the information of customer's orders and record of inventory. It is also in charge of controlling the picking system regarding which items to pick, when to pick, etc. Figure 10 shows an overview of the target logistics warehouse of our simulation modeling. The blue lower-right area represents the Auto Storage System, green box represents the AGV Transport System's AGV, white box represents the AGV Picking System's AGV, yellow transparent box represents an inventory shelf, and light blue box close to the inventory selves represents a human picker. The target simulation model was developed and tested using a single computer with Intel Core i7-7800X (3.5 GHz, 12 CPU), 16 GB RAM, Windows 10 Pro (64 bit). The authors prepared the order data and parameters of robots and material-handling equipment based on the actual orders of logistics warehouse and machine's specifications. Statistical variation of agent's behaviors is implemented in each agent's action. In the action that simulates statistical variation, the time taken by the behavior is calculated by multiplying average task processing time and a random value generated from Gaussian distribution using Python's default library. The calculated time is passed onto the agent head. The developed agents are:  
* Racrew Agent: represents an AGV in the Racrew AGV Picking System. * iCarry Agent: represents an AGV in the iCarry AGV Transport System. * Picker Agent: represents a human picker in the Racrew AGV Picking System. * AutoStorage Agent: represents an auto storage system in the simulated logistics warehouse. * WMS Agent: represents a WMS in the simulated logistics warehouse. Using NetLogo, we defined the same agents with the identical rules of behaviors and map information. For fairness, we outsourced the development of the simulation model using NetLogo to a software development company. We only conveyed the simulation model's requirements, input, and output requirement of the simulation model so as not interfere with the design policy of the model that changes the development costs. We did not specify the internal data structure or an agent's communication sequence but specified the actual agents' behaviors, e.g., conditions to trigger actions, where to take certain actions, etc.