International Journal of Technology Management and Information System (IJTMIS) eISSN: 2710-6268 [Vol. 4 No. 2 June 2022]
Journal website: http://myjms.mohe.gov.my/index.php/ijtmis
LOGISTICS DISTRIBUTION SYSTEM PLANNING USING DRONE CASE STUDY IN JAKARTA
Reza Pahlevi1*, Nahry2* and Sutanto3*
1 2 3 Faculty of Engineering, University Indonesia, Depok, INDONESIA
*Corresponding author: [email protected], [email protected], [email protected]
Article Information:
Article history:
Received date : 12 May 2022 Revised date : 24 May 2022 Accepted date : 11 June 2022 Published date : 29 June 2022
To cite this document:
Pahlevi, R., Nahry, N., & Sutanto, S.
(2022). LOGISTICS DISTRIBUTION SYSTEM PLANNING USING DRONE CASE STUDY IN JAKARTA.
International Journal of Technology Management and Information System, 4(2), 1-12.
Abstract: Jakarta is the capital city of Indonesia with high population growth and high transportation activity, the transportation sector is the second largest contributor to greenhouse gases (GHG) after the industrial sector.
Meanwhile, the emergence of e-commerce which has an impact on increasing people to shop boldly. This has resulted an increase delivery of goods to the final consumer (Last Mile Delivery) in small volumes with a higher frequency. This condition has negative impacts such as traffic congestion, increased carbon emissions and the number of accidents. Unmanned Aerial Vehicle (UAV) can be a solution to overcome shipping problems. The purpose of this study is to evaluate the effectiveness of the drone distribution system on last mile delivery activities in Jakarta by considering internal and external costs.
Distribution system planning by forming a Heterogeneous Fleet Vehicle Routing Problem with Drone and External Costs (HFVRPDEC) model and applying the model using data from a parcel delivery company in Jakarta and conducting distribution simulation by forming 9(nine)scenarios. The research variables consisted of the type of vehicle and distribution system, the type of vehicle consisted V1 (Drone), V2 (Motorcycle), V3 (Pick-up box car), while the distribution system consisted of one-tier, two-tier and multi-tier. The result of Scenario shows managed to reduce the distance travelled 53% with a combination of drones and pick-up, while emissions can be reduced by 100% with drone compared to the existing condition. The result of study expected to provide solution Vehicle Routing Problem, benefits for operators, good
service for customers, and minimize the impact of negative externalities, especially air pollution due to damage.
Keywords: Drones, Distribution System, Vehicle Routing Problem, Cost, Emission.
1. Introduction
According to the Institute for Essential Service Reform (IESR), the transportation sector is the second largest contributor to greenhouse gas (GHG) emissions after the industrial sector in Jakarta.
Meanwhile, the emergence of e-commerce has an impact on increasing people shopping online.
The number of e-commerce users in 2020 reached 39.9 million in Indonesia. The main factors that affect people shopping are price, convenience, and time savings (Akhmad Hidayatno et al, 2019).
Goods purchased through e-commerce tend to ship in small volumes so they are shipped at a higher frequency. According to (J. Olsson et al., 2019), Last Mile Delivery (LMD) is one of the most expensive, inefficient, and most polluting parts of the supply chain, accounting for around 13-75%
of the total supply chain costs. Unmanned Aerial Vehicle (UAV) can be an alternative solution of cost and delivery time to overcome the problem of LMD delivery (K. Dorling et al., 2017). With respect to carbon dioxide emissions, drone delivery has advantages over truck delivery and is more environmentally friendly (A. Goodchild and Toy, 2018). The purpose of this study is to evaluate the effectiveness of the drone distribution system on last mile delivery activities in Jakarta by considering internal and external costs. This paper is arranged in the following order: Section 2 explain the literature review. Section 3 explain the methodology that cover entire research procces data analysis and mathematical model development. Section 4 show the scenario and evaluation result. Section 5 conclusion and further research.
2. Literature Review
Various studies on vehicle routing problems with drones have been developed and various models and objective functions have succeeded in optimizing the solution to the VRP problem. (Sudipta Chowdhury et al., 2021) drones are more effective for delivery in areas drone to natural disasters.
(Miguel Figliozzi, Jesus Saenz, Javier Faulin, 2020) that affect the carbon cycle are vehicle routes, vehicle capacity and delivery duration. (Anne Goodchild, Jordan Toy, 2018) and (Miguel A.
Figliozzi, 2020) drones have lower CO2 emissions compared to trucks for short distance delivery services trucks have the advantage of CO2 emissions for long distance deliveries VMT drones over trucks because they have to return to the depot after delivery.
In another study the results show that the global warming potential (GWP) per 1 km of shipments by drone is one-sixth of that of motorcycle shipments, and particulates generated by drone shipments of half of motorcycle shipments. The actual reduction in environmental impact taking into account delivery distances is 13 times higher in rural areas than in urban areas (Jiyoon Park, Solhee Kim, and Kyo Suh, 2018).The use of more than one type of vehicle with a combination of drones to save costs in mathematical models has also been widely developed, such as in (Patchara
Wang, Jiuh-Biing Sheu , 2019),( Minh Anh Nguyen, et al., 2020), Bo Lan (US,2020), (Sooyeon Kim et al., 2020),(Cl´ement Lemardel´ et al., 2021), in general from In these seven studies, Vehicle Routing Prolem with Drone (VRPD) saves an average of 20% in costs. Based on research (Talitha, 2020) the heterogeneous fleet vehicle routing problem model or abbreviated HFVRP has been developed into Heterogeneous Fleet Vehicle Routing Problem with Time Window and External Costs (HFVRPTW-EC) which has succeeded in reducing total costs by 34% - 44% and reducing pollution concentrations air by 38% - 47% for motorcycles and pick-up box cars fueled by gasoline while for motorcycles and pick-up box cars fueled by CNG (Compressed Natural Gas) a total cost reduction of 41% - 50% and a reduction in air pollution concentration by 50% - 58%.
In research (Hong Jiang, Xinhui Ren, 2020) the results show that from several scenarios drones excel in shipping over a distance of 7 km, while for short distances you have to increase drone airports because drones cannot deliver door to door while research (Gilang Rizky Pratama, et al.
al., 2020) compared the TSP, FSTSP, and PDSTSP models to see which model is the most effective for minimizing delivery times using one drone in last-mile logistics.
The results of a comparison of 15 instances with six to ten customers show that FSTSP and PDSTSP are more effective than TSP in terms of delivery mission completion time. In addition, a pattern was also found that in the data with the average distance of the customer to the nearest depot, PDSTSP is more effective to implement, and vice versa if the average distance between the customer and the depot is further, then the FSTSP is more effective in achieving the fastest delivery time.
2.1 Problem Statement
Based on the literature study that has been carried out, the gaps found are as follows:
a) In Indonesia, there has been no research on the distribution system using drones in Jakarta b) Previous studies have not implemented the LMD distribution system with a combination of vehicles using drones, delivery time windows conditions and considering external costs for case studies in Jakarta.
3. Method
This research has several stages of research. The stages begin with identifying the problem, then determining the formulation of the problem and the objectives to be achieved. The next stage is to conduct a literature study to obtain guidelines and basic knowledge related to the issues discussed.
The next stage is the determination of research variables to identify the needs of the data used in the study. Next is to collect data from secondary data. After all the data has been collected, interpretation and data processing are carried out so that the data can be used for further processing.
The next process is the process of developing a mathematical model. Furthermore, planning and model application using software are carried out. The analysis process includes comparison of total costs, comparison of exhaust emission concentrations, evaluation of distribution scenarios and sensitivity analysis of research variables. The last stage is making conclusions regarding the results of the analysis carried out and suggestions for input for further research development.
3.1 Materials
The study area is focused on one of the depots of an express delivery service company in Jakarta, Indonesia. The depot that is the focus of this study is located in Rawamangun Village, Pulo Gadung District, East Jakarta City.
3.1.1 Samples
Interpretation and processing of data by processing from raw data into data that is ready to be processed for the next stage. The data is processed as needed for model development and analysis process. The data processing carried out is to make a data recapitulation at one depot in one week.
The number of requests or end users for seven days is 1514 demand points.
The type of vehicle used in the existing condition for seven days is a motorcycle with a box installation at the back. the highest number of demands was on Friday with 296 demand points, while the lowest number of demands was on Sunday with 181 demand points. The highest number of vehicles was on Thursday with 69 motorcycles, while the lowest number of vehicles was on Sundays with 47 motorcycles.
3.1.2 Site
The data used is secondary data from an express delivery service company in Jakarta, Indonesia, which is not named for privacy purposes. This express delivery service company or 3PL (Third Party Logistics) has same day delivery services.
3.1.3 Procedures
The data that is recapitulated is secondary data. The data processing includes the data cleaning process by deleting incomplete or incomplete data, irrelevant data and duplication of data. All data that has been processed in this process is then used for the existing model development stage and scenario development using software. The seventh stage is the development of a mathematical model to form the objective function and the constraints of the defined objective function.
Design: Planning what kind of distribution scenario needs to be done to get the best distribution solution. The distribution scenario carried out considers the combination of vehicles and distribution network systems. The types of vehicles considered are Drone (V1) motorbike (with box installation) (V2) and pick-up box truck (V3). Meanwhile, the distribution network system considered is one-tier system, two-tier system and multi-tier system. The distribution scenario plan that has been determined is used for the model application process using software. The software used is VRP Spreadsheet Solver from (Gunes Erdogan, 2017).
Variables: Research variables are divided into two, namely independent variables and dependent variables. The independent variables in this study are the use of modes and distribution systems, while the dependent variables in this study are internal costs, external costs, total costs and exhaust emission concentrations.
Power and sample size: The depot that is the focus of this study is located in Rawamangun Village, Pulo Gadung District, East Jakarta City.
This study evaluates the effectiveness of the distribution system using drones with various delivery scenarios compared to using motorbikes and trucks by considering internal and external costs, as well as calculating the concentration of exhaust emissions.
3.2 Measurement
The model application with software is divided into two, namely for existing conditions and scenarios using drones. The existing model that is formed is an actual condition or a condition that actually occurs. The results of the existing condition model and scenario using drones are the total value of the trip length or vehicle kilometers traveled (VKT). While the calculation for costs using the equation:
𝐸 =𝐸𝐶𝑚.𝑉𝐾𝑇𝑖
𝑛𝑖 (3.1).
where:
E : Average Cost of Each Vehicle/Parcel
ECm: Cost Index Based on Regional Morphology (Urban/SubUrban/Rural) VKTi: Travel Length of Each Vehicle (km)
ni : Number of Stops / Delivery
Based on equation 3.1, the ECm value or cost index for the internal cost index is obtained through the calculation of vehicle operating costs in 2021. While the external cost index for urban areas is obtained from (Victoria transport institute study, 2020). The negative externalities considered are exhaust gas emissions which include carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), sulfur oxides (SO2), particulates 2.5 (PM2.5) and particulates 10 (PM10) for urban areas.
In Indonesia. Equation 3.1 produces internal costs, external costs and total costs for both existing conditions and scenarios using drones. The total cost is the sum of the internal and external costs of IDR/km multiplied by VKT (km). In addition to cost, the concentration of exhaust emissions can also be determined by multiplying the index of concentration of exhaust emissions in grams/km by the value of VKT (units of km), so that the value of the concentration of exhaust emissions is obtained for existing conditions and scenarios using drones.
3.3 Data Analysis
The analysis is carried out based on the calculation results, it is found that the total cost of delivery and the concentration of exhaust emissions in the existing conditions using motorbikes / trucks and using drones. The first analysis is to do a cost comparison (internal, external and total) for the existing condition with a scenario using a drone, the second analysis is to compare the concentration of air pollution for the existing condition with a scenario using a drone. The comparison was carried out to determine whether the cost components and exhaust emission concentrations of the scenarios using drones had succeeded in reducing the cost components and concentrations of exhaust gas emissions in the existing condition. The third analysis is to evaluate
the scenario based on the success parameters, namely the smallest total cost (internal and external) and the smallest exhaust emission concentration. Scenario evaluation is carried out to obtain the most effective scenario that considers the combination of vehicles and distribution network systems. The fourth analysis is to analyze the sensitivity of the research variables. In this case, it can be seen whether the independent variables in this study can be said to be sensitive to the dependent variable
3.3.1 Validity and Reliability
Various variations of VRP have been developed, one of the variations that are often used are VRP with Heterogeneous Fleet, VRP with Drone and VRP with Time Window. In this study, modifications of the three types of VRP models were developed, the VRP modification in this study is Heterogeneous Fleet Vehicle Routing Problem with Drone and External Cost (HFVRPDEC). The definition of HFVRPDEC is a problem of route optimization to minimize internal costs, external costs and air pollution concentrations by using more than one type of vehicle combined with drones. The formulation of the HFVRPDEC mathematical model begins by defining the objective function and then forming the constraints. is the origin of the trip and is the destination of the trip. Assume N = {0, 1, 2, n, n+1} as a collection of consumers / end users and depots (0 and n+1 is depot notation) and ∈ 𝑁. k is a vehicle of one type of vehicle m, where k
= {0, 1, 2, …, Km} and k Km. Km is the set of vehicles for each type of vehicle. Type of vehicle m = {0, 1, 2, …, M} and m M where M is a set of different types of vehicles. The following is the formulation of the HFVRPDEC mathematical model.
Min Z =
∑
𝑀
𝑚=1 𝑚𝜖𝑀
∑
𝑘𝑚
𝑘=1 𝑘𝜖𝐾𝑚
∑
𝑛
𝑖=0
∑
𝑛+1
𝑗=1
(𝛼𝑚 + 𝛽𝑚(𝐶𝑖𝑗𝑘𝑚 . 𝑋𝑖𝑗𝑘𝑚))
(3.2)
MinY=
∑
𝑀
𝑚=1
∑
𝑘𝑚
𝑘=1
∑
𝑛
𝑖=0
∑
𝑛+1
𝑗=1
(𝜎𝑚. 𝐶𝑖𝑗𝑘𝑚 . 𝑋𝑖𝑗𝑘𝑚)
(3.3)
Constrain:
∑
𝑀
𝑚=1 𝑚𝜖𝑀
∑
𝑘𝑚
𝑘=1 𝑘𝜖𝐾𝑚
∑
𝑛
𝑖=0
∑
𝑛+1
𝑗=0
𝑋𝑖𝑗𝑘𝑚 = 1 ;∀𝑖𝑗 ∈ 𝑁 ; 𝑁
≠ 0 (3.4) ∑
𝑛
𝑗 =1
𝑋𝑜𝑗𝑘𝑚 ≤ 1 ; ∀𝑘
∈ 𝐾𝑚 ; ∀𝑚 ∈ 𝑀 (3.5)
∑
𝑛
𝑖 =0
𝑋𝑖(𝑛 + 1)𝑘𝑚 ≤ 1 ; ∀𝑘 ∈ 𝐾𝑚 ; ∀𝑚 ∈ 𝑀 (3.6)
∑
𝑛
𝑖=0
∑
𝑛+1
𝑗=1
ℎ𝑚. 𝑋𝑖𝑗𝑘𝑚 ≤ 400 𝑓𝑡 ; ∀𝑘 ∈ 𝐾𝑚 ; ∀𝑚 ∈ 𝑀 (3.7)
∑
𝑛
𝑖=0
∑
𝑛+1
𝑗=1
𝑑𝑗. 𝑋𝑖𝑗𝑘𝑚 ≤ 𝑄𝑚 ; ∀𝑘 ∈ 𝐾𝑚 ; ∀𝑚 ∈ 𝑀 (3.8)
𝑡 ≤ 𝑡𝑜𝑘
(3.9)𝑡′ ≤ 𝑡𝑒
(3.10) X𝑖𝑗𝑘 ∈ {0,1} ; ∀𝑖𝑗 ∈ 𝑁 ; ∀𝑘 ∈ 𝐾𝑚 ; ∀𝑚 ∈ M (3.11)The objective function of the HFVRPDEC model is divided into two, the first objective function is to minimize the total cost Z which includes internal costs and external costs. The second objective function is to minimize the concentration of air pollution Y. The internal cost is obtained by multiplying the internal cost index (Rp/km) with the total length of the trip (km). It is the same with external costs, namely the multiplication of the external cost index (Rp/km) with the total trip length (km). The concentration of air pollution is obtained by multiplying the air pollution index (gram/km) with the total length of the trip (km). The total length of the trip is the sum of the trip lengths of each k vehicle with m vehicle types. In this objective function, it is assumed that the length of the trip (km) is directly proportional to the cost and the distance proxy used has been calibrated with the travel time. Constraints (3.4) show that each consumer is visited only once.
Constraints (3.5) show that all vehicles start or leave the depot (3.6) indicate that all vehicles end up at the depot, while constraints (3.7) are the height limit of one k vehicle, hm is the total height of j and 400 ft is the maximum height limit of the type. vehicle k. Constraints (3.8) is the carrying capacity of one k vehicle, dj is the number of consumer packages j and Qm is the maximum package capacity of k vehicle types. Constraints (3.9) and (3.10) are delivery time window constraints where ts is the earliest time to start LMD vehicle operations, te is the latest time to start LMD vehicle operations, tok is the earliest departure time from the depot, t'ok is time the latest arrival to the depot (3.11) is a binary variable that only has a value of 0 or 1, if Xijkm = 1 then path is traversed by k vehicles of m type of vehicle, and vice versa if Xijkm = 0 then path is not traversed by k vehicles of type vehicle m.
4. Results and Discussion
The application of the HFVRPDEC model with VRP Solver was divided into two conditions, namely the existing conditions and the scenario conditions. Time window starting at 07.00 - 22.00.
Considering that the express delivery service analyzed had the same day delivery service, within that time window, the goods delivered must have arrived at the customer/end user. Analysis of the existing conditions was carried out in one week (7 days) and then were selected representing peak days.
Figure 1: VKT and Number of Packages in Existing Conditions
Based on Figure 1, it can be seen that the peak day condition was Friday with the highest number of VKT value and packages compared to other days. It was normal reasoning that Friday is the last day to the weekend, so that the number of same day package delivery demands increased. scenario planning is presented in Table 1.
Table 1: Scenario Planning
Scenario Types of Vehicle Distribution System
Existing V2 One Tier System
SK-1 V1 One Tier System
SK-2 V3 One Tier System
SK-3 V1 Two Tier System
SK-4 V2 Two Tier System
SK-5 V3 Two Tier System
SK-6 V1&V3 Two Tier System
SK-7 V2&V3 Two Tier System
SK-8 V2&V3 Multi Tier System
SK-9 V1&V3 Multi Tier System
Application of the HFVRPDEC model using software produced the following results: Figure 2 presents the total VKT for each type of vehicle and each scenario including the existing conditions, Figure 3 presents the vehicle operating costs for each scenario including the existing conditions Figure 4 presents the vehicle emission for each scenario including the existing conditions.
Figure 2: Travelled Vehicle Kilometer
According to Figure 2, it can be seen that the existing conditions produce VKT values that are much more than the eight scenarios, this is because the existing not optimize the travel route with alternative mode, and scenario 4 higher than other because scenario 4 do not combination with other mode. The calculation of the internal cost index or vehicle operating cost is based on market price conditions in 2022. Based on the calculations, it is found that the internal cost index for V1 vehicle type is Rp.7,922 /km, for the V2 vehicle type is Rp. 7,404 /km, for the V3 vehicle type is Rp. 19,115 /km and the external cost index for V1 vehicle type is Rp.329.2 /km, for the V2 vehicle type is Rp. 651.9 /km, for the V3 vehicle type is Rp. 1796.29 /km This index is then used to determine vehicle operating costs for each scenario shown in figure 4 show emission produced for each scenario with Emission index for V1 vehicle type is 0.079 g/km, for the V2 vehicle type is 23.49 g/km, for the V3 vehicle type is 57.326 g/km.
Figure 3: Total Cost (Internal and external)
Figure 4: Total Emission
Based on Figure 3 and 4, it can be analyzed that the existing conditions produce VOC and VOE values that are much more than the eight scenarios. This is because the index value, VKT of the existing conditions and scenario 2 is the largest compared to the eight scenarios In the existing conditions, no route optimization with alternative or combination mode, so when a package needs to be delivered, the operator tends to rush in delivering the package, so that in many cases, one vehicle only delivers one package and one mode. The absence of optimization with alternative or combination mode that is carried out operationally can result in the large value of VKT and affect the amount of operational costs for the delivery. In general, SK-9 is the best scenario with the largest percentage reduction in operating costs and emission for peak day conditions. The use of the combination V1 and V3 type of vehicle with multi tier system because the operating cost index and emission index for V1(drone) vehicles is much less than V2andV3. SK-3 was a scenario that was closest to the existing condition use V1 type of vehicle with two tier system because total of drone is almost the same as the current condition even though it uses drones, because the number of drone vehicles is more to deliver goods, the limitation of drones is that they can only cover distances within their radius. However, both SK-6 and, SK-9 resulted in a reduction in operating costs of more than 40%, and more than 70% total emission so that the use of the variations in vehicle types with drone. Just using a drone can lower 5% operating costs and 100% emission.
Therefore, it can be said that the application of route optimization with drone results in a significant reduction in emission and operational costs compared to the existing condition.
5. Conclusion
Optimization of the last mile delivery route with the HFVRPDEC model combination of drone in urban areas resulted in an average reduction in operating costs of 69.56% and total emission 70%- 100% compared to the existing conditions. The combination of scenarios analysed resulted in a reduction in operational costs by more than 40% for peak day conditions. The best scenario is SK- 9 use of the combination V1 and V3 type of vehicle with multi -tier system reduction in operating costs of 79% and total emission 100% compared to the existing conditions. However, it needs to be examined more deeply considering that the addition of new types of vehicles and new technology to logistics operations. The limitations in this paper can be devel.
6. Acknowledgement
This research was supported by the Directorate of Research and Development of Universitas Indonesia in the 2022 University of Indonesia Research Grant Program.
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