Location Optimization Charging Station of Three-Wheel

Location Optimization Charging Station of Three-Wheel Electric Vehicle: A Case Study

Nida An Khofiyah

Master Program of Industrial Engineering Department, Faculty of Engineering Universitas Sebelas Maret, Jl. Ir. Sutami, 36 A, Surakarta, Indonesia

[email protected]

Ananda Vania Arisa Putri Bachelor Program of Industrial Engineering Department, Faculty of Engineering Universitas Diponegoro, Jl. Prof. Soedarto, SH Tembalang, Semarang, Indonesia

[email protected]

Wahyudi Sutopo University Centre of Excellence for Electrical Energy Storage Technology

Research Group Industrial Engineering and Techno-Economic, Industrial Engineering Department, Faculty of Engineering

Universitas Sebelas Maret, Jl. Ir. Sutami, 36 A, Surakarta, Indonesia [email protected]

Abstract To supporting the downstream and commercialization of the battery electric vehicle (BEV), Universitas Sebelas Maret is developing a battery-based three-wheeled electric vehicle. This 3-wheeled electric vehicle has the advantage of being more stable in use and can carry passengers or goods. However, in the application of the use of 3-wheeled electric vehicles, there is no sufficient charging station to support mass use, so people are still reluctant to use it. The supply chain network design is needed to analyze the optimum location of the charging station. This study proposes the location of a 3-wheeled electric vehicle charging station to achieve the widest possible coverage with the least number of charging stations and minimal investment costs with a case study in the city of Surakarta. Potential public facilities that are used as locations for charging stations are markets and malls, both of which are frequently visited and easily found by the public. The method to be used is a modification of the backup coverage model and the distance-based model run with IBM ILOG® CPLEX® Optimization. The results obtained are a recommendation to the city government to support the acceleration of electric vehicles, especially 3-wheeled vehicles in Surakarta. Keywords Charging Station, Location Optimization, Supply Chain Network Design, and Three Wheel Electric Vehicle 1. Introduction

Air quality in Indonesia is getting worse, especially in big cities in Indonesia. The decline in air quality is caused by various sectors with the biggest pollution coming from motor vehicles. In the last 10 years, there has been a very rapid increase in the number of motorized vehicles, especially the increase in motorbikes which has reached 30% (Ismiyati et. al., 2014; Habibie and Sutopo, 2020). This has an impact not only on the health and environmental sectors but also on the economic and social sectors (Sutopo et. al., 2013). For this reason, the government makes regulations for the acceleration of the battery-based electric motor vehicle program as an effort to increase energy efficiency, energy security, and energy conservation in the transportation sector, and the realization of clean energy, clean air quality, and environmentally friendly in Indonesia according to articles according to presidential regulation No.55 in 2019. In realizing this, the government is working with industry and academia to make battery-based electric vehicle products. Universitas Sebelas Maret has succeeded in building a lithium battery factory to support electric vehicles.

Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management Singapore, March 7-11, 2021

© IEOM Society International 2243

mailto:[email protected]



Currently, Universitas Sebelas Maret is developing a Three-Wheel Electric Vehicle which has the advantage of being more stable than two-wheeled bicycles, can be used for goods distribution in urban areas, and can also be used to carry passengers (Syafiq, 2015; Arifurrahman et. al., 2018). However, the problem in using this Three-Wheel Electric Vehicle is that no charging station can be accessed in public facilities in urban areas. So that people are still reluctant to use this Three-Wheel Electric Vehicle. The charging station is an important component of an electric vehicle, so it is important to build a charging station before the Three-Wheel Electric Vehicle is used en masse by the public (Sutopo et. al., 2019; Istiqomah and Sutopo, 2020). Figure 1 shown Three-Wheel Electric Vehicle produced by Universitas Sebelas Maret.

Figure 1. Three-Wheel Electric Vehicle

Supply chain network design is a strategic activity that must be carried out in Supply Chain Management and includes decisions about the location, number, and capacity of production and distribution facilities in a supply chain. The purpose of the existence of a supply chain network is to meet customer needs which of course can change dynamically from time to time (Klibi et al, 2010, Pujawan and Er, 2017). The supply chain network is the result of several strategic decisions, one of which is a decision about the location of production facilities and warehouses as well as decisions about purchasing raw materials (Pujawan and Er, 20117). However, in every decision, many considerations need to be taken into accounts, such as considerations of cost, location, environment, economy, and others. So that in designing a supply chain network, a quantitative model is needed to solve existing problems. In this study, the problem of the location of the charging station is a problem that needs to be resolved with a supply chain network design model. In research, Jing et. al. (2017) examined the effect of the location of public facilities for charging a Battery Electrical Vehicle (BEV) by placing several charging stations using a mixed nonlinear programming method, with an equilibrium-based heuristic algorithm. Then in the research of Zhang, et. al. (2017) examined the optimization of the location of electric ship charging stations based on the backup coverage model by analyzing the feasibility, reserve location models, and optimization methods. Research by Akbari et. al. (2018) discussed the optimization of the EV charging station location by applying a Genetic Algorithm and the best position to place the charging station is obtained to meet customer demands. Likewise in the research of Pagany et. al. (2019) and Cui et. al. (2018) discusses the same thing but different methods and objects of research. From this previous research, it can be seen that optimizing the location of the charging station on the electric vehicle is an interesting theme reviewed in the last 5 years. From previous research, the model used in this study is the backup coverage model. Hogan and ReVelle (1986) introduced the notion of reserve coverage into the location model. These models are developed in the context of determining the emergency location of the facilities needed to serve a specific population. Then, Araz (2007) proposes a multi-purpose maximum covering the location model, three objectives are the maximization of the population covered by one vehicle, maximizing the population with coverage reserves, and minimizing the total travel distance from locations at a distance that is greater than the predetermined distance standard for all zones. Zhu (2016) proposes



a new model for the location of a plug-in electric vehicle problem charging station. Intending to minimize the total cost, the proposed model simultaneously addresses the problem of where to find a charging station and how many chargers should be installed at each charging station. So that the Backup coverage model can be used in the solution to determine the optimal location of the Three-Wheel Electric Vehicle charging station. The application of charging stations is the most significant problem for the electrical vehicle (EV) industry. For this reason, by optimizing the distance between charging stations, costs can be minimized (Akbari et al, 2018). In optimizing the distance between charger stations so that they can cover all areas, it is necessary to determine public facilities that can be used as locations for charging stations. According to Nugrahadi et al (2020) markets and malls that are public facilities and are often found in almost all regions can be used as charging stations. In designing a supply chain network, things that need to be considered are the number of entities, and the flow of the supply chain. So it is necessary to create a supply chain network for the location of filling stations. Figure 2 below describes the supply chain network design process.

Figure 2. Supply chain network design process

The problem described in this study is to find the best number of potential facilities built to minimize the total weighted distance from the facility to the customer, assuming that all facilities can meet all customer demands and all requests are met. This study aims to optimize the location selection for the charging station for the Three Wheel Electric Vehicle public facilities with a case study in the city of Surakarta. Potential public facilities that are used as locations for Three-Wheel Electric Vehicle charging stations are markets and malls, where these two facilities are often and easily found by the public. Then there are 4 (four) involved entities, namely charging station providers, market owners (government), mall owners (private), and Three-Wheel Electric Vehicle users. Of these four entities, the battery charging activity is carried out at the charging station, in which the swapped battery is moved from the charging station to the Three-Wheel Electric Vehicle user.

2. Methods

The model in this research is the development of the model in Zhang et. al. (2017) and Akbari et. al. (2018). From these two studies, a model for determining the optimization of the charging location of the Three Wheel Electric Vehicle was made. This research considers the coverage demand maximization and cost minimization. The following table 1 describes the comparison between the two models.



Table 1. Illustrate the comparison between the two models Aspect Zhang et. al. (2017) Akbari et. al. (2018)

Object of research Electric ship Charging Stations Electric Vehicle Charging Stations Methods Backup Coverage Model Genetic Algorithm Performance criteria • Number of ship in demand

• The construction cost of charging station • Minimize the total charging cost • Distance between settlements and the

closets charging station Constraint • The ships in the demand zone should go

to the same charging station of a fixed grade at a certain time

• The ship charging requirement must be satisfied

• One charging station of a grade can be constructed in the candidate site

• The average cost per kilometer for the user

• Cost of electricity generated per kWh • Cost contamination controlling for

production • Power utilization of an EV for every

kilometer (kWh/km) Alternative solution 5 charging station sites are selected as

candidate charging sites

Mode 2: Slow charging mode Mode 3: Medium to fast speed charging mode Mode 4: Ultra-fast charging mode

Application/software Matlab 7.0 Genetic Algorithm (GA) Output The location problem is overall, from initial

possible station sites chosen to the capacity determination for the final location sites.

Charging mode of the most precise charging station and suitable location

2.1 Influence Diagram

The influence diagram of optimizing the location of the three-wheel electric vehicle charging station can be seen in Figure 3. The purpose of this research is to optimize the location of the Three-Wheel Electric Vehicle charging station in the city of Surakarta by maximizing coverage and minimizing costs. The constraint is to fulfill all requests and limit the number of facilities to p to keep costs minimal. And the criteria are minimizing the total distance from facilities to customers, minimizing the number of facilities, and minimizing costs.

Figure 3. Influence diagram this research



2.2 Mathematical Modeling The method used is the comparison between the backup coverage models by Zhang et al. (2017) and Akbari et. al. (2018). This model is developed in the context of site facilities required to serve certain requests. In such a situation, the unavailability of facilities within a certain time or standard distance will jeopardize the performance of the system (Zhang et. al., 2017). This method is proposed to make the overall location decision from the initial possible location of the selected station for the determination of the final site capacity. A model that can easily be generalized to other problems regarding user-requested allocation facilities. The objective functions in this model are maximizing coverage and minimizing costs. The decision variables are the location of the charging station and investment costs. The fixed variable it is the location of the charging station, the type of vehicle (in this case, the Three-Wheel Electric Vehicle). Then the variable that changes is the Three-Wheel Electric Vehicle user request. There is one supply chain involved, namely the supply chain network design location for the charging station Three-Wheel Electric Vehicle. Objective function:

𝑍𝑍 = 𝑀𝑀𝑀𝑀𝑀𝑀∑ ∑ 𝐹𝐹𝑖𝑖𝑖𝑖𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖∈𝑀𝑀𝑖𝑖∈𝐼𝐼 +𝑀𝑀𝑀𝑀𝑀𝑀 ∑ ∑ 𝑑𝑑𝑀𝑀𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖𝐶𝐶𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖∈𝐽𝐽𝑖𝑖∈𝐼𝐼 (1) Constraint: ∑ ∑ 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖∈𝑀𝑀 𝑌𝑌𝑖𝑖,𝑖𝑖 ≥𝑖𝑖∈𝐼𝐼 1; ∀𝑗𝑗 ∈ 𝐽𝐽 (2) 𝑌𝑌𝑖𝑖𝑖𝑖 ≤ ∑ 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖∈𝑀𝑀 ; ∀𝑀𝑀 ∈ 𝐼𝐼 , 𝑗𝑗 ∈ 𝐽𝐽 (3) ∑ 𝑑𝑑𝑖𝑖 𝑌𝑌𝑖𝑖𝑖𝑖 ≤ ∑ 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖∈𝑀𝑀 , ∀𝑀𝑀 ∈ 𝐼𝐼𝑖𝑖∈𝐽𝐽 (4) ∑ 𝑋𝑋𝑖𝑖𝑖𝑖 ≤ 1𝑖𝑖∈𝑀𝑀 , ∀𝑀𝑀 ∈ 𝐼𝐼 (5) ∑ ∑ 𝑋𝑋𝑖𝑖𝑖𝑖 𝑖𝑖∈𝑀𝑀 = 𝑃𝑃, ∀𝑗𝑗 ∈ 𝐽𝐽𝑖𝑖∈𝐼𝐼 (6) 𝑋𝑋𝑖𝑖𝑖𝑖 ∈ {0,1},∀𝑀𝑀 ∈ 𝐼𝐼,𝑚𝑚 ∈ 𝑀𝑀 (7) 𝑌𝑌𝑖𝑖𝑖𝑖 ∈ {0,1},∀𝑀𝑀 ∈ 𝐼𝐼, 𝑗𝑗 ∈ 𝐽𝐽 (8) The descriptions for each notation are as follows: 𝐼𝐼 = The set of charging station candidate sites 𝐽𝐽 = The set of demand points 𝑀𝑀 = Set of charging station levels 𝐹𝐹𝑖𝑖𝑖𝑖 = Investment cost to build charging station i level k 𝑑𝑑𝑀𝑀𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖 = The distance between charging station i to demand point j 𝐶𝐶 = Transportation cost per kilometer 𝑑𝑑𝑖𝑖 = demand at node j 𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖 = The service capacity of charging station level k

𝑋𝑋𝑖𝑖𝑖𝑖 = �1, if we choose the candidate site 𝑀𝑀 and transform it into charging station level 𝑚𝑚 0, otherwise

𝑌𝑌𝑖𝑖𝑖𝑖 = �1, if demand point 𝑗𝑗 is covered by a charging station located at 𝑀𝑀0, otherwise

The objective function (1) is to minimize the transformation cost. Constraint (2) states that each demand point should be covered by at least one charging station. Constraint (3) means that the service can be offered only if the charging station is located in this candidate site. Constraint (4) restricts the service capacity of the charging station. Constraint (5) means one candidate site can only be transformed into one-grade charging station. Constraint (6) means we want to locate exactly P facilities. Finally, constraints (7) and (8) are the integrality constraint.

Figure 4. Flowchart for solving the model



Figure 4 above explains the flowchart of this research, which begins with determining the decision variable, then the initial input parameters, then running the model, if the optimal solution has been obtained, the work is finished. But if not, it will be repeated for the input or mathematical model. The following table 2 describes the IEC 61851-1 charging mode which will be used as a limiting level in this study. Charging mode is divided into 4 modes with different specifications and different charging times.

Table 2. IEC 61851-1 charging mode Charging Mode Max Current per Phase Charging Time Vehicle Battery Charger

Mode 1 16 A 4 – 8 h On Board Mode 2 32 A 2 – 4 h On Board Mode 3 63 A 1 – 2 h On Board Mode 4 400 A DC 5 – 30 min Off-Board

Based on IEC 61851-1 charging mode, the following 3 modes are selected which will be the type of charging station to be built (Raboaca, et. al., 2020; Akbari et. al., 2018). a. Mode 2: This is the slow charging mode applied by household type receptacles with the inner wire protection

component in the air conditioner. b. Mode 3: This is a medium to fast speed charging mode that uses a specific EV outlet with built-in AC control

and protection functions. c. Mode 4: This is a very fast charging mode with an external charger in DC. 3. Results and Analysis

3.1. Case Study in Surakarta City This research was conducted with a case study in the city of Surakarta. Surakarta City is a city located in the province of Central Java. With a coordinate point of 7 ° 34′0 ″ S 110 ° 49′0 ″ E and with a city area of 44.04 km2. There are 5 districts in this city with the largest Banjarsari sub-district. The daily transportation used by the people of Solo is various, some are using motorbikes, cars, or public transportation. However, not many people use electric vehicles as private vehicles, because there are no public charging station facilities in the city of Surakarta.

Figure 5. Candidate Three-Wheel Electric Vehicle charging station sites

Figure 5 explains the Candidate Three-Wheel Electric Vehicle charging station sites, in each sub-district in the city of Surakarta. Candidate locations for charging stations use public market facilities and malls. There are 45 markets and



malls in the city of Surakarta, 4 of which are malls. The distance between the charging station candidates and the sub-districts in Surakarta is explained in table 3 below.

Table 3. Distance charging station i to demand point j (in kilometer) No. Market

(CS location) Name of District

Laweyan Serengan Pasar Kliwon Jebres Banjarsari 1 3.4 3.9 3.8 5.5 1.7 2 3.2 4.2 4.2 5.8 1.4 3 3.4 2.8 3.1 5.5 2.6 4 2.4 3.8 4.4 6.5 1.4 5 1.9 4.2 5.0 7.0 1.2 6 3.3 2.9 3.2 5.5 2.5 7 3.6 3.6 3.4 5.2 2.2 8 2.2 4.4 5.0 6.8 0.9 9 1.4 4.0 5.1 7.4 1.8 10 3.3 4.6 4.5 5.9 1.1 11 3.5 4.4 4.1 5.5 1.5 12 3.2 2.5 3.1 5.8 2.8 13 3.2 2.4 3.2 5.9 2.9 14 3.6 2.5 2.8 5.4 3.0 15 3.3 5.0 5.0 6.2 0.8 16 3.4 4.5 4.4 5.8 1.2 17 4.0 4.5 4.0 5.1 1.9 18 4.4 4.4 3.7 4.7 2.3 19 2.0 3.5 4.5 6.9 1.9 20 5.5 3.0 1.3 3.5 4.4 21 5.1 3.1 1.8 3.8 3.8 22 5.4 3.7 2.1 3.4 3.9 23 5.0 4.2 2.9 3.9 3.1 24 7.0 5.3 3.0 1.9 5.1 25 6.6 5.9 4.1 3.1 4.3 26 5.0 5.0 3.9 4.4 2.7 27 5.1 4.3 3.0 3.8 3.2 28 6.7 5.4 3.4 2.4 4.7 29 5.1 4.3 3.0 3.8 3.2 30 4.5 3.3 2.5 4.3 3.1 31 6.3 5.6 3.8 3.2 4.0 32 1.0 5.2 7.0 9.7 3.6



Table 3. Distance charging station i to demand point j (in kilometer) continued No. Market

(CS location) Name of District

Laweyan Serengan Pasar Kliwon Jebres Banjarsari 33 1.1 3.8 5.2 7.8 2.5 34 2.3 2.8 4.1 6.8 2.7 35 1.7 3.2 4.9 7.7 3.0 36 1.3 3.8 5.7 8.5 3.4 37 0.6 4.5 6.3 8.9 3.2 38 2.0 3.1 4.4 7.0 2.4 39 5.7 2.2 0.7 4.1 5.0 40 5.0 2.0 1.4 4.5 4.3 41 4.7 1.8 1.7 4.8 4.2 42 4.3 2.7 2.3 4.6 3.3 43 5.7 2.3 0.6 3.9 5.0 44 5.7 2.3 0.7 4.0 4.9 45 4.2 1.4 2.2 5.4 4.1

3.2. Numerical Example

Due to the addition of Three Wheel Electric Vehicle / Three-Wheel Electric Vehicle users every month, in this study researchers used a numerical example. For example, in running the location model for the Three-Wheel Electric Vehicle charging station in the city of Surakarta. Table 4 explains the numerical example of the request for a Three-Wheel Electric Vehicle in each sub-district in the city of Surakarta, which is obtained from multiplying the market share of 5% with the population in the sub-district in the city of Surakarta.

Table 4. A numerical example for demand Three-Wheel Electric Vehicle

No District Total Demand 1 Laweyan 101196 5060 2 Serengan 53923 2696 3 Pasar Kliwon 84126 4206 4 Jebres 144175 7209 5 Banjarsari 178849 8942

Total 562269 28113 The roadmap for public electric vehicle battery exchange stations for two-wheeled or three-wheeled vehicles in the presentation of the Opportunities and Challenges of National Electric Car Development states that Indonesia is preparing investment funds for the manufacture of battery swap charging stations totaling 342 billion with a target of 800,000 electric motorbike users and 4000 charging stations (Hendra, 2020). So it can be said that the investment cost in building 1 charging station is 85,500,000 with charging mode 2 specifications. And it is assumed that it will increase by 10% for the faster charging mode. So that it can be seen the investment costs of the charging station in table 5 below.

Table 5. Investment cost charging station Three-Wheel Electric Vehicle

Charging Mode Max Current per Phase Charging Time Output Power

(kW) Investment cost

(IDR) Mode 2 63 AC 4 h ≤ 22 kW 85.500.000 Mode 3 100 AC/250DC 30 min ≤ 50 kW 94.050.000 Mode 4 300 AC/500DC 15 min ≤ 150 kW 102.600.000



3.3. Results and Discussion Table 6 below describes the optimal location from the results of running using ILOG CPLEX at the location of the Three-Wheel Electric Vehicle charging station by considering the optimal coverage area in the city of Surakarta. So that the results are obtained in Figure 6, which illustrates the points of the selected market/mall as the charging station location. The result has shown that all level 3 are selected to minimize costs.

Table 6. Location of charging station Three-Wheel Electric Vehicle selected (Xij)

Candidate sites

P = 3 P = 4 P = 5 Lvl 1 Lvl 2 Lvl 3 Lvl 1 Lvl 2 Lvl 3 Lvl 1 Lvl 2 Lvl 3

8 v 15 v v v 24 v v 37 v v v 44 v v 45 v

Table 7 below shows the results for the Yij decision variable. This table shows which candidate locations are supportive of the point of demand. Because there are 5 points of demand, the results must meet the demand. And the following results are obtained, in the scenario of making 3 CS, candidate sites (8) will be created to supply Laweyan and Banjarsari Demand points, to candidate sites (24) to supply Jebres demand points. And candidate sites (43) to supply-demand points for Serengan and Pasar Kliwon. Then in the scenario of making 4 CS, the result is that at candidate sites (24) to supply Jebres, candidate sites (15) supply Banjarsari, candidate sites (37) to supply Laweyan. And candidate sites (43) to supply Serenggan and Pasar Kliwon. Then in the scenario of making 5 CS, 5 candidate sites are selected to supply each of the existing demand points according to the district candidate sites, namely (15), (24), (37), (43), and (45). Figure illustration can be seen in Figure 6.

Table 7. Result for Yij

Candidate sites

P = 3 P = 4 P = 5

Law

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Sere

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Pasa

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Jebr

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Ban

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Law

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Sere

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on

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Ban

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Law

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on

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8 v v 15 v v 24 v v v 37 v v 43 v v v v v 45 v



P = 5 P = 4

P = 3

Figure 6. Optimal Locating Charging Station Three-Wheel Electric Vehicle

4. Conclusion

In this research, the Three-Wheel Electric Vehicle charging station location is proposed to solve the problem of optimum charging location to support the acceleration of electric vehicles, especially three-wheeled electric vehicles, with a case study in the city of Surakarta. This model has been implemented by considering 3 levels/scenario types of charging stations. This model can provide input to local governments regarding the optimal solution in determining the location of the Three-Wheel Electric Vehicle charging station. However, the weakness of this model is that it still uses numerical examples in determining the user demand for Three-Wheel Electric Vehicle, so that the results obtained do not fully cover the demand in Surakarta. And also transportation costs and investment costs are still using the assumptions of researchers. This study also has not considered additional parameters such as queues or traffic density due to the limitations of the model used. So it is hoped that the next research can use real and primary data so that it can answer problems real and accurately according to real conditions. Acknowledgments This research was supported by the Students Exchange of Merdeka Belajar Kampus Merdeka (MBKM) Program between the Department of Industrial Engineering Universitas Sebelas Maret and Universitas Diponegoro.



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Biographies Nida An Khofiyah is a student at the Master Program of Industrial Engineering Department, Universitas Sebelas Maret, Surakarta, Indonesia. She is also a research assistant in the Laboratory of Logistics System and Business. She received her Bachelor of Engineering degree from Universitas Sebelas Maret. Research interests are related to techno-economics, logistics, commercialization technology, and drone technology. She has published research papers twice during her final year in engineering associated with the commercialization of drone technology and drone batteries. She has published 6 articles Scopus indexed. Ananda Vania Arisa Putri is a student in the Bachelor Program of Industrial Engineering, Diponegoro University, Semarang, Indonesia. She is still a graduate student of Industrial Engineering, Diponegoro University. Her research interests are health safety and environment, logistics and supply chain management, and procurement. She already has several certificates such as the Certificate of Young Expert in Construction Occupational Safety and Health, Project Risk Management certificate, and Basic Logistics certificate. Wahyudi Sutopo is a professor in industrial engineering and coordinator for the research group of industrial engineering and techno-economy (RG-RITE) of Faculty Engineering, Universitas Sebelas Maret (UNS), Indonesia. He earned his Ph.D. in Industrial Engineering & Management from Institut Teknologi Bandung in 2011. He has done projects with Indonesia endowment fund for education (LPDP), sustainable higher education research alliances (SHERA), MIT-Indonesia research alliance (MIRA), PT Pertamina (Persero), PT Toyota Motor Manufacturing Indonesia, and various other companies. He has published more than 160 articles indexed Scopus, and his research interests include logistics & supply chain management, engineering economy, cost analysis & estimation, and technology commercialization. He is a member of the board of industrial engineering chapter - the institute of Indonesian engineers (BKTI-PII), Indonesian Supply Chain & Logistics Institute (ISLI), Society of Industrial Engineering, and Operations Management (IEOM), and Institute of Industrial & Systems Engineers (IISE). His email address: [email protected].



Documents

Location Optimization Charging Station of Three-Wheel