## 1 Introduction
[PERSON] and [PERSON] [12] studied the condition that an MH is economically preferable compared to the traditional port service by using throughput-based evaluation. In [13; 14; 15], the method to generate an optimal schedule and crane movement of MH was developed using mathematical modeling for short-term which is up to 2 days port operation efficiency. Mathematical modeling such as mixed integer-programming is employed to optimize scheduling of periodic routing of special vessels in offshore oil industry [16]. In this study, we develop a method and simulation model to evaluate and design an MH system for the port system design phase. There have been many studies on capacity planning. The goal of these studies is to determine the number or capacity of elements of the system to ensure a desired level of service. Typically, the service level guarantee is approximate and pursued by a safety factor constraint; cf., [17; 18; 19; 20]. Next, we discuss some examples in the vehicle capacity planning context. In [21], a Petri net model was introduced for the capacity planning of a system consisting of unmanned aerial vehicles (UAVs) and ground stations providing logistics support for the UAVs. The Petri net model helps to determine the number of resources that guarantees a desired level of service. [PERSON] and [PERSON] [22] proposed an analytical approach to determine the minimum number of identical automated guided vehicles (AGVs) that are required to guarantee a required level of service. [PERSON] et al. [23] and [PERSON] et al. [24] determined the number and location of logistics support stations for UAVs that have limited flight time in various situations. The concept of service level and the detailed behavior of the transportation units are slightly  
Figure 1: Mobile harbor (MH) system. different in their context, but the essence remains the same. For example, AGVs usually move on a fixed route, and battery life and charging issues are important in an AGV-based transportation system. However, the essence remains the same; MHs cannot move unless they are filled with fuel, and in a broad sense, they are all categorized as transportation systems. In [25], analytic models for the capacities of two types of automated container terminals in traditional ports were developed. These models focused on transport efficiency and stacking capacity and used discrete event simulation to verify the results. In the context of a port or manufacturing system, queuing approximations are often used to set target utilization levels for the system resources as an approximation to service level; cf., [26; 27; 28]. Following the rough capacity analysis, a detailed simulation study may be used to refine the estimates of the resources that are required to achieve the target service level [29; 30; 31; 32]. However, existing design such as queuing approximations, rough capacity analysis, and detailed simulation study for analyzing traditional ports cannot be applied to the MH system. While the economic feasibility of an MH-based port system design was studied through the throughput-based method by [PERSON] and [PERSON] [12], the method is insufficient to determine a detailed MH system design such as the number of berth and the mobile harbor to satisfy target service level. In this paper, we develop an MH simulation model that reveals the characteristics of MHs for evaluating and designing MH-based port systems. Discrete-event simulation is an effective tool to analyze the performance of complex system such as semiconductor wafer fabrication facilities [33]. In addition, we propose a procedure to generate an optimal design with the simulation model. The developed simulation model is applied to obtain the optimal design of the MH (number of MH, type of MH, and number of berth) with given information such as expected throughput, ship information, and cost of the equipment. The optimal design of an MH guarantees the required service level of the port such as waiting time/service time ratio with minimal cost.