delivery management system using the clustering ...

International Journal of Industrial Engineering, 21(2), 1-13, 2014

DELIVERY MANAGEMENT SYSTEM USING THE CLUSTERING BASED MULTIPLE ANT COLONY ALGORITHM: KOREAN HOME APPLIANCE DELIVERY Taeho Kim and Hongchul Lee School of Industrial Management Engineering, Korea University, Seoul, Republic of Korea

This paper deals with the heterogeneous fleet vehicle routing and scheduling problems with time windows (HFVRPTW) in the area of Korean home appliance delivery. The suppliers of modern home appliance products in Korea not only have to provide the traditional service of simply delivering goods to customers within the promised time, but they also need to perform additional services such as installation of the product and explanation of the products functions. Therefore, businesses reducing the delivery cost while improving the quality of the service experienced by customers is an important issue. In order to meet these two demands, we generated a delivery schedule by using a heuristic clustering-based multiple ant colony system (MACS) algorithm. In addition, to improve service quality, we set up an expert system composed of a manager system and an android-based driver system. The system was tested for home appliance delivery in Korea. This paper is significant in that it constructs an expert system for the entire process of distribution, from the generation of an actual schedule to management system setup. Keywords: HFVRPTW, Ant colony algorithm, Home appliance delivery, Android; Information System

1. INTRODUCTION The physical distribution industry is facing a rapid change in its business environment due to the development of information and communication technology and the spread of Internet accessibility. Products are ordered both online and offline. Through online communities, customers can freely share information relating to the entire process of product purchasing such as product functions, delivery and installation. In particular, products handled in home appliance delivery in recent years, like Smart TVs, notebooks, air conditioners and refrigerators, have complex functions in contrast to their predecessors. Hence why it is important to provide installation and demonstration service while guaranteeing accurate and timely delivery. Such extended services have actually become an important factor for customers in building an image of a given company. Accordingly, separately from the traditional work of simply delivering a product to a customer, qualitative improvements of service, like product installation and explanation of product functions, have become an important part of home appliance delivery in Korea (Kim et al., 2013). From the companies’ point of view, reducing delivery costs while improving the quality of delivery service experienced by customers is an important problem. Basically, the problem of satisfying the constraints of delivery time desired by customers while finding the shortest traveling route for vehicles is known as the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW model is a representative NP-hard problem (Lenstra and kan, 1981; Savelsbergh, 1985). There are many studies that have used metaheuristics to solve this problem (Cordeau et al., 2001; Haghani and Banihashemi, 2002; Sheridan et al., 2013). In this paper, we used the ant colony system (ACS) among the various metaheuristic methods to generate schedules (Dorigo and Gambardella, 1997a, 1997b). ACS has the advantage of being able to respond flexibly even when the constraint rules change. We also utilized a heuristic clustering algorithm in this paper to improve the calculation speed of the local search part that requires the longest calculation time among the ACS processes (Dondo and Cerdá, 2007). A delivery management system is required for qualitative delivery service improvement. (Santos et al., 2008; Moon et al. 2012). We constructed an Android-based delivery management system to flexibly handle such problems as delivery delays and delivery sequence changes that can occur due to the characteristics of delivery work. With this system, managers can easily manage various accidents that can occur during deliveries and more effectively monitor the locations of drivers and manage the delivery progress rate as well as idle drivers.

2. LITERATURE REVIEW Ever since Dantzig and Ramser (1959) attempted to solve the vehicle routing problem (VRP) by using an LP heuristic, many researchers have introduced various mathematical models and solutions. Of the VRP types, VRPTW is the VRP with the customer-demanded time constraint. Since VRPTW is an NP-hard problem, an optimum solution within the restricted time cannot be found. Studies related to VRPTW have advanced greatly with insertion heuristic research ISSN 1943-670X

 INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING

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(Solomon, 1987) as the starting point. Supported by recent advances in computer technology, studies applying metaheuristic methods such as simulated annealing (Osman,1993; Czech and Czarnas, 2002; Lin et al., 2011), tabu search (Nanry and Wesley Barnes, 2000; Cordeau and Laporte, 2003), genetic algorithms (Berger and Barkaoui, 2004; Lai et al., 2012), evolution strategies (Homberger and Gehring, 1999), and ACS (Yu and Yang, 2011) to VRP are being widely conducted. Schulze and Fahle (1999) and Gehring and Homberger (2002) have proposed a parallel tabu search heuristic algorithm that can solve large-scale VRPTW. Zhang et al. (2007) used residual capacity as heuristic information and applied it to ACS, and used ASRank and an MMAS strategy for pherormon update. Gambardella et al. (1999) defined Multiple Ant Colony System for VRPTW that solved VRPTW by applying ACS. From an analysis of the existing studies mentioned above, it was found that a superior solution could be derived not from using a heuristic alone but using it together with a different heuristic or a metaheuristic. Also, recent trends in metaheuristic algorithm research show that methods of saving, insertion, and nearest neighbor are used in obtaining the initial solution and that majority of metaheuristics combine improvement type solutions such as local search techniques like 2-opt or Or-opt (Zachariadis and Kiranoudis, 2011). We generated a delivery schedule by using a heuristic clustering-based multiple ant colony system algorithm among the various metaheuristic algorithms. The ACS is a metaheuristic method that copies the ant’s ability to find the shortest route from a food source back to its nest. The ACS algorithm easily incorporates complex constraint conditions, and since searching is done with the artificial ants moving at the same time, a solution that comprehensively reflects the characteristics of superior solutions can be found. (Mullen et al., 2009).

3. DELIVERY MODEL DEFINITION 3.1 Current state analysis The Korean home appliance delivery model in this paper differs from the traditional delivery model. As previously stated, home appliance products used today need to be not only delivered within the promised time to the purchasing customer, but also installed and demonstrated. This paper is based on the delivery model of Korean electronics company A. The delivery process of Korean electronics company A consists of a driver and an installation/service technician riding together in a delivery truck starting from the delivery center to deliver and install products and provide service in sequential order. 3.2 Problem definition The mathematical model for the proposed delivery system is as follows. A vehicle has V number of customers including the delivery center (V0). Vehicle load capacity (qv) is different for each vehicle. The cost of traveling by a vehicle is composed of travel cost and fixed cost. Parameters C: customers (C0: delivery center) V: vehicles qv: load capacity of vehicle v di: demand at customer i ai: earliest service time at customer i bi: latest service time at customer i t vi : vehicle arrival time at customer i wiv : waiting time at customer i by vehicle v t vij : travel time from i to j by vehicle v cjiv : travel cost from i to j by vehicle v fcv: fixed cost for using vehicle v mvi : moving time from vehicle v to customer i st vi : service time at customer i by vehicle v loti: longitude at customer i lati: latitude at customer i Tmax∶ maximum working time for vehicle M: Big M Decision variables 2

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xijv = {

1, if vehicle v travels from i to j 0, otherwise

mi (∑ ∑ ∑ cijv xijv v

i

∑ fcv )

(1)

j

subject to: ∑ ∑ xijv v

for i = 0

∑ ∑ xijv i

(2)

j i

v

for v

(3)

j

∑ ∑ xijv = 1 for j i

(4)

j

∑ xijv = ∑ xjiv i

1

(5)

j

∑ xiiv = 0 for i v

aj

t vi

t vi

mvi

mvi

(6) st vi

st vi

t vij

t vi

(xijv

1) (xiv

j for i 1) for i

, j , v

, v

(7) (8)

Objective function 1 is for minimizing the delivery cost (travel cost and fixed cost). Equation 2 is the constraint on the number of vehicles available for use. Equation 3 is the constraint on vehicle load capacity. Equation 4 is the constraint of allowing only one visit per customer. Equation 5 is the constraint that a vehicle must return to the delivery center. Equation 6 is the constraint that customers visited cannot be revisited. Equation 7 is the delivery time constraint. Equation 8 is the constraint that a vehicle must return within fixed business hours.

4. CLUSTERING ANT COLONY SYSTEM FOR HFVRPTW The problem dealt with in this paper is a multi-objective problem of calculating the number of vehicles and the vehicle schedule when vehicle capacities differ. The overall architecture for solving HFVRPTW is shown in Figure 1. This architecture is based on the multiple ant colony system (MACS) (Gambardella et al., 1999). MACS-HFVRPTW is comprised of two ACSs with different objectives: Ant Colony System-Delivery Vehicle (ACS-DV) that calculates the number of vehicles after receiving the customer data input; and Ant Colony System-Delivery Time (ACS-DT) which calculates the vehicle schedule after receiving the number of vehicles resulting from ACS-DV and customer data input.

MACS-HFVRPTW

customers data

ACS-DV

vehicle numbers

customers data vehicle numbers

ACS-DT

Delivery schedule

Figure 1. MACS-HFVRPTW process 3

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4.1 Heuristic clustering For HFVRPTW, the cluster first-route second method (Bowerman et al., 1994) was used to quickly calculate the delivery schedule. Although the performance of the SWAP algorithm, one of the methods for preventing the ACS from falling into a local optimum, is high, it not only consumes the bulk of the ACS algorithm execution time, but also has the shortcoming of greatly increasing the execution time for finding the optimum solution when the quantity of data for SWAP execution increases. To reduce the SWAP execution time, treating the same type of customers as a single customer by using the heuristic clustering technique decreased the number of customers. The heuristic clustering procedure is as shown in Figure 2. Clustering is executed quickly enough to the extent that it has no impact on the overall algorithm execution time. Parameters K: clusters aKc: earliest service time at cluster Kc bKc: latest service time at cluster Kc tKc: working time at cluster Kc dKc: demand at cluster Kc lotKc: longtitude at cluster Kc latKc: latitude at cluster Kc Clustering start

d

Open List N , sort by increasing values of the ai Open List Vd, sort by increasing values of qd

Open an empty list Kn Assign the top entry of list Vd to cluster Kn

Pick up the top node i on the list Nd, place at Kn Initialize the parameters of cluster Kn aKnßai, bKnßbi, dKnßdi, tKnßt0i + mi Delete node i from list Nd make copy of Nd, N'd

Pick up j from N'd

dKn+dj ≤ qd/3

yes

no Delete j from N'd

tKn + tij + mj < Tmax no

yes

no

aKn + tKn + tij + mj ≤ bj

yes

no

dKnßdKn + dj tKnßtKn + tij + mj if(bKn < bj) : bKn ßbj lotKn ßavg(lot1, …, lotj) latKn ßavg(lat1, …, latj)

Delete j from N'd Delete j from Nd

N'd is empty?

yes d

N is empty?

yes End

Figure 2. Clustering procedure Each cluster is generated according to the delivery constraints, including delivery capacity, possible travel time between the current to the next customer and vehicle driver’s daily work hours. Upon completion of the heuristic4

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clustering, several customers are combined in a single cluster, and the single cluster is recognized as a single customer and applied to the ACS. This method significantly reduces the input size for the ACS, and greatly enhances the execution speed of the algorithm. 4.2 Ant colony system By using clustered customer data, the ACS proceeds as shown in Figure 3. This procedure is linked to Figure 4.

Initialize the parameters - Schedule : SCD - ACS repeat num : ACS_num = 200 - SWAP repeat num : SWAP_num = 90 - pheromone values Calculate distance between customers d Open List N , sort by increasing values of the ai

ACS Start

an ant move one step - Assign the top entry of list Nd to an ant

Pick up j from Nd j = j +1

no

Check the constraint - vehicle capacity - maximum working time

yes

ACS_num = 1

no

random value(q) > qm

yes yes

no

Select customer by Insertion score table

Select customer by Roulette wheel

Update local pheromone

Nd size = j

yes Put selected customers into SCD

SWAP Start (fig. 4)

Figure 3. ACS procedure

5

no

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4.2.1 Initial solution configuration The initial schedule is assigned in sequence to each vehicle according to the initialization rule: fast delivery time, shortest distance from the delivery center and short waiting time. Artificial ants travel from the delivery center to the first customer according to the initialization rule. When they begin to travel to the second customer, they travel to the customer with the highest insert score that combines pheromone information (τ) and heuristic information (η) (Equation 9). The notations α and β indicate the relative importance of pheromone information and heuristic information, respectively. (

ij )

( ij )

(9)

Previous ACS studies generally use the reciprocal of the distance between the current customer and the next customer to be inserted as heuristic information (Mullen et al., 2009). In this paper, however, in order to minimize driver waiting time, the criterion of the customer with short waiting time was added to the equation for the customer with the shortest service distance, as shown in Equation 10. The heuristic score, as formulated in Equation 10, ends up high for the customer nearest to the current customer and with the shortest waiting time.

ij

1 = ( v) t ij ) = (1

ij

ij

= (1

ij

ij

)

(

(t vi

aj

1 mvi

st vi

t vij )

)

(10) (11)

, (i, j)

(12)

The pheromone information accumulated on the travel route of the artificial ants is shown in Equation 11. Pheromone information is accumulated on the route chosen by the artificial ants and the accumulated pheromones evaporate after a fixed time. The pheromone retention rate ρ is a value between 0 and 1. = 1/( ) is the initial pheromone value where C is the number of customers and is the route distance (initial solution) at the initial stage. When the shortest route from the routing composition is completed, the global pheromone update takes place as shown in Equation 12. is the solution with the shortest route. By accumulating pheromones on the shortest route, we increase the probability the route being chosen by the artificial ants. Through an update of the entire area, the pheromones on a route not chosen evaporate and lower the route’s selection probability. Similarly, we induce convergence of a solution by accumulating pheromones on the optimal route. 4.2.2 Local search When the initial solution is complete, route searching begins with the initial solution as a basis. Since ACS uses information from the entire area at the initial search stage, it provides superior search ability and makes searching near global optimal solution possible. On the other hand, due to the ACS’s characteristic of searching for an optimal solution based on the initial solution, there is a substantial risk of falling into a local optimum. In order to prevent this, the Roulette Wheel and SWAP methods were employed. When an artificial ant moves forward, it usually selects a customer with the highest insert score (Equation 9). With the roulette wheel method, an ant does not advance solely based on the insert score, but generates a random value (q) between 0 and 1. If this value is smaller than the predetermined value (qm), it selects the customer with the highest insert score; if it is equal to or greater than qm, it selects a customer according to Equation 13. [ ij ] ij

∑

= {

[

[ ij ]

ij ]

[

ij ]

(13)

0

By repeatedly applying the SWAP method to the schedule thus generated, progressive improvements to the solution are made. The SWAP method is a technique for improving the solution by randomly selecting two vehicles from the generated schedule and exchanging the customers within them. The detailed procedure is shown in Figure 4.

6

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SWAP Start

Select two vehilces randomly in SCD -V1, V2 Initialize the swap cadidate table

Pick up Ci+2 from V1 Exchanges customers Ci+2 with Ćj in V2 no

Check the constraint - vehicle capacity - maximum working time

no

yes Put Ci+2, Ćj into the swap cadidate table

no

i = V1 size j = V2 size

yes Select max saving cost customer form swap candidate table

SCD = min(SCD)

SWAP_num > 90

yes Update global pheromone

ACS_num > 200

no

ACS Start (fig. 3)

yes END

Figure 4. SWAP procedure

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From the schedule generated by ACS, two vehicles (V1, V2) are randomly selected. V1 = < C1, C2, C3, C4, C5, C6, …, Ci > V2 = < Ć1, Ć2, Ć3, Ć4, Ć5, Ć6, …, Ćj > From the customers assigned to the selected vehicles, customer Ci+2 for vehicle V1 and customer Ćj for vehicle V2 are exchanged and stored in Table 1 if the constraints of vehicles V1 and V2 (capacity, travel time constraint, daily operation hours of the vehicle) are satisfied. Table 1. SWAP candidate table V1 customers C2 C2 C2 C4 C4 … Ci

V2 customers Ć1 Ć3 Ć4 Ć3 Ć5 … Ćj

Saving cost 100 250 200 300 100 …

Of the solutions generated during the SWAP execution, the one that minimizes the cost is chosen and is continually improved as described by the following example. For a list of candidate solutions stored in Table 1, the final customers to be exchanged are C2 ↔ Ć4 and C4 ↔ Ć3. Of the V2 customers that can be exchanged with the V1 customer C2, customer Ć3 gives the greatest saving of 250. However, a greater saving of 300 is possible when Ć3 is exchanged with C4 instead of C2. Ć3 is thus exchanged with C4, and C2 is exchanged with Ć4 to save the second largest cost amount. Through a series of such processes, the solution for HFVRPTW can be obtained within a satisfactory time period. 4.3 Algorithm analysis The Algorithms of this paper are designed to apply to the actual home appliance delivery company in Korea. The most important thing is to rapidly reschedule as soon as possible when an accident occurs during delivery. Therefore, we used the strategy to reduce the execution speed of the algorithm even though the optimization rate of the algorithm decreased. The algorithm has been tested on standard problems found in Solomon (1987). Solomon’s benchmark problem was widely used to evaluate the VRPTW algorithm. That includes geographical data, the number of customers serviced by a vehicle, percent of time-constrained customers and tightness and positioning of the time windows. The geographical data are randomly generated in problem sets R1, clustered in problem sets C1, and a mix of random and clustered structures in problem sets by RC1. Table 2 compares our algorithm to other available metaheuristic algorithms with respect to three parameters: average number of used vehicles, average distance traveled and average CPU time (Dondo and Cerdá, 2009). The reference metaheuristic techniques in Table 2 are: GTA (Gambardella et al., 1999), RT (Rochat and Taillard, 1995), TB (Taillard et al., 1997), PHGA (Berger and Barkaoui , 2004), B-VH (Bent and Van Hentenryck, 2004), CW (Cordone and Calvo, 2001), DC (Dondo and Cerdá, 2009), and CA is our algorithm. The result indicates that our algorithm significantly decreased CPU time but increased distance traveled by about 7-9% compared to the past study. Table 2. Performance Comparison of metaheuristic algorithm for VRPTW Problem Type R1 Vehicles Distance CPU Time C1 Vehicles Distance CPU Time RC1 Vehicles Distance CPU Time

GTA 12.38 1210.83 1800 10.00 828.38 1800 11.92 1388.13 1800

RT 12.58 1197.42 2700 10.00 828.45 3200 12.33 1269.48 2600

TB 12.33 1220.35 13774 10.00 825.45 14630 11.90 1381.31 11264

PHGA 12.17 1251.40 1800 10.00 828.50 1800 11.88 1414.86 1800

8

B-VH 12.08 1288.35 1800 10.00 828.95 1800 11.88 1456.49 1800

CW 12.50 1241.89 1382 10.00 834.05 649 12.12 1388.15 2900

DC 14.22 1218.21 701 10.00 826.35 353 14.00 1356.38 892

CA 13.75 1387.04 123 10.00 887.70 28 13.25 1512.17 143

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5. REAL-LIFE EXAMPLE The HFVRPTW algorithm engine proposed in this paper was written in JAVA and tested on a PC with Inter Core 2.2 GHz and 2G RAM. The application program that runs on drivers’ smartphones was built on the Android OS Version 2.2 platform. For test input data, a single day customer order data from Korean electronics company A for Songpa-gu district, Seoul was used. The total number of customer orders was 201. MS-SQL was used for the database, and the essential data tables were customer information, product information, vehicle information and delivery center information. The overall delivery model data structure is shown in Figure 5.

Product

Center PK

PK

center_id

size weights movingTime installTime serviceTime

address latitude longitude

Vehicle PK

Customer

vehicle_no

PK

FK1 center_id startTime endTime capacity

customer_id

FK1 product_id FK2 center_id deliveryDate earliestTime latestTime quantity address latitude longitude

Schedule PK

product_id

schedule_id

FK1 vehicle_no FK2 customer_id deliverySeq deliveryTime

Figure 5. Essential DB Schema

5.1 Expert system configuration The entire system configuration is as shown in Figure 6. When a customer places an order, either online or offline, the order details - delivery address, delivery time, product code, etc. - are stored in the DB. The clustering ACS engine generates the delivery schedule by using the order details stored in the DB. The delivery schedule is converted into an XML format and transmitted via a web server to the drivers’ Android-based smartphones. GPS information from each driver’s smartphone is also periodically transmitted to the server. Furthermore, the manager can monitor the entire delivery process.

manager

order

Web Server

Schedule GPS location

schedule data

order data

customer

DB

Clustering ACS Engine

Figure 6. System Configuration 9

Kim and Lee

5.2 Experiment result. The experiment is divided into the stage of generating the delivery schedule by using the clustering ACS engine and the stage of building the delivery expert system consisting of a manager system and a delivery driver system. 5.2.1 Clustering ACS engine The clustering ACS engine generates the delivery schedule following the flow shown in Figure 7. First, customers are clustered according to the fixed criteria. Second, the delivery schedule is generated based on the clustered customers through MACS-HFVRPTW. Finally, the delivery sequence of the customers included in each cluster is determined.

Figure 7. Clustering ACS Engine procedure The relative importance values, α and β, of the pheromone and heuristic information of MACS-HFVRPTW, were set equally at 1, and the pheromone retention rate (ρ) was set to 0.9. The maximum value selection probability (qm) was empirically set to 0.2 because a smaller probability value led to lower cost value. The ACS iteration count was set at 200 and the SWAP algorithm iteration count was set at 90 based on the results of the execution, as shown in Figure 8.

Figure 8. SWAP Iteration Dependent Cost Graph Using order information for 201 customers, the test computer produced an outcome in about 4 minutes. The total number of vehicles was 20, each vehicle made deliveries to 10 customers on average. 5.2.2 Expert system The expert system manages and executes the actual delivery based on the delivery schedule generated by the clustering ACS engine is shown inFigure 9. The manager can monitor in real time the locations of drivers currently in the field and the delivery status information. Drivers can verify from a smartphone the delivery order and the delivery location. The Android screen for the driver is shown in Figure 10. Since it is a VRP model with time windows, driver routes are intricately set. With this expert system in place, managers are able to efficiently monitor the current delivery status, figure out the locations of drivers and manage idle drivers. The ability to quickly respond to unexpected situations that may arise during delivery reduces delivery-related problems such aslate deliveries. Delivery service quality is consequently improved and this in turn leads to higher customer satisfaction. The Android application for the drivers can be used with smartphones and the GPS built into the smartphones currently used by the drivers, and thus there is an advantage in that the system can be set up without purchasing additional equipment. 10

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Figure 9. Manager Screen

Figure 10. Android-Based Driver Screen

6. CONCLUSION In this paper, we dealt with HFVRPTW in the field of Korean home appliance delivery. Unlike traditional physical distribution, home appliance delivery not only requires delivery within the promised time period, but also product installation and additional services. In order to generate an optimal delivery schedule with respect to HFVRPTW, a MACS algorithm based on heuristic clustering was used. A system for managers and an android-based system for drivers were also set up to improve the service quality. In existing studies dealing with delivery algorithms, algorithms were mostly executed on sample data only, algorithm execution speed was unreliable when the customer data size is increased, or algorithms were difficult to apply to actual delivery situations because they were unable to flexibly handle unexpected situations such as delivery delays, accidents, or changes in various delivery constraints. In light of such shortcomings, we generated a delivery schedule by using MACS among the various metaheuristic algorithms because of its flexibility in handling many constraints. The heuristic clustering method was used to reduce the number of customers, which in turn shortened the SWAP time that consumes the greatest amount of the MACS algorithm execution time. With this approach, a satisfactory execution time could be obtained. Together with generating a delivery schedule by using the heuristic clustering-based MACS algorithm, a system for managers and a system for drivers were constructed. Managers are thus able to monitor the current delivery status in real time and quickly respond to situations such as delivery delays and changes in delivery sequence. Drivers, meanwhile, are able to verify an optimized delivery schedule in real time using their smartphones. With such an overall system setup, the quality of service experienced by customers could be improved. The significance of this paper lies in constructing an expert 11

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system covering the entire delivery process, from generation of a schedule to management system setup.

7. ACKNOWLEDGEMENTS This work was supported by the BK21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University).

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