# Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System

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## Abstract

The information transmission path optimization (ITPO) can often affect the efficiency and accuracy of remanufacturing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanufacturing service system, which leads to a critical need for designing planning models to deal with this added uncertainty and complexity. In this paper, a three-dimensional (3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm (Improved AC) was put forward. Moreover, to further improve the operation efficiency of the algorithm, an improved ant colony-genetic algorithm (AC-GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the effectiveness of AC-GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC-GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss.

## Keywords

Remanufacturing service Information transmission Path optimization Ant colony algorithm Genetic algorithm## 1 Introduction

As remanufacturing service process is a highly customized process involving information in various types and large amount, a series of problems will be generated in construction of information platform and information service process. For example, there is significant difference in type, usage degree, and remanufacturability of waste products, which increases the difficulty degree in information preprocessing, waste product resource classification and remanufacturable product information collection. The customer demand of remanufacturing service is of diversity and randomness, and the incoming time and initial status of remanufacturable products are uncertain. All these factors lead to the challenge to demand information matching, process information matching, and decision information processing mechanism; Since remanufacturing service system enjoys a cross-enterprise and cross-region information network, seeking proper information transmission nodes and paths in such network and realizing information source cross transmission to secure the real-time and accurate transmission of information resource are issues to be addressed for the establishment of informatizational service platform of remanufacturing service system.

Regarding above problems, researchers have conducted large amounts of researches on information collection technology, information data structure processing technology, and information matching technology [10, 11], and have built information service modes that suitable for various areas [12]. However, in the aspect of information transmission, researches are insufficient. As we know that an accurate and reliable information transmission plays a vital role in realizing the information service of remanufacturing service system [13], if information network fails to secure the integrity in transmission and exchanging of collected and matched information, will not the information service quality be secured. On this basis, this paper proposed an optimization method of 3D information transmission path based on improved ant colony-genetic algorithm (AC-GA), moreover the effectiveness of such method was analyzed based on the case study of the remanufacturing service demand information transmission of a certain roller.

## 2 Related Works

Comparison of path planning algorithms

Algorithm classification | Algorithms | Basic theory | Advantage | Disadvantage |
---|---|---|---|---|

Traditional algorithm | Simulated annealing algorithm | Simulating the annealing process of solid matter | Simple description, flexible use, high operation efficiency, less limit of initial condition | Slow convergence rate and randomness |

Artificial potential field algorithm | Simulating object motion under gravitation and repulsion force | Smooth and safe route, simple description | Local optimization | |

Fuzzy logic algorithm | Simulating driving experience | Bein in accordance with human mind and conducted without math modeling | Hard to conclude fuzzy rule | |

Tabu search algorithm | Simulating human intelligence behavior, and stepwise global optimization | |||

Graphics algorithm | C space method | Expand the obstacles into polygon in motion space | Intuitive and easy to obtain the shortest route | Poor flexibility |

Grid method | Representing map with the coding trellis | Suitable for environment modeling | Hard to solve complex environmental information problem | |

Free-space method | Building free space using predefined basic shapes | High flexibility | Hard to be realized | |

Voronoi graph method | Performing space division using basic shapes called elements | Realizing effective obstacle avoidance | Taking too much time in redrawing | |

Bio-inspired intelligent algorithm | Ant colony algorithm | Iterative simulation of ants foraging behavior | Sound capacity of overall control, essential parallelism, easy to be realized by computer | Large amount of computation, and easy to fall in local optimization |

Genetic algorithm | Based on biological reproduction mechanism, chromosome selection, chromosome chiasma, and chromosome variation | Easy to be mixed with other algorithms to show iterative dominance | Insufficient operation efficiency | |

Neural network algorithm | Simulating animal neural network, and performing distributed parallel information processing | Excellent learning ability and robustness | Poor generalization ability | |

Particle swarm optimization | Simulating birds flying and feeding behavior | Simplicity, easy to be realized, sound robustness, quick convergence rate | Easy to fall in local optimization |

According to the characteristics of different information route planning algorithms, ant colony algorithm is regarded of significant advantage in remanufacturing service system information route optimization, because it has large amount of information resource involved in remanufacturing service system, numerous information transmission network nodes, and 3D distribution space. Being often introduced in 3D route planning problem of robot under complex environment [20], ant colony algorithm can divide the space between robot location (original point) and destination point into three-dimensional grids and calculate the shortest optimized route between original point and destination point [21]. With strong robustness, ant colony algorithm can realize parallel calculation and is easy to be combined with other algorithms, which is more suitable for solving large-scale dynamic optimization problem [22, 23]. Some researchers have introduced ant colony algorithm in functional optimization and solution of continuous space and fast optimization problem of proliferative process route [24, 25]. The application of ant colony algorithm in self-organized service recommendation network further proved that such algorithm can effectively improve the success rate and recall ratio of service discovery [26]. However due to the large computation amount of ant colony algorithm, this paper proposed an improved ant colony algorithm (Improved AC) by combining normal ant colony algorithm with genetic algorithm than features sound convergence, less computation time, higher robustness, and iterative advantages, and applied the improved AC to solve information transmission route optimization problem of remanufacturing service system.

## 3 Framework of Information Transmission Network

*X*axis,

*Y*axis and

*Z*axis is the physic coordinate of the network

*N*(

*V*,

*E*). In

*N*(

*V*,

*E*),

*V*is the collection of all network nodes (exchangers, routers and hosts),

*E*is the collection of all edges in the figure, where in each edge represents a direct communication path between two adjacent points. Each routing node in information transmission routing network

*N*(

*V*,

*E*) is corresponded to an only routing node coordinate

*p*(

*u*,

*ω*,

*i*). Each

*p*(

*u*,

*ω*,

*i*) ∈

*V*has three attributes: including information transmission delay(

*p*), information communication cost(

*p*) and information packet_loss(

*p*), the attribute parameter can be represented by <delay(

*p*), cost(

*p*), packet_loss(

*p*)>. Moreover, the attribute parameter of each edge

*e*∈

*E*can be represented by <delay(

*e*), cost(

*e*), packet_loss(

*e*)>. The 3D map of routing nodes that corresponds with remanufacturing service information transmission network

*N*(

*V*,

*E*) in Figure 2 is shown in Figure 3.

## 4 Problem Statement

High-efficiency space routing planning is the guarantee for space information transmission [28]. Remanufacturing service information transmission routing planning is to seek an optimal remanufacturing service information communication path from route starting point *S* ∈ *V* to target routing point *M* ∈ {*V* − {*s*}} in the 3D information transmission routing network *N*(*V*, *E*), as shown in Figure 3, where the disabled nodes (information transmission nodes in Load/fault) are distributed. The communication service is guaranteed to meet constraint conditions, such as information delay, commutation cost, information packet_loss.

*N*(

*V*,

*E*) is symmetrical and there is only one edge between two adjacent joints. During information communication

*T*(

*S*,

*M*) process from original point

*S*to target point

*M*, there are 3 attributes including information transmission delay (

*T*(

*S*,

*M*)), information communication cost (

*T*(

*S*,

*M*)), and information packet_loss(

*T*(

*S*,

*M*)):

*T*(

*S*,

*M*)) is the information transmission delay, i.e., the mean transit time from information original point to information receiving user (μs).

*n*

_{s}is the number of originating node. \(t_{\text{d}}^{i}\) is the time interval from sending data at originating node i to information receiving user (μs). cost(

*T*(

*S*,

*M*)) is information communication cost, i.e., the total unit energy consumption for information communication transmission.

*e*

_{i}is the unit energy consumed by routing node

*i*in receiving and sending data.

*N*is the number of all routing nodes that are involved in

*T*(

*S*,

*M*) transmission path. packet_loss(

*T*(

*S*,

*M*)) is the probability that information fails to be transferred to user nodes under the condition where the average load factor of the route \(k = {{\sum\nolimits_{i = 1}^{N} {k_{i} } } \mathord{\left/ {\vphantom {{\sum\nolimits_{i = 1}^{N} {k_{i} } } N}} \right. \kern-0pt} N}.\)

*k*

_{i}is the load factor of the

*i*th routing node in the information transmission route. \(\sum\nolimits_{i = 1}^{N} {D_{i} }\) is the total amount of data sent from all routing nodes in

*T*(

*S*,

*M*) transmission path,

*D*

_{i}is the amount of data sent from the node

*i. D*

_{s}is the total amount of data received by user node

*M*.

## 5 Optimization Method for Information Transmission Path

### 5.1 The Improved Ant Colony Algorithm (Improved AC) for Path Planning

Basic ant colony algorithms are only suitable for 2D path planning problem [26]. However, regarding the 3D path planning problem in 3D map of information transmission routing network *N*(*V*, *E*) shown in Figure 3, this paper proposes a 3D path optimization AC algorithm for remanufacturing service information transmission by referring the thought of 3D space robot path planning ant colony algorithm, and concrete steps are shown below.

**Step 1:** Coordinate transformation. As shown in Figure 3, the straight-line distance from information original point *S* to target point *M* is *L*, and obstacles *O*_{1}, *O*_{2},…, *O*_{K} are distributed between *S* and *M*.

*O*-

*XYZ*in the network

*N*(

*V*,

*E*) shown in Figure 3, point

*O*can coincide with

*S*point and the original point

*O*′ of the new coordinate can be obtained. In addition, the three original axes of

*XYZ*are rotated by angle of

*ε*

_{X},

*ε*

_{Y},

*ε*

_{Z}, respectively, making

*O*′

*Y*′ axis coincide with

*SM*[29]. Thus the new coordinate system

*O*′-

*X*′

*Y*′

*Z*′ is obtained as shown in Figure 4.

The transformation relation between coordinate *O*′-*X*′*Y*′*Z*′ and *O*-*XYZ* is:

*R*:

(2) In coordinate system *O*′-*X*′*Y*′*Z*′, the cube ABCD-EFGH is established, which constructs the information space network *N*′(*V*′, *E*′) shown in Figure 4. In the network, the ABCD surface of the cube, which is basically a square plane with side length of *L*, is on *X*′*Z*′ plane. Side AB coincides with *X*′ axis, side BC//*Z*′ axis, and the original point *O*′ and point *A* are in the same position. The cube side AE = *L*, and the coordinate of point *M* is (0, *L*, 0). The *O*′*M* is divided into (*n* + 1) equal segments. Through each equal diversion point, *n* planes Π_{i} (*i* = l, 2, …, *n*) which are parallel to ABCD can be obtained. The square plane Π_{i} (*i* = l, 2, …, *n*) is divided into *m* × *m* equal little squares. Therefore, the real coordinate of the point *p*(*u*, *ω*, *i*) ∈ *V* (*u*, *ω* = 0, l, …, *m*) on square plane Π_{i} in coordination system *O*′-*X*′*Y*′*Z*′ is

**Step 2:** Allow list calculation and goal conversion. Suppose there are totally *W* = (*w*1, *w*2,…) items of remanufacturing service information on network space *N*′(*V*′, *E*′) to be simultaneously transmitted from original point S(0,0,0).

Calculate the allowed(*u*, *i*, *ω*) (*u*, *ω* = 0, l, …, *m*) of all points on plane Π_{i}(*i* = l, 2, …, *n* − 1): suppose *p*(*u*, *ω*, *i*) is a certain point on plane Π_{i}(*i* = l, 2, …, *n* − 1), regarding a random point *p*(*k*, *i* + 1, *q*)(*k*, *q* = 0, l, …, *m*) on plane Π_{i+1}, if *p*(*k*, *i* + 1, *q*) is fault-free, the information on line segment *p*(*u*, *i*, *ω*)*p*(*k*, *i* + 1, *q*) can be successfully transmitted, therefore point *p*(*k*, *i* + 1, *q*) can be added into allowed(*u*, *i*, *ω*). Based on such method, all allowed reachable points of *p*(*u*, *i*, *ω*) can be calculated and stored in allowed(*u*, *i*, *ω*). Remove all nodes outside of allowed list as well as links that dissatisfy bandwidth constraints.

Suppose the properties of path form point *p*(*u*, *i*, *ω*) to point *p*(*k*, *i* + 1, *q*) is <delay(*T*(*p*_{i}, *p*_{i+1})), cost(*T*(*p*_{i}, *p*_{i+1})), packet_loss(*T*(*p*_{i}, *p*_{i+1}))>, thus the problem of information transmission path optimization can be transformed into calculating the minimum of the weighted path properties, that is MIin *O*(*p*_{i}, *p*_{i+1}) = [delay(*T*(*p*_{i}, *p*_{i+1})) cost(*T*(*p*_{i}, *p*_{i+1})) packet_loss (*T*(*p*_{i}, *p*_{i+1}))] *W*(*p*_{i}, *p*_{i+1}). *W*(*p*_{i}, *p*_{i+1}) is weight matrix of the path properties.

**Step 3:** Initialization of the pheromone list. Initialize the pheromone list \(\tau_{u\omega }^{i}\) of all points *p*(*u*, *i*, *ω*) (*u*, *ω* = 0, l, …, *m*) on plane Π_{i}(*i* = l, 2, …, *n* − 1): pheromone list is an array, where each data is used to represent the joint strength of pheromone between *p*(*u*, *i*, *ω*) and *p*(*k*, *i* + 1, *q*). Suppose the initialized pheromone \(\tau_{u\omega }^{i} (0) = {\text{A}}\quad \Delta \tau_{u\omega }^{i} (0) = 0\) (A is a certain constant).

**Step 4:**Calculation of transition probability. According to heuristic information value and pheromone value, in each procedure of information transmission path establishment in coordinate system

*O*′-

*X*′

*Y*′

*Z*′, the next node of

*p*(

*u*,

*i*,

*ω*) in information transmission is determined based on Eq. (6).

*J*is a random variable produced according to probability distribution shown in Eq. (7).

*r*is a constant within [0, 1], and

*r*

_{0}is even-distributed random number within [0, 1].

*p*(

*u*,

*i*,

*ω*) on plane Π

_{i}(

*i*= l, 2, …,

*n*), the probability of

*p*(

*k*,

*i*+ 1,

*q*) on plane Π

_{i+1}is selected, which is shown as Eq. (7):

*τ*

_{i+1}is the amount of pheromone stored on point

*p*(

*k*,

*i*+ 1,

*q*), \(\eta_{i,i + 1} = {1 \mathord{\left/ {\vphantom {1 {d(p_{i} ,p_{i + 1} )}}} \right. \kern-0pt} {d(p_{i} ,p_{i + 1} )}}\) is heuristic function, \(\beta = \left\{ {\begin{array}{l} {{{(4t - 2r)}/ t},0 \le r \le t} \\ {2,t \le q} \\ \end{array} } \right.\) (

*t*is the critical time) is heuristic value,

*β*reflects the level of emphasis that has been placed on total heuristic information in selection of information transmission paths during information transmission process.

**Step 5:**Select the next node and perform partial updating. At each node, to which information is transferred, Eq. (8) will be immediately used to perform real time updating of partial pheromone. If information arrives at certain node where no proper next node can be sought, such information is regarded invalid.

*p*(

*u*,

*i*,

*ω*),

*μ*is a certain parameter between 0–1, \(\tau_{0}\) is the initial value of pheromone of each feasible point.

**Step 6:**Judge whether arriving at target node or not. In judging whether the transmitted information meet with each other at certain point, if there exists information meeting, then meeting strategy operation will be performed and new path will be generated according to Eq. (9) and placed into path table. After that, the updating process is performed according to Eq. (10). Otherwise, transit to procedure (4).

*L*

_{new}represents the length of the new path that is generated when path length

*L*(

*w*

_{1}) meets path length

*L*(

*w*

_{2}), Δτ = 1/

*L*

_{new}.

**Step 7:**Global updating. Perform global pheromone updating according to Eq. (11):

*ρ*(0 <

*ρ*< l) is the pheromone volatility; 1 −

*ρ*is attenuation degree of pheromone with time; \(\Delta \tau_{u\omega i} = \sum\nolimits_{i = 1}^{W} {\Delta \tau_{u\omega i}^{w} }\) is the information gain of each path after performing iteration (\(\Delta \tau_{u\omega i}^{w} = {1 \mathord{\left/ {\vphantom {1 {L_{\text{w}} }}} \right. \kern-0pt} {L_{\text{w}} }},\) the

*w*th information goes through the node

*p*(

*u*,

*i*,

*ω*) in current transmission process; otherwise \(\Delta \tau_{u\omega i}^{w} = 0\)).

**Step 8:** Judge stop condition. Judge whether the algorithm meets the stop condition. If the stop condition is met, the optimized results can be output, otherwise turn to step 4. After all information to be transferred go through step 1 to step 8, the path from *S* to *M* is sought successfully.

### 5.2 The Further Improved Ant-Genetic Algorithm (AC-GA) for Path Planning

According to the operation flow of AC-GA-based 3D path optimization, and based on initial population established by genetic algorithm and collected node information, the optimized route can be determined, where the calculation steps are shown from step 9 to step 14.

**Step 9:** Population initialization. Each ant reaching sink node in stipulated cycle (an alternate path) is regarded as an individual of population *P*(*t*). The individual coding is conducted by using ID number of node as gene code. When the stipulated population size is met, the AC algorithm is finished, otherwise turn to step 4.

**Step 10:**Calculation of individual fitness degree. Through population initialization, each individual fitness degree

*F*can be calculated by Eq. (12), and then

*F*is ranked in the order from big value to small value,

*N*individuals (

*N*<

*M*):

*E*

_{i+1}is the communication cost of node

*i*+ 1,

*i*+ 1 is the selected next node.

*R*

_{i,i+1}is the signal strength received by node

*i*+ 1.

*x*is the path from original point to sink node;

*n*

_{x}is the hop count of

*x*.

*α*,

*β*,

*λ*are weight constants. Such function is designed to balance routing efficiency and cost, thus realizing relatively higher cost-benefit ratio.

**Step 11:**Selection operation. The population

*P*(

*t*) was subjected to proportional selection as well as probability selection using Eq. (13). Therefore, some individuals with larger fitness degree are more likely to be preserved to the next generation.

**Step 12:** Crossover and mutation operation. The individual was subjected to single-point crossover operation, where the crossover point will be selected between two individuals with the same gene. If both individuals share two pairs of the same genes, then either pair of gene can be selected, otherwise cancel the crossover operation.

Individual *i* was subjected to mutation operation. Mutant gene was selected from collection *Ω*, *Ω* = *N*_{b}{*i*}{*i* − 1}∩*N*_{b}{*i*}{ *i*+ 1}; *i* − 1 and *i* + 1 respectively represent both neighbours of node *i*; *N*_{b}{*i*} represents the collection of nodes which are of effective communication. If *Ω* ∊ Φ, mutation operation can be avoided, otherwise evolutionary algorithm is performed for such individual to remove the same genes, and thus avoid being trapped into endless loop.

**Step 13:**Elimination operation. By mixing

*M*individuals with

*N*individuals, a new population including

*M*+

*N*individuals in step 9 and step 12 can be obtained. After that the individuals with lower fitness degree were multiplied with penalty factor, which can be shown in Eq. (14):

*μ*∈ (0, 1) is penalty factor,

*F*

_{min}(

*x*) is the individuals with lower fitness degrees.

**Step 14:**Judge whether the algorithm is finished or not. The change rate

*ξ*of fitness degree is defined as:

*δ*is a threshold value of change rate of fitness degree.

*j*is the number of iterations from

*n*to

*n*−

*j*.

*n*is the current number of iterations. When

*ξ*>

*δ*, it turns to step 10, otherwise the algorithm is finished.

## 6 Numerical Study

### 6.1 Illustrative Example

Taking the certain roller remanufacturing service demand information transmission of certain roller as example, suppose the coordinate of real-time routing node where user submits service demand is *S*(3, 1, 9), while the coordinate of routing node where the platform accepts demand information of remanufacturing service is *M*(16, 16, 6). The network for sending the information in remanufacturing service system is 100 Mbit/Ethernet.

*S*′

*M*′ in the

*Y*′ direction was divided into 16 equal segments. After non-dimensionalization, the 2D plane was divided into 16 × 16, each grid’s length was set to 1. The designed 3D path planning AC-GA algorithm was programmed using MATLAB R2012a. The optimized communication path from original point

*S*(3, 1, 9) to

*M*(16, 16, 6) was simulated, resulting in the optimized 3D path simulation results in

*O*′-

*X*′

*Y*′

*Z*′ (Figure 6(a)) and in

*O*-

*XYZ*(Figure 6(b)) and path coordinate table (Table 2). According to Eqs. (1) and (2), along such optimized path, the information transmission delay is 206.5148 μs and the communication cost is 20.0588. According to Eq. (3), information packet_loss is 7.7375% while the average information load factor is 53.4723%. Finally, the roller remanufacturing service demand information was transferred along the optimized path specified in Table 2.

Node coordination of optimized 3D path

(3, 1, 9) | (8, 2, 8) | (11, 3, 13) | (2, 4, 7) |

(16, 5, 10) | (6, 6, 7) | (13, 7, 14) | (9, 8, 12) |

(10, 9, 1) | (3, 10, 11) | (4, 11, 11) | (10, 12, 2) |

(4, 13, 14) | (8, 14, 16) | (6, 15, 1) | (16, 16, 6) |

### 6.2 Performance Results and Discussion

#### 6.2.1 Result Validation

#### 6.2.2 Performance Analysis of AC-GA Algorithm

*T*(

*S*,

*M*)), cost(

*T*(

*S*,

*M*)), packet_loss(

*T*(

*S*,

*M*)) during the optimization process of information transmission path planning, the performance analysis of AC-GA algorithm can be realized.

- (1)
Analysis of information transmission delay

*T*(

*S*,

*M*)) of improved AC and AC-GA algorithm during the optimization process of information transmission path planning can be calculated, respectively. The comparison results are shown in Figure 8.

- (2)
Analysis of information communication cost (

*T*(*S*,*M*))

*T*(

*S*,

*M*)) of improved AC and AC-GA algorithm under 3 different meshing situations of 9 × 9, 16 × 16, 23 × 23, can be calculated respectively. The comparison results of both total communication costs under 3 different meshing situations are shown in Figure 9. It can be seen that with the increase of network scale, the communication cost of AC-GA algorithm is significantly lower than that of improved AC.

- (3)
Analysis of information packet_loss (

*T*(*S*,*M*))

*T*(

*S*,

*M*)) of two algorithms when network node average load factor is 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% can be calculated, respectively, and the results are shown in Figure 10. It can be seen that the higher the information load factor is, the higher the information packet_loss(

*T*(

*S*,

*M*)) will be.

## 7 Conclusions

- (1)
By referring the service-oriented manufacturing thought and combining the features of remanufacturing and service, the concept of remanufacturing service was proposed and a 3D framework for remanufacturing service information transmission network was built.

- (2)
Since the traditional ant colony algorithm (AC) is only suitable for two-dimensional path planning but not applicable for three-dimensional path planning, an improved 3D ant colony algorithm (Improved AC) was proposed to solve the problem of 3D path optimization in the information network.

- (3)
To further improve the convergence of the algorithm and decrease the number of iterations, this paper proposed an improved ant colony-genetic algorithm (AC-GA) by combining ant colony algorithm and genetic algorithm to realize the 3D information transmission path optimization in remanufacturing service system.

- (4)
Taking the remanufacturing service demand information transmission of certain roller as example, the effectiveness of such algorithm was analyzed. Through comparative analysis of performances, it can be demonstrated that the AC-GA algorithm has good operation efficiency in information transmission path optimization. In addition, AC-GA algorithm has advantages over improved AC in aspects of information transmission delay, information communication cost and information pakect_loss. This study lays foundation for remanufacturing service system research, and provides the basic theories on the system information transmission as well as references for the research and application of remanufacturing service in other industries.

## Notes

### Authors’ Contributions

X-HX was in charge of the whole trial; LW wrote the manuscript; J-HC, XL and J-WL assisted with sampling and laboratory analyses. All authors read and approved the final manuscript.

### Authors’ Information

Lei Wang, born in 1987, is currently a lecturer at *Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering*, *Wuhan University of Science and Technology*, *China*. Her main research interests include manufacturing/remanufacturing systems engineering, reverse supply chain management, knowledge management and engineering.

Xu-Hui Xia, born in 1966, is currently a professor and PhD candidate supervisor at *Wuhan University of Science and Technology*, *China.* His main research interests include manufacturing systems engineering, supply chain management, optimization and decision with application in manufacturing industry.

Jian-Hua Cao, born in 1989, is currently an assistant engineer and PhD candidate at *Wuhan University of Science & Technology*, *China*. His main research interests include manufacturing/remanufacturing systems engineering and reverse supply chain management.

Xiang Liu, born in 1983, is currently an engineer at *Wuhan University of Science and Technology*, *China*. His main research interests include image processing, computer vision and its application in manufacturing engineering.

Jun-Wei Liu, born in 1983, is currently a lecturer at *Wuhan University of Science and Technology*, *China*. His research interests are in the areas of manufacturing informatization and intelligent manufacturing.

### Competing Interests

The authors declare that they have no competing interests.

### Funding

Supported by National Natural Science Foundation of China (Grant Nos. 51805385, 71471143), Hubei Provincial Natural Science Foundation of China (Grant No. 2018CFB265), and Center for Service Science and Engineering of Wuhan University of Science and Technology (Grant No. CSSE2017KA04).

### Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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