An RFID-based Shopping Service System for retailers

Advanced Engineering Informatics 25 (2011) 103–115

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An RFID-based Shopping Service System for retailers Jiang-Liang Hou *, Ting-Gin Chen 1 Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu (300), Taiwan

a r t i c l e

i n f o

Article history: Received 7 December 2009 Received in revised form 19 April 2010 Accepted 23 April 2010 Available online 21 May 2010 Keywords: Shopping service Route planning Route guidance Personal recommendation RFID

a b s t r a c t Modern markets have put intensive effort on commodity arrangement in order to satisfy the consumer demands on commodity purchase. However, most markets do not provide satisfactory shopping services to customers. For instance, without a customized shopping recommendation for each individual customer, consumers have to spend a lot of time for commodity selection. Furthermore, most large-spaced markets merely utilize signs in front the aisles of specific commodity areas to direct consumers, which cannot provide an accurate guidance for commodity search. Therefore, regarding the shopping services of a modern market, this research develops a customized commodity recommendation algorithm and a shopping route determination and guiding algorithm. Based on the proposed algorithms, a Shopping Service System (3S-System) is established by integrating the RFID technology. Considering the consumer demands, consumer shopping preferences and market promotion plans, this research proposes an integrated, heuristic methodology to provide a customized purchase list, route recommendation and realtime direction guiding for consumer shopping. Moreover, based on the proposed methodology, a Shopping Service System (3S-System) is established, and a simulated market is created in order to verify the feasibility of the proposed model. The verification results show that the system can offer customers appropriate shopping route recommendation in a short time and could achieve real-time guiding. As a whole, this research provides a methodology and system to provide effective shopping services for consumers and as a result the shopping service quality of modern markets can be enhanced and the sales volume of commodities can be increased. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction In the customer-oriented era, the customers have higher demand for retailer service quality. The new-style retailers (including wholesalers) should provide not only a wide selection of usable and value-added merchandises for customers, but also a diversified, user-friendly shopping environment with wide exhibition space. Traditionally, the retailers provide paper-based promotional catalogs to support customers to browse and select merchandises. However, such promotional approach is not designed on the basis of individual preferences. Thus, the promotional information cannot completely meet the shopping demands of distinct customers. As a result, it is difficult for the retailers to increase their sales volumes owing to the in-effective shopping assistance. In the broad space of the retailer, categorization signs are affixed on the shelves to assist the customers to find the merchandises they intend to purchase. As the scale of the retailers is increased further (e.g., the shopping space is expanded to several

* Corresponding author. Tel.: +886 3 5742658; fax: +886 3 5722685. E-mail addresses: [email protected] (J.-L. Hou), [email protected] (T.-G. Chen). 1 Tel.:+886 3 5715131x33981; fax: +886 3 5722685. 1474-0346/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aei.2010.04.003

floors), it might be difficult for customers to find the locations of the merchandises via the signs. Customers have to identify the floors that their target merchandises are stored via the map and then find the detailed shelf locations through the signs. The existing shopping model is inconvenient and inefficient. As customers want to buy merchandises of various types and the shopping route is not provided, they have to plan the route according to their experience. The method for shopping route planning is time-consuming and optimality of the result cannot be guaranteed. The as-is model of shopping behaviors mentioned above is shown in Fig. 1. The issues regarding the absence of a Shopping Service System in a retailer can be summarized into the following three points: (1) Shopping list determination: the traditional shopping assistance service lacks customized merchandise recommendation. (2) Shopping route planning: customers have to spend time on planning the shopping route based on their experience and optimality of the planning result cannot be guaranteed. (3) Shopping route guidance: the real-time guidance service for the merchandise locations is not provided in a traditional retailer.

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Fig. 1. The as-is model of shopping behaviors.

The above issues are the research motivations of this study. Thus, an effective shopping service model and technology is developed to assist the customers efficiently identify the merchandises they intend to purchase in a retailer and to increase the sales volumes of the retailer. In a new-style retailer (e.g., the supermarkets or wholesalers), the previous paper-based promotion catalogs can be replaced by the customized shopping recommendations for the target customers. The customized recommendations can be generated based on historical shopping lists of the target customer. The historical shopping records and current shopping lists can be combined with sales promotion. The promotion approach based on the shopping history and demands of the individual customers can significantly increase the customer satisfaction and sales volume. Considering the scheduled shopping time and browsing speed of the customers, merchandises with higher recommendation can be selected from customized recommendation list so as to plan the shopping route where the customers can browse the recommended merchandises within their scheduled time. The route can be provided to the customers to improve their browsing and shopping efficiency. Moreover, the RFID technology is also applied to the shopping environment to realize real-time positioning of customer location. The real-time guidance information of the shopping route can be determined and provided o the customers through route planning and customer positioning results via the RFID technology. As a result, the customers can efficiently com-

plete merchandise shopping with the real-time shopping directions. The to-be shopping model proposed in this research can be shown in Fig. 2. The role of this study can be demonstrated via a research map (Fig. 3). The highlighted areas of Fig. 3 denote the main focuses of this study. This study develops effective algorithms for shopping list determination, shopping route planning and shopping route guidance (RFID). In addition to the effectiveness of the proposed algorithms, different from the previous literature, this research comprehensively integrates these related topics and algorithms to enhance the service quality of the shopping services in realworld retailers. In the following, the related research subjects are introduced in Section 2. Section 3 presents the integrated shopping route determination and guidance model developed in this research. Details of the shopping route determination and guidance algorithms are provided in Section 4. The Shopping Service System established based on the proposed algorithms is introduced in Section 5. The performance of the proposed algorithms and system is evaluated in Section 6. 2. Literature review The research subjects included applications of the RFID technology, route planning, and guidance and tracking via the RFID technology. The literature related to these subjects is reviewed as follows.

Fig. 2. The to-be model of shopping behaviors.

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Shopping Services for Retailers RFID Applications

Route Planning

route planning methods route planning applications

105

optimal route planning approximate route planning

Guidance and Tracking via RFID visitor guiding warehouse management security management production management object positioning motion recognition

Guidance

object guiding transportatin guiding visitor tracking

Tracking

delivery tracking path tracking

Fig. 3. The research map of this study.

2.1. Applications of RFID technology Development of the RFID technology has matured gradually and the scope of its applications has been extended. For example, Chow et al. [5] employed the RFID technology to improve the warehouse management efficiency. McCoy et al. [10] used the RFID technology to establish the airport passenger tracking and tracking system to improve the efficiency of the security management. Gandino et al. [6] applied the RFID technology to a distributing center of agricultural merchandises for the production history tracking and quality control. In addition to the applications for inventory control and passenger management, the RFID technology has been widely applied to the object positioning. Goodrum et al. [7] attached the active RFID tags to small tools in a plant where the RFID reader was installed to identify the locations of various small tools in real-time. Patil et al. [12] established a positioning system for buildings with usage of technologies such as RFID and Wireless Fidelity (Wi-Fi). 2.2. Route planning The route planning can be classified into the two types, including the optimal route planning and approximate optimal route planning. In the optimal route planning, the exhaustion method and the dynamic programming are often used for a small number of candidate routes to achieve the optimal route solution. Chang [2] used dynamic programming to derive the shortest path for the plane passes through multiple locations. Determination of the optimal solution via dynamic programming is time-consuming. In addition to the full length of the route, some other domain-specific constraints should also be concerned for routes selection. Lee and Kardaras [9] planned the optimal route where the distance from the starting point to the destination is the shortest and the number of obstacles within the route is also minimized. In the approximate optimal route planning, the approximate optimal solution is often determined via search algorithms. For instance, the genetic algorithm (GA), ant colony system (ACS) and Tabu search algorithm have been widely used to derive the distribution route of merchandises for logistics suppliers [3,13,4]. 2.3. Guidance and tracking via the RFID technology Previous studies have discussed the improvement of visit service quality of the exhibition hall or efficiency of merchandise delivery tracking for suppliers. Ishizuka et al. [8] established the guiding system of a research center in the University of Tokyo

via the RFID technology. The system classified the visitors according to their scientific education levels in order to provide different explanations for the visitors. Baum et al. [1] used the RFID technology to establish the positioning system of an automated guided vehicle (AGV). Besides the guidance application, the RFID technology can be applied to track people and objects. Mori et al. [11] developed a tracking system for indoor people localization with floor pressure sensors and the RFID technology. Other applications can be seen in the hospital adopted the RFID Tag function of the thermometer to control the SARS spread in the hospital Wang et al. [14]. By comparing with the previous research, this study develops effective algorithms for shopping list determination, shopping route planning and shopping route guidance (RFID). In brief, this study develops algorithms for shopping list determination (via data mining and weighted indices), shopping route planning (via dynamic programming) and shopping route guidance (via RFID technology and coordinate determination). Different from the previous works, this research comprehensively integrates these related topics and algorithms to enhance the service quality of the shopping services in retailers. 3. Methodology for shopping route determination and guidance In this research, based on the shopping demands and preferences of the target customer and with the sales promotion, a methodology for shopping route planning and guidance is developed to provide the shopping route suggestions for the target customer. In addition, the RFID technology is employed to provide the real-time route guidance according to the location of the target customer. The methodology focuses on providing recommendation lists for shopping and real-time shopping route guidance based on the shopping preference of each individual customer. As a result, the shopping efficiency and sales can be increased via the customized shopping services. In the proposed methodology, several assumptions are made as follows:  The efficiency for shopping is essential for the target customers (e.g., the analytical customers).  The entrance and the exit of a retailer are the starting and ending points for shopping route planning.  The shopping history of each customer has been recorded.  The walking speed of a customer is stable and thus a specified value can be given to represent the customer walking speed for the entire shopping process.

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 The time for distinct customers to browse the identical merchandise is equal and can be assigned a definite value.  The RFID readability is not concerned in the proposed methodology.

methodology is shown in Fig. 4. The two subjects (i.e., shopping route determination and guidance) of the proposed methodology are described as follows.

Based on the above assumptions, a two-phase methodology with shopping route determination and shopping route guidance is developed. The algorithm for shopping route determination can be further classified into two types, i.e., the shopping route determination with a definite shopping list and shopping route determination without a definite shopping list. As a definite shopping list can be obtained, the shopping route can be determined according to the merchandises identified by the target customer. The shopping route determination model can assist the target customer to browse the merchandises to be purchased via the shortest route. On the other hand, if the definite shopping list cannot be acquired, the recommended merchandises to be browsed can be determined based on the shopping history of the customer and the sales promotion strategies of the retailer. According to the shopping criteria specified by the target customer, the merchandises with higher recommendation are identified and the shortest shopping route for browsing the recommended merchandises can be derived in order to assist the target customer to efficiently browse their preferred merchandises within the scheduled shopping time. The conceptual model of the proposed

4. Algorithms for shopping route determination and guidance

Input

4.1. Shopping route determination with a definite shopping list Most customers have their own shopping lists as they go shopping in a retailer. In this algorithm, the optimal shopping route can be determined for customers based on their shopping lists. It is assumed that the input (i.e., the pre-defined shopping list) of the shopping route determination and guidance can be obtained as the customer assigns the items via the PDA with a RFID reader when shopping. Firstly, after acquiring the shopping list assigned by the customer, locations of the identified merchandises can be obtained. Furthermore, the relative distance of the merchandise locations is checked. In order to save shopping time for the target

Optimal shopping route

Analysis

Shopping list With definite shopping list

Shopping route suggestions for customers can be made based on their shopping preferences. The corresponding algorithm can be divided into the algorithm with a definite shopping list and the algorithm without a definite shopping list. Details of the shopping route determination model are described as follows.

ID

Item

1

A

2

B

3

C

Shopping direction

Items Locations Distance Seq.

Shopping Condition Type

Without definite shopping list

1 2 3

Pref. 6 4 7

Time

80 mins

Speed

60m/min

Preferences

Item

1

D

2

A

3

C

Promotions

Recommendation

Shopping Route Determination

Shopping Route Guidance

Algorithms

Shopping Route Planning Module A-1 parameters

A-2 -shopping recommendation -shopping route -shopping direction

Merchandise Data Maintenance Module Shelf Location Data Maintenance Module

D-1 data maintenance D-2 status feedback E-1 data maintenance E-2 status feedback

Shopping Service DB

-item data -shopping records -shelf data -user data

User

Administrator

Shopping Service System User Data Maintenance Module

B. personal data

Fig. 4. The shelf layout in a retailer.

C. user data

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customer and to reduce the total length of the shopping route, the dynamic programming method is used to determine the shortest shopping route, and the customer is advised to take the shopping route. The symbols used in this algorithm are defined as follows: AWL(SLi) D

D(SLi, SLj) fn ðSLi Þ

Lx(SLi) Ly(SLi) LSLn

mfn(SLi, SLj)

NWL(SLi) SeqðSLi ; nÞ Seqi

SLi

Set of the turning points of all the aisles adjacent to SLi The total length of the route where the target customer browses all the listed merchandises in the optimal shopping route The relative distance between locations SLi and SLj The shortest distance for browsing rest of the merchandises if the n’th merchandise to be browsed is SLi The X coordinate of location SLi The Y coordinate of location SLi Set of the merchandises to be browsed in the n’th browsing sequence (including the n’th merchandise) The distance for browsing rest of the merchandises if the n’th and (n + 1)’th merchandises to be browsed are SLi and SLj, respectively The number of the turning points of the aisles adjacent to SLi Merchandise to be browsed in the (n + 1)’th sequence when SLi is in the n’th sequence The i’th merchandise to be browsed by the target customer in the optimal shopping route The i’th merchandise in the shopping list of the target customer

The designed aisle arrangement is shown in Fig. 4. It is assumed that each merchandise is placed on the single shelf (as shown in Fig. 4(A)). The shelf towards the same aisle and at the same side of the aisle can be defined as the single shelf (as shown in Fig. 5(B)). The shelves at the aisles are arranged in parallel, and the customers cannot pass through the physical shelves. In addition, each merchandise in the shelf has their corresponding aisle turning points. The turning points corresponding to each merchandise are the intersection points of the aisles adjacent to the merchandise (as shown in Fig. 5(C)). Merchandises with the shelves towards the same aisle are defined that the merchandises are placed in the same shelf area (as shown in Fig. 5(D)). That is, customers can browse merchandises in the same shelf area without passing through any aisle turning points.

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For shopping route determination with a definite shopping list, the shopping list should be acquired first. According to the list, the coordinates of the specified merchandises can be obtained and the relative distance of the merchandises can be derived. The dynamic programming is used to determine the browse sequence with the shortest route. The suggested sequence can be provided for the target customer to improve the shopping efficiency. The details of the proposed algorithm are described as follows. 4.1.1. Step (A1) – acquisition of the shopping list from the target customer The shopping list of the target customer is required before deriving the optimal shopping route. The shopping list includes the item number i and the corresponding item SLi. 4.1.2. Step (A2) – acquisition of location coordinates of the specified items The geographic coordinates of the specified items can be acquired according to the shopping list and WMS (warehouse management system) of the retailer. Taking SLi as an example, the X coordinate Lx(SLi) and the Y coordinate Ly(SLi) corresponding to SLi can be acquired in this step. 4.1.3. Step (A3) – calculation of the relative distance of the specified items In this method, the relative distance between merchandises can be derived based on the shelf locations to determine the shortest route for browsing the merchandises in the target list. The shelves are placed in parallel in the retailer and it should be considered that whether a shelf obstructs the direct channel between two merchandises during calculation of the distance between different shelves. The aisle turning points of the different merchandises can be used to determine whether the different merchandises are in the same shelf area. If they are in the same shelf area, it means that there exists a direct connection between the two merchandises and the distance between these two merchandises can be calculated by linear distance. Otherwise, if the two merchandises are not in the same shelf area, it means that there exist a shelf obstructing the customer and the distance should be calculated by segments. The methods for calculating the distance between merchandises are as follows: 4.1.3.1. Merchandises in the same shelf area. If SLi and SLj are in the same shelf area (with identical aisle turning points), the target customers can reach SLi and SLj without passing through any turning points. Thus, the distance D(SLi, SLj) between SLi and SLj is the linear distance of geographical coordinates of the two merchandises.

Fig. 5. The proposed framework for shopping route determination and guidance.

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4.1.3.2. Merchandises in different shelf areas. If SLi and SLj are placed in different shelf areas (with distinct aisle turning points), the actual walking distance for the target customer reach SLi and SLj should be calculated by segments. The distance for the customer walk from SLi to SLj can be divided into three segments. The first segment is from SLi to the turning point of the adjacent aisle WL(SLi, h) (the h’th adjacent turning point of SLi); the second segment is from the turning point WL(SLi, h) of SLi to the turning point WL(SLj, k) of SLj; the third segment is from the aisle turning point WL(SLj, k) to SLj. The three segments can be used to derive the relative distance between SLi and SLj. Since there are several turning points adjacent to each merchandise, the relative distance between SLi and SLj will be changed if different turning points are selected. Thus, the shortest distance D(SLi, SLj) and the corresponding turning points (i.e., Out (SLi, SLj) and In(SLi, SLj)) between SLi and SLj can be identified to denote the optimal route from SLi to SLj. In summary, the equation for deriving the distance D(SLi, SLj) between SLi and SLj is as follows: IF THEN

AWLðSLi Þ ¼ AWLðSLj Þ DðSLi ; SLj Þ ¼ jLx ðSLi Þ  Lx ðSLj Þj þ jLy ðSLi Þ  Ly ðSLj Þj

OTHERWISE

DðSLi ; SLj Þ ¼ minjLx ðSLi Þ  Lx ðWLa ðSLi ÞÞj þ jLy ðSLi Þ  Ly ðWLa ðSLi ÞÞj þ jLx ðWLa ðSLi ÞÞ  Lx ðWLb ðSLj ÞÞj þ jLy ðWLa ðSLi ÞÞ  Ly ðWLb ðSLj ÞÞj þ jLx ðWLb ðSLj ÞÞ  Lx ðSLj Þj þ jLy ðWLb ðSLj ÞÞ  Ly ðSLj Þj

for a ¼ 1; 2; 3 . . . ; NWLðSLi Þ; b ¼ 1; 2; 3; . . . ; NWLðSLj Þ

ð1Þ

4.1.4. Step (A4) – determination of the optimal shopping route for the target customer Based on the specified shopping list, shelf locations and the derived shortest distance between the merchandises, the dynamic programming is used to determine the optimal shopping route for the target customer to browse the specified merchandises in the shortest route. In the dynamic programming, the merchandise to be browsed in the last shopping sequence is regarded as the starting point is used to derive the route length needed for browsing of the rest of merchandises. After that, the merchandise in the preceding sequence is regarded as the starting point used to calculate the shortest route length required for browsing of the rest of merchandises. The concept can be represented as the following equation:  fn ðSLi Þ ¼ minfDðSLi ; SLj Þ þ fnþ1 ðSLj Þg for SLj 2 LSLn  SLi

ð2Þ

The concept can be used to determine the route mf n ðSLi ; SLj Þ for the target customer to browse SLi and SLj in the n’th and (n + 1)’th sequence and browse the rest of merchandises. The different merchandises can be selected from the rest of merchandises as the one to be browsed in the next sequence. The minimum value should be taken for the total route length mf n ðSLi ; SLj Þ from SLi to the rest of merchandise SLj, and the value is defined as the shortest route fn ðSLi Þ. That is, fn ðSLi Þ denotes the shortest route to browse the rest of merchandises with SLi in the n’th sequence as the starting point. The concept can be expressed via Eq. (4). In addition, SLj corresponding to the shortest route can be defined as the merchandise SeqðSLi ; nÞ to be browsed in the next sequence after SLi (the n’th sequence), as shown in Eq. (5).  mf n ðSLi ; SLj Þ ¼ DðSLi ; SLj Þ þ fnþ1 ðSLj ÞandSLj 2 LSLn  SLi

ð3Þ

fn ðSLi Þ ¼ minfmfn ðSLi ; SLj ÞgforSLj 2 LSLn  Sli

ð4Þ

SeqðSLi ; nÞ ¼ SLA where A ¼ fajmfn ðSLi ; SLa Þ ¼ minfmfn ðSLi ; SLj Þgg j

At last, the minimum value is taken for the route length mf 0 ðSEntry; SLi Þ formed by all the merchandises SLi. The value can be regarded as the shortest route length D where the target customer can browse all the merchandises specified in the shopping list. The merchandise SLi corresponding to the shortest route mf 0 ðSEntry; SLi Þ ¼ D can be regarded as the merchandise Seq1 to be browsed in the first sequence. The concept can be revealed in Eqs. (6) and (7).

D ¼ minfmf 0 ðSEntry; SLi Þg A i   ¼ faf0 ðSLa Þ ¼ minfmf 0 ðSEntry; SLi Þgg Seq1 ¼ SLA

ð7Þ Seq1

In addition, after acquiring the first merchandise to be browsed, the merchandises corresponding to the other sequence Seqn that the target customer can browse the specified merchandises via the shortest route (Eq. (8)). As a whole, the optimal shopping route can be obtained by connecting the sequenced merchandises with their corresponding aisle turning points.

Seqn ¼ SeqðSeqn1 ; n  1Þ

ð8Þ

4.2. Shopping route determination without a definite shopping list Some customers go to the retailers without a definite shopping list and they might just want to spend time in viewing new merchandises. The shopping list is replaced with customer preferences and retailer promotion strategies to determine the optimal shopping route in this model. The symbols for shopping route determination without a definite shopping list are defined as follows:

a AT(n) b CSKi

Cus(SKi) DisSKi Lx(SKi) Ly(SKi) n OriSKi Pro(SKi) Sel(SKi) SKi T

T(SKi) WT(SKi, SKj)

ð5Þ

ð6Þ

i

The weight of the customer preference for the merchandises The total time for the target customer to browse the top n types of merchandises The weight of the retailer promotion strategy The number of the merchandises of the i’th type purchased by the target customer in the historical records The preference degree of the target customer for the SKi merchandises The total of the discounted price for the SKi merchandises The X coordinate of the shelf location for the SKi merchandises The Y coordinate of the shelf location for the SKi merchandises The number of the merchandise types recommended in the optimal shopping route The total of the original price for the SKi merchandises The browse recommendation level for the SKi merchandises The promotion level for the SKi merchandises The i’th merchandise type in the retailer The total time for the target customer to browse the merchandises in the optimal shopping route The average time for customers to browse the SKi merchandise Time for the target customer to walk between the shelves of the SKi and SKj merchandises

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The data related to shopping preferences of the target customer and the promotion level of the retailer is required in the shopping route determination without a definite shopping list. The recommendation level for merchandise browsing can be derived according to the shopping preferences and promotion level. The shortest shopping route with different types of merchandises can be determined based on the recommendation level of each merchandise and the expected shopping time specified by the target customer. Comparison between the reasoned and specified shopping time is carry out. The reasoned time close to and not greater than the specified time will be selected and the corresponding route is regarded as the optimal shopping route for the target customer. The detailed steps of this model are described as follows. 4.2.1. Step (B1) – acquisition of the preference degree corresponding to each merchandise At this step, the previous shopping behavior of the target customer is used to reveal the shopping preference of the target customer. Namely, the shopping preference degree (Cus(SKi)) corresponding to the SKi merchandises for the target customers can be represented by the ratio between the total number of the purchased SKi merchandises (CSKi) and total number of all purP chased merchandises ( NSK j¼1 CSKj ) by the target customer in the historical records. The corresponding equation can be represented as Eq. (9).

CusðSKi Þ ¼ CSKi

, NSK X

CSKj for i ¼ 1; 2; 3; . . . NSK

ð9Þ

j¼1

4.2.2. Step (B2) – acquisition of the promotion level corresponding to each merchandise In this algorithm, the discount rate of each merchandise in the retailer is defined as its promotion level. Namely, the promotion level Sel(SKi) of the SKi merchandise can be defined as the ratio between the total value of the original prices of all SKi merchandises (OriSKi) and, the total value of the discounted prices of all SKi merchandises (DisSKi). The sales promotion level can be derived via Eq. (10):

SelðSKi Þ ¼ OriSKi =DisSKi

ð10Þ

4.2.3. Step (B3) – calculation of the browse recommendation degree corresponding to each merchandise It is assumed that a, the weight of the browse recommendation level for each type of merchandise, can be calculated based on customer preference level, and that b, the weight of the browse recommendation level for each type of merchandise, can be calculated based on the sales promotion. Thus, the browse recommendation level for SKi can be defined as the weighted multiplication from two weights of the customer merchandise preference level Cus(SKi) and the promotion level Sel(SKi). The browse recommendation degree can be derived via Eq. (11).

ProðSKi Þ ¼ CusðSKi Þ  a þ SelðSKi Þ  b

ð11Þ

4.2.4. Step (B4) – acquisition of coordinates of shelves corresponding to merchandises In this step, the mean coordinates of the shelf of can be calculated after acquiring the shelf locations of all types of merchandises. The shelf location closest to the mean coordinates can be defined as the representative shelf location of that merchandise type. Taking SKi as an example, after acquiring the coordinates of all SKi merchandises, the average X and Y coordinates can be calculated. In addition, the shelf closest to the average coordinates is considered as the representative shelf location of the SKi merchan-

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dises. The coordinates of the shelf is indicated as the representative shelf coordinates for SKi, i.e., Lx(SKi) and Ly(SKi). 4.2.5. Step (B5) – calculation of relative distance between different types of merchandises This calculation method is identical to the method for deriving the optimal shopping route with a definite shopping list (see Step (A3)). 4.2.6. Step (B6) – determination of browse sequence of all recommended types of merchandises in the shortest shopping route This step is the same as that indicated in the optimal shopping route determination model with a definite shopping list (as shown in Step (A4)). However, this model is restricted by the expected shopping time specified by the target customer. Therefore, only the merchandise types with higher browse recommendation levels are suggested to the target customer. The derived browse sequence corresponding to each recommended merchandise type in the shortest route can be used as the basis for the subsequent route selection. 4.2.7. Step (B7) – determination of the optimal number of merchandise types recommended to be browsed by target customers In the previous step, the browse sequence of the each recommended merchandise type in the shortest route has been determined. In this step, the optimal number of merchandise types recommended to be browsed by the target customer can be determined within the expected shopping time specified by the customer. The time required for the customer to browse the merchandises in a retailer can be divided into two components including the browse time (T(SKi)) and movement time (WTðSKi ; SKj Þ: 4.2.8.). In the shortest shopping route where the n merchandise types with higher browse recommendation levels are browsed, the total time AT(n) for the customer to browse the n types of merchandises can be calculated via Eq. (12)

ATðnÞ ¼

n X

½TðSeqn ðiÞÞ þ DðSeqn ðiÞ; Seqn ði þ 1ÞÞ=m

ð12Þ

i¼0

In order to provide an applicable browse suggestion for the target customer to browse the merchandises in the recommended shopping route within the expected shopping time, the total time for browsing all the recommended types of merchandises and the specified shopping time should be compared. Only the total time closest to and not greater than the specified shopping time can be applied and defined as T  , i.e., the optimal browse time for target customer. The number of merchandise types with total browse time closest to and not greater than the expected time is defined as n , i.e., the optimal number of merchandise types recommended to the target customer. The equations are shown as Eqs. (13) and (14).

n ¼ fijATðiÞ 6 ST T  ¼ ATðn Þ

and ATði þ 1Þ > STg

ð13Þ ð14Þ

4.2.8. Step (B8) – determination of the optimal shopping route for target customer The optimal browse sequence for the shortest shopping route and the optimal number of merchandise types recommended to be browsed by the target customer has been determined in the previous steps. In this step, the merchandises recommended to be browsed are connected with their aisle turning points according to the optimal shopping sequence and this is also regarded as the optimal shopping route.

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4.3. Shopping route guidance

WLCðX; Y; iÞ

The shopping route recommended to the target customer can be determined based on the specified shopping lists or the historical records of the target customer. Based on the derived shortest shopping route, the shopping route guidance can be provided to the target customer in this phase. To be specific, the shopping route guidance provides the shopping direction and distance of the next merchandise to the target customer. It is expected that the shopping route guidance model can assist the target customer to efficiently browse the suggested merchandises via the recommended shopping route. The symbols for the shopping route guidance model can be defined as follows:

WLRðRTi Þ

AWLC(X, Y) f(A)

DS Lx(A) LCx(A) LCx LCx LCy LCy LSx ðiÞ LSy ðiÞ NL NR RTi

Set of the turning points corresponding to location (X, Y) The occurrence frequency of aisle turning point set A in the RFID tags detected by the RFID reader within a time interval The shopping route guidance provided for the target customer The X coordinate of the location of tag A (or aisle turning point A) The Y coordinate of the location of tag A (or aisle turning point A) The X coordinate of the location of the target customer before correction The X coordinate of the location of the target customer after correction The Y coordinate of the location of the target customer before correction The Y coordinate of the location of the target customer after correction The X coordinate of the i’th location in the optimal shopping route The Y coordinate of the i’th location in the optimal shopping route The number of locations that the target customer passes through The number of RFID tags detected by the RFID reader Information in the i’th RFID tag detected by RFID Reader

The i’th aisle turning point corresponding to location ðX; YÞ Set of the aisle turning points corresponding to the i’th RFID tag detected by the RFID reader

The to-be operation scenario of a retailer can be revealed via Fig. 6. The key idea for customer positioning is to firstly determine the customer location based on the center of locations of items read by the RFID reader. After that, the derived location is revised based on the alignment of aisles in order to enhance reasonability of the derived customer location. The proposed model utilizes the RFID technology to acquire the coordinate of the location of the target customer. It is assumed that the staff of the retailer offers the UHF RFID reader to the customers, and the customers can carry the reader or put it in the shopping cart during shopping. In addition, RFID tags with the shelf ID are attached on the shelves. The RFID reader can read the information contained in the RFID tags attached to the shelves around the target customer as they walk in the retailer. The coordinate of the target customer location can be determined according to the detected coordinates of the shelf locations. Thus the guidance suggestion about the optimal shopping route can be determined and provided to the target customer. The RFID positioning method used in this model is different from the ordinary one. In the applications of the ordinary RFID positioning method, the RFID readers are arranged in the positioning environment and the RFID tags are attached to the objects (e.g., the customers). The object location is calculated via the method of triangulation location. Many RFID readers are required to ensure accurate positioning. Furthermore, the RFID results are provided to the objects through other medium (such as PDA mobile device) after RFID positioning. This positioning method requires higher costs and might violate individual privacy. Thus, in the proposed model, the RFID tags are attached in the positioning environment and the RFID readers as well as the PDA can be provided to the customers. The positioning process can be performed via PDA. The retailer cannot reveal the customer locations if the PDA is not connected with the back-end system and thus the customer privacy can be guaranteed. The shopping route guidance model includes the shopping route suggestion and determination of moving direction for the target customer. The detailed steps are described as follows.

Fig. 6. The RFID-based operation scenario of a retailer.

J.-L. Hou, T.-G. Chen / Advanced Engineering Informatics 25 (2011) 103–115

4.3.1. Step (C1) – acquisition of recommended shopping route for the target customer In the previous steps (from Step (A1) to Step (B8)), the optimal shopping route for the target customer has been determined based on the pre-defined shopping list or the shopping preferences of the target customer. In this step, the reasoned shopping route can be used as the basis for the subsequent route guidance. 4.3.2. Step (C2) – acquisition of RFID tag information around the target customer In this approach, the location of the target customer can be acquired via the RFID technology. As the target customer walks in the market, the RFID reader carried by him/her can regularly read out the attached RFID tags around the location of target customers. If RFID Reader reads out the information in the NR RFID Tags (number of RFID Tags read by RFID Reader may be different, because it may be affected by location of target customers), the geographical coordinates of the affixed RFID Tag, i.e. (Lx ðRTi Þ and Ly ðRTi Þ) and the set of aisle turning points corresponding to the affixed RFID Tag, (WLRðRTi Þ) can be obtained with the information (RTi ) in RFID Tag (WLRðRTi Þ). 4.3.3. Step (C3) – calculation of geographical coordinates of target customer location In the previous step, the information in the affixed RFID Tag and the relevant geographical coordinates has been obtained. In this method, the mean value of the geographical coordinates of RFID Tag can be defined as the coordinates (i.e. LCx and LCy ) of target customer location before correction. The equations are written as Eqs. (15) and (16).

P LCx ¼

ð15Þ

NR P

LCy ¼

Lx ðRTi Þ

i

customer stands between transverse shelves, Y coordinate value of the turning point corresponding to the target customer location is equal. After correction, Y coordinate value (LCy ) is equal to the Y coordinate value of the corresponding aisle turning point. The inferred coordinate values of the target customer location can be correction according to the principles above in the methodology, as expressed in Eq. (18).

IF

i

Lx ðWLCðLCx ; LC y ; 1ÞÞ ¼ Lx ðWLCðLCx ; LC y ; 2ÞÞ

THEN LCx ¼ Lx ðWLCðLCx ; LC y ; 1ÞÞ ELSE

IF

jLCx  LSx ðNL þ 1Þj > 0

THEN DS ¼ moving along X axis jLCx  LSx ðNL þ 1Þj distance DS ¼ moving along Y axis jLCy  LSy ðNL þ 1Þj distance ð19Þ

ð16Þ

NR

ð18Þ

LCy ¼ Ly ðWLCðLCx ; LC y ; 1ÞÞ

4.3.5. Step (C5) – suggestion of the shopping route guidance The corrected geological coordinates (LCx and LCy ) of the target customer have been determined in the previous step, the number of the locations the target customer pass through is defined as NL, and thus, the next target location is the NL + 1 location in the optimal shopping route, the coordinate value is LSx ðNL þ 1Þ and LSy ðNL þ 1Þ. Later, the comparison between the corrected coordinate value of the target customer location and the coordinate value of the next target location is made. If the X coordinate difference is zero, it means current target customer location and his next target location is in the same longitudinal aisle, and thus it is proposed that the customer moves along Y axis (DS = moving along Y axis jLCy  LSy ðNL þ 1Þj distance). On the other hand, if the Y coordinate difference is zero, it means the location of current target customers and his next target location is in the same transverse aisle, and thus it is proposed that the customer moves along X axis (DS = moving along X axis jLCx  LSx ðNL þ 1Þj distance). Eq. (19) is the equation for shopping route guidance.

ELSE

Ly ðRTi Þ

111

5. Development of Shopping Service System 4.3.4. Step (C4)?Correction of geographical coordinate of target customer location Limited by the read-out range and ability of the RFID Reader, the mean value of the coordinates of RFID Tag read by RFID Reader which is defined as the coordinate of the target customer location may have error. In order to reduce the error, the inferred coordinate values of the target customer position can be corrected based on the coordinates of the aisle turning point corresponding to the target customers’ location in the method. The information in the affixed RFID Tag around the target customers’ position is obtained in the previous steps. The f ðWLRðRTi ÞÞ is the occurrence frequency of the set of the aisle turning points corresponding to the information in each RFID Tag read by RFID Reader. The highest frequency of occurrence of set of the aisle turning points is taken and is defined as the set of the aisle turning points corresponding to the target customer position, AWLCðLCx ; LCy Þ where the i aisle turning point is WLCðLCx ; LC y ; iÞ, the coordinates value of the turning point are Lx ðWLCðLCx ; LC y ; iÞÞ and Ly ðWLCðLCx ; LC y ; iÞÞ. The equation is shown as Eq. (17).

AWLCðLCx ; LC y Þ ¼ WLRðAÞ where A ¼ fajf ðWLRðaÞÞ ¼ Max f ðWLRðRTi ÞÞg i

ð17Þ

If the target customer stands between the longitudinal shelves, the X coordinate value of the turning point corresponding to the target customer location is equal. After correction, the X coordinate value (LCx ) of the target customer location is equal to the X coordinate value of the corresponding aisle turning point. Contrarily, if the target

This research established a Shopping Service System for the shopping list determination, shopping route determination and guidance. This system establishment was based on Microsoft Pocket PC. VB.Net (Visual Basic.Net) was used to develop the various system functions and system operation interfaces. In addition, Microsoft SQL Server CE was used for the back-end system to store and manage the relevant data. This system can be divided into four modules, which are Shopping Route Planning Module, Merchandise Data Maintenance Module, Shelf Location Data Maintenance Module and User Data Maintenance Module. In the proposed Shopping Service System, the system users can be divided into the common users and administrators. The common user can perform the shopping route determination and guidance functions when logging into the system. This system can infer the shortest shopping route with the shopping list or shopping condition input by the user. Furthermore, it can estimate the total length of the route and total time needed for shopping according to the browse sequence of the merchandises. The information will be displayed on the system page to help the customer know the recommended shopping route (Fig. 7). Later, users can perform the function of the shopping route guidance to obtain the real-time shopping route guidance result (see Fig. 8). The shopping route guidance information includes the next target location and the direction and distance of it. In addition, the common user can perform the user data maintenance function. Besides the functions mentioned above, the administrator can perform user data maintenance function, merchandise data maintenance function (Fig. 9)

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Fig. 7. Shopping route determination result.

Fig. 8. Shopping route guidance result.

and shelf location data maintenance function (Fig. 10). In summary, the modules of this system are expected to assist customers plan their shopping route and achieve efficient shopping.

Fig. 9. Merchandise data maintenance.

Fig. 10. Shelf location data maintenance.

300 and 500 types of merchandises were established after referring to the various supermarkets. The mean time needed for browsing merchandises, the display shelf code of each good and previous shopping records of 30 customers were produced by using a random number function.

6. System performance evaluation 6.2. Simulated supermarket In order to validate the feasibility of the proposed system, a simulated supermarket was established after referring to the data collected from various supermarkets. The simulated supermarket was used as the scene of system application to validate the performance of the method and system function modules. The validation and evaluation of the system was divided into System Database Establishment, Simulated Supermarket Establishment and Testing Analysis of Shopping Route Planning and Guidance. The details are described in the following sections. 6.1. System database In this research, all the relevant data of simulated supermarket were established to validate the system after referring to the operating data of various supermarkets. For the simulated supermarket, the data of 100 shelves was established and geographical coordinate of the shelves, and the coordinate values of the corresponding aisle turning points were defined in this research. The data of 100,

A simulated supermarket was established after referring to the shelf arrangement of various supermarkets. The simulated supermarket included two shelves and 12 shelves. Among them, the length and the width of the shelf was 30 cm. Each shelf was affixed with a RFID Tag. The width of the aisle between shelves was 100 cm. It was hypothesized that the shopping cart is equipped with hand-held PDA Reader from Symbol Company. RFID was applied to the simulated supermarket. The RFID reader is located in different locations of the simulated aisles to test the positioning accuracy of the proposed approach. The shelf arrangement is shown in Fig. 11, and the actual simulated supermarket is shown in Fig. 12. 6.3. Test and analysis of shopping route determination and guidance The key point of this system was to avoid time-consuming and inefficient shopping planning route based on the previous experi-

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Shelf 1

Shelf 2

Shelf 3

Shelf 4

Shelf 5

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

Aisle

Aisle

Shelf 6

RFID Tag

Aisle 100 cm

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

Shelf 7

Shelf 8

Shelf 9

Shelf 10

Shelf 11

Shelf 12

100 cm A

30 cm

30 cm B

Fig. 11. Shelf arrangement of simulated supermarket.

Table 1 Performance of distinct route determination algorithms.

Shopping distance Shopping time Improving rate of A

A

B

C

527.53 m 122.06 min —

826.27 m 152.07 min 36.15%

821.40 m 151.50 m 35.78%

Note: A: dynamic programming; B: local search; C: random search.

tion, random number function of Excel was used to generate the data of 30 customers. The recommended shopping route planning function was implemented for 30 customers in the supermarket where the merchandise type number was 100, 300 and 500. The planned shopping time was set 1 h, and the walking speed is 10 m/min. The average system planning time was 2.633 s, 4.367 s, and 5.700 s for 100 merchandise types, 300 types, and 500 types after calculation, respectively. The effect of supermarket scale on the route planning time is shown in Fig. 13. The system planning time was very short and the increase range of the planning time was not high with the expansion of the supermarket size (the increase in merchandise number). Thus, the shopping route planning method proposed in this research could provide the route planning services in short time. The efficiency of the assistance service was proven to be optimal.

Fig. 12. Simulation of supermarket.

6 5 4 3 2 1 0

6.5. Comparison of shopping route planning performance 100

300 # of Items

500

Fig. 13. Effect of distinct supermarket scales on system planning time.

ence of customers. For the subject of the testing and analysis of shopping route planning, this research planes the system verification comprised of three sub-subjects including the influence of supermarket size on system planning time, comparison of shopping route performance and RFID positioning efficiency testing. The system verification results of the three sub-subjects are described in the following section. 6.4. Effect of supermarket scale on system planning time In order to know the influence of different supermarket scales on the planning time of the recommended shopping route func-

In order to realize the performance of dynamic programming of shopping route, the results of dynamic programming of shopping route (equivalent to Global Search route planning), the shopping route planning based on the preemptive purchase of the merchandise near customer location (customers often preemptively buy the merchandises near themselves and continue to select the next merchandise in this way until all listed merchandises are purchased; it is similar to Local Search) and the shopping route planning based on high purchase intention (customers browse and purchase merchandises according to the sequence from the high to low degree of purchase intention; similar with Random Search) were compared. As indicated in the 30 groups of the shopping route planning results of the dynamic programming, the improving rate of the average route length was 36.15% when comparing with the shopping route planning via the preemptive purchase of the merchandises near the customer location. According to the results, the customers could save 36.15% of route length if they utilize the

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Shelf 1

Shelf 2

Shelf 3

Shelf 4

Shelf 5

Shelf 6

Shelf 1

Shelf 2

Shelf 3

Shelf 4

Shelf 5

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

(10,110)

(40,110)

(70,110)

(200,110)

(230,110)

(260,110)

(10,110)

(40,110)

(70,110)

(200,110)

(230,110)

(260,110)

Point 1

Point 2

Point 3

Point 7

Point 8

Point 1

Point 2

Point 3

Point 7

Point 8

Point 4

Point 10

Point 11

Point 12

Point 6

Aisle

Aisle Point 13

Point 14

Point 15

Point 16

Point 9 RFID Tag

Point 17

Point 18

Point 19

Point 20

Point 21

Point 22

Point 23

Point 4

Point 24

Point 10 RFID Tag

Point 11

Point 12

Point 5

Aisle

Point 9

Aisle

Aisle

Point 5

Point 6

Aisle Point 13

Point 14 RFID Tag

RFID Tag

Point 20

Shelf 6

Point 17

Point 18

Point 19

(40,10)

(70,10)

Point 21

(2 0 0 , 1 0 )

Point 22

Point 15 RFID Tag

Point 23

Point 16 RFID Tag

Point 24

(1 0, 10)

(40,10)

(70,10)

(200,10)

(230,10)

(26 0 ,10 )

(10,10)

(230,10)

(2 6 0 , 1 0 )

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

RFID Tag

Shelf 7

Shelf 8

Shelf 9

Shelf 10

Shelf 11

Shelf 12

Shelf 7

Shelf 8

Shelf 9

Shelf 10

Shelf 11

Shelf 12

Fig. 14. Distribution of RFID positioning effectiveness.

results generated via the dynamic programming. In addition, in the dynamic programming algorithm, the improving rate of the average route length was 35.78% when comparing with the shopping route planning based on high purchase intention. It was found that the shopping route planning based on the dynamic programming was superior to the route planning based on high purchase intention. From the above results, the proposed route planning based on the dynamic programming was superior to the route planning often adopted by the common customers (Table 1). 6.6. Test of RFID positioning effectiveness The proposed method for the route planning and guidance was applied to the simulated market where the RFID Tag attached to the supermarket shelf and the shopping cart was equipped with an RFID reader. The RFID reader can automatically read the information of RFID tags attached to the shelves around the shopping cart (the customer location) when customers go shopping in the supermarket. The coordinates of customer location were determined based on the information of RFID tags. The shopping route guidance information could be provided for the customers with the coordinates of customer location and the route planning result. In this research, a simulated supermarket was established based on this system application. RFID tags were attached to the 12 or 18 specified locations (shelves or aisles) in the simulated supermarket. Twenty-four fixed points were selected from the supermarket as the testing points of system positioning for two-phase system positioning tests. The polling period of the RFID reader is 3 s. The test results indicate that, when 12 RFID tags attached in the supermarket were used as the positioning basis, the success rate of system positioning was 50% (distribution of the testing points with successful positioning is shown in Fig. 14(a)), and standard error was 5 cm. As the number of the RFID tags increased to 18 (in average, 2.55 RFID tags/cm2), the success rate reached 100% (distribution of the testing points with successful positioning is shown in Fig. 14(b)), the mean error was only 1.25 cm. Thus, the positioning accuracy of target customer based on RFID was very high in this model. As a whole, based on the distinct indices (e.g., effectiveness of RFID positioning, shopping route planning performance and time for shopping route planning) the proposed shopping route determination and guidance algorithms and Shopping Service System can be applied in supermarket for higher shopping service quality. 7. Conclusions and future work The proposed shopping route determination and guidance methodology with the RFID technology can provide high-quality shopping services for customers. Among the services, determina-

tion of the customized merchandises list recommended to be browsed can provide the customers without definite shopping list to quickly browse merchandises, so the customers do not need to spend too much time browsing merchandises that they do not need and are not interested in. The shopping route can assist the customers without rich shopping experience or better space awareness to plan the shortest shopping route, the shopping route planning result can help the customers reducing the moving time for browsing merchandises and can greatly improve the efficiency of shopping, Lastly, the shopping route guidance service can help the customers moving quickly to the shelf of merchandises which they want to find in a supermarket with complex aisle arrangement. It can reduce the time customers spent on finding shelves and raise the satisfaction of the customers. However, as indicated in the assumptions of this research, the limitations of this study include: The shopping history of distinct customers should be maintained in the database in advance for shopping list generation. Some parameters about customer behaviors (e.g., the walking speed and time for browsing merchandise) should be given and keep constant for optimal shopping route determination. In the future, there are still issues to be further discussed, such as incorporating factors in the determination of customized merchandise list recommended to be browsed so as to ensure that the recommended list will better adapt to the customer demands or be close to the sales promotion goals; or the shopping route can be replaced with the integrated information of customers number and the location distribution. Future studies can also consider the moving tendency of the customer location so as to provide the visualized route guidance information, and thus the system will be more suitable for customers. References [1] M. Baum, B. Niemann, F. Abelbeck, D.H. Fricke, L. Overmeyer, ‘‘Radio frequency identification application in smart hospitals”, in: Proceedings of the 20th IEEE International Symposium on Computer-Based Medical Systems, 2007, pp. 337–342. [2] W.Y. Chang, ‘‘Flight path planning of multiple objectives inspection”, Graduate Institute of Aeronautics and Astronautics, National Cheng Gung University, Doctoral Dissertation, 2007. [3] Y.K. Chen, ‘‘Route planning analysis of logistics distribution practice – genetic algorithm”, Graduate Institute of Information Management, Ta Tung University, Master’s Thesis, 2006. [4] C.H. Chiou, ‘‘Research on goods wagons route in logistics distribution centers”, Graduate Institute of Civil Engineering, National Taiwan University, Master’s Thesis, 2002. [5] K.H. Chow, K.L. Choy, W.B. Lee, ‘‘Design for a RFID-based resource management system for warehouse operation”, in: Proceedings of the Third IEEE International Conference on Industrial Informatics, 2005, pp. 785–790. [6] F. Gandino, B. Montrucchio, M. Rebaudengo, E.R. Sanchez, ‘‘Analysis of an RFIDbased information system for tracking and tracing in an agri-food chain”, in: Proceedings of the First Annual RFID Eurasia, 2007, pp. 1–6. [7] P.M. Goodrum, M.A. McLaren, A. Durfee, ‘‘The application of active radio frequency identification technology for tool tracking on construction job sites”, Automation in Construction, vol. 15, No. 3, 2006, pp. 292–302.

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