Novel Computational Intelligence for Optimizing Cyber Physical Preevaluation System
Abstract
Owing to the quality heterogeneity of returned used products, firms engaged in remanufacturing activities are obliged to employ 100 % inspection of these products to evaluate their quality and suitability for remanufacturing. In addition to visual inspection, a recent tendency is to use data recorded in electronic devices (e.g., radio frequency identification (RFID)) implanted in the products. In this way, information is obtained quickly without the need for complete (and expensive) product disassembly. Nevertheless, making sense of RFID data in a complex cyber physical system (CPS) environment (which involves such as cloud computing for used product life cycle information retrieval and physically used products scanning) is a complex task. For instance, if an RFID readers fails, there may be missing values exist. The purpose of this chapter is to employ two computational intelligence (CI) optimization methods which can improve the reliability of such inspection process.
Keywords
Remanufacturability Cyber physical preevaluation system Reliabilityredundancy allocation problem Firefly algorithm Teaching–learningbased optimization Radio frequency identification1 Introduction
Returns acquired by a remanufacturer are typically highly variable in quality [16]. A significant consequence of this uncertainty is the inclusion of a collection/classification stage and a corresponding system of qualitydependent routing of supply in a reverse logistics network [13]. The potential value of sorting and classification product returns has been explored by different researchers such as [7] and several sorting policies have also been proposed in the literature (e.g., [4]). In addition, the management of product return is characterized also by the lack of information associated with such used products [29]. The recent emergence of networked radio frequency identification (RFID) system is a means of connecting a product tagged with an RFID chip to a network and thereby carrying complete information associated with it throughout its lifecycle. In this way information is obtained quickly without the need for complete (and expensive) product disassembly [53]. Several authors (e.g., [14, 27, 34]) have mentioned the use or potential use of RFID and related technology in the reverse logistics network. Nevertheless, making sense of RFID data is a complex task. For instance, if an RFID readers fails, there may be missing values exist [10]. The purpose of this chapter is to employ two innovative computational intelligence (CI) approaches for improving the reliability of such classification/inspection process.
The remainder of this chapter is organized as follows. Subsequent to the introduction in Sect. 1, the background of cyber physical Remanufacturability preevaluation system is briefed in Sect. 2. Then, the problem statement is presented in Sect. 3 which is followed by a problem formulation detailed in Sect. 4. The proposed methodologies are then detailed in Sect. 5. Next Sect. 6 conducts an experimental study to demonstrate the feasibility of our proposed approaches. The future research directions are highlighted in Sect. 6. Finally, the conclusion is drawn in Sect. 6 of this chapter.
2 Background of Cyber Physical Preevaluation System
2.1 What is Remanufacturability?
There is a growing interest in remanufacturability analyses of the remanufacturing systems since it is the key element to maintain customer satisfaction and thus company profitability. Generally speaking, remanufacturability is the ability of used products to be easily remanufactured and be determined by the configurable parameters, the failed state, and the remanufacturing technology [49]. Regarding the configurable parameters, Wu [42] pointed out that the influent factors included the technological feasibility of remanufacturing, the economic feasibility of remanufacturing, the environmental feasibility of remanufacturing, and the product’s service ability. In a similar vein, [12] emphasized that for evaluating the remanufacturability of used products, an integrated method in which the technology feasibility (including disassembly, cleaning, inspection and sorting, par reconditioning, machine upgrading and reassembly), economic feasibility (focusing on the remanufacturing cost), and environmental benefits (such as energy saving, material saving and pollution reduction) should be analysed. Furthermore, some researchers (e.g., [2, 8, 51]) proposed that to enhance remanufacturability of used products, manufacturers should take into account the early stages of the products’ designs. In the light of this statement, in [17, 38, 40], the authors stated that different design structure matrix could be used as a very useful tool to examine the relationship between the different processes in order to obtain a clear ranking of the easily activities of remanufacturing.
2.2 Why Remanufacturability Preevaluation?
In most cases, remanufacturing processes must adopt the activity of preevaluation because products have not been designed to be remanufactured [52]. This activity has extracted the “secret” affecting the success of remanufacturing since it allows for the selective using of desired parts and/or materials. In other words, it provide a relatively efficient and effective means for a remanufacturer to obtain feedback before the used products are admitted into the remanufacturing plant, specially, the information about which used products/parts can be disassembled [43].
2.3 Cyber Physical Preevaluation System
One of the ways to evaluation such ability is through cyberphysical system (CPS), which, in our context, use sensorembedded products with networked computing to control the evaluation processes in order to remove uncertainty to the remanufacturing systems. An early of the successful marriage of sensorbased products and evaluation processes is radio frequency identification (RFID) tag. The advent of RFID tag is critical to automatic identification, movement tracking, access control, information collection, and evaluation of operation/system’s performance. Furthermore, it is also considered by some researchers (e.g., [23, 24, 25, 29]) as one of the most technology for revolutionizing a wide range of applications including remanufacturing and reverse logistics.
3 Problem Statement
When a used product is collected, the first step is to evaluate its remanufacturability, which is the premise to decide whether it is worthy to remanufacture the product. At this stage, effective and reliable systems are required to gather and evaluate product usage data. Recently, some studies (e.g., [1, 28, 53]) have reported permanent sensor embedded tagging (such as RFID) may generate valuable information for improving the efficiency of remanufacturing process. Their analyses suggested that, since there is a high level of uncertainty about the quality of components entering the remanufacturing process, RFIDderived information can assist in sorting components where manual inspection is traditionally employed.
This may be true for parts that are sensitive to remanufacture, however, one of the major puzzles that RFID classification system has posed for practitioners is the reading accuracy and system reliability after its adoption. Bearing this in mind, in this study, we are about to set up a 4stage inspection procedure to keep the misclassification rate [53] at the lowest level. Each stage is constituted of an RFID inspecting system that is responsible for a certain type of data collection and evaluation. While the used products flow passing through these four inspection points, if any of them works improperly, the operator of the cross docking should be notified to take that certain used product out for a further inspection. Such interruption highly affects the working efficiency of a cross docking station within a remanufacturing process and thus the reliability of the entire RFID system should be enhanced as much as possible by taking various constraints into account.
4 Mathematical Modelling
As it can be seen, our focal problem falls under the category of RFID operational level research. Nevertheless, according to our recent review [44], literature provides little guidance in addressing this issue. Therefore, in this chapter, we propose to model our focal problem as a reliabilityredundancy allocation problem (RRAP).
4.1 ReliabilityRedundancy Allocation Problem
General form of RRAP problem
General Form of RRAP Problem  

Maximize  
R _{ s } = f(r, n)  
Subject to  
\( \begin{array}{*{20}c} {g_{j} \left( {{\mathbf{r}},{\mathbf{n}}} \right) \le l_{j} ,} & {j = 1,2, \cdots ,m} \\ \end{array} \)  
where:  
R _{ s }  the reliability of a system 
r = (r _{1}, r _{2}, ···, r _{ d })  the vector of the component reliabilities for the system 
r _{ i }:  the reliability of the ith subsystem 
n = (n _{1}, n _{2}, ···, n _{ d })  the vector of redundancy allocation for the system 
n _{ i }  the number of components in the ith subsystem 
f( · )  the objective function for the overall system reliability 
g(r, n)  the set of constraint functions 
g _{ j }(r, n)  the jth constraint function 
d  the number of subsystems in the system 
l  the vector of resource limitations 
The goal of RRAP problem is to find the optimal combination of components and the reliabilities of the components to achieve the highest system reliability [41].
4.2 4Stage Series System
5 Proposed Methodology
Many classical mathematical methods have failed to address the nonconvexities and nonsmoothness in RRAP problems. As an alternative to the classical optimization approaches, the CI approaches have been given much attention by many researchers because of their superior capability in finding an almost global optimal solution. In this research, we choose teaching—learningbased optimization (TLBO) and firefly algorithm (FA) as a vehicle to address our 4stage series system problem.
5.1 Background of TLBO
Teaching—earningbased optimization (TLBO) is a new efficient population based algorithm inspired by the influence of a teacher on the output of learners in a class, which learners first acquire knowledge from a teacher (i.e., teacher phase) and then from classmates (i.e., learner phase) [35]. In principle, population consists of learners in a class and design variables are courses offered. The output in TLBO algorithm is considered in terms of results or grades of the learners which depend on the quality of teacher. That means, a high quality teacher is usually considered as a highly learned person who trains learners so that they can have better results in terms of their marks or grades. Moreover, learners also learn from the interaction among themselves which also helps in improving their results. Working of both the phase is explained below.
In many aspects, TLBO resembles evolutionary algorithms [33] such as an initial population is randomly generated; moving/learning towards teacher and classmates can be regarded as a special mutation operator; and selection is deterministic (i.e., two solutions are compared and the better one always survives) [11 ]. The TLBO algorithm has been used in solving many problems, remarkable results have been reported about TLBO outperforming many algorithms such as differential evolution [39], evolutionary strategies [6], and particle swarm optimization [26].
5.2 Background of FA

Fireflies are unisex so that one firefly will be attracted to other fireflies regardless of their sex, that means no mutation operation will be done to alter the attractiveness fireflies have for each other;

The sharing of information or food between the fireflies is proportional to the attractiveness that increases with a decreasing Cartesian or Euclidean distance between them due to the fact that the air absorbs light. Thus for any two flashing fireflies, the less bright one will move towards the brighter one. If there is no brighter one than a particular firefly, it will move randomly;

The brightness of a firefly is determined by the landscape of the objective function. For the maximization problems, the light intensity is proportional to the value of the objective function.
Furthermore, there are two important issues in the FA that are the variation of light intensity or brightness and formulation of attractiveness. Yang [45] simplifies the attractiveness β of a firefly is determined by its brightness I which in turn is associated with the encoded objective function. As light intensity and thus attractiveness decreases as their distance from the source increases, the variations of light intensity and attractiveness should be monotonically decreasing functions.
 Variation of Light Intensity: Suppose that there exists a swarm of n fireflies, and x _{ i }, i = 1, 2, …, n represents a solution for a firefly i initially positioned randomly in the space, whereas f(x _{ i }) denotes its fitness value. In the simplest form, the light intensity I(r) varies with the distance r monotonically and exponentially. That is determined through Eq. (6) [45, 46, 47]:where I _{0} is the original light intensity, γ is the light absorption coefficient, and r is the distance between firefly i and firefly j at x _{ i } and x _{ j } as Cartesian distance \( r_{ij} = \left\ {x_{i}  x_{j} } \right\ = \sqrt {\sum\limits_{k = 1}^{d} {\left( {x_{i,k}  x_{j,k} } \right)^{2} } } \)or the ℓ_{2}  norm, where x _{ i,k } is the kth component of the spatial coordinate x _{ i } of the ith firefly and d is the number of dimensions we have, for d = 2, we have \( r_{ij} = \sqrt {\left( {x_{i}  x_{j} } \right)^{2} + \left( {y_{i}  y_{j} } \right)^{2} } \).$$ I = I_{0} e^{{  \gamma r_{ij} }} $$(6)
 Movement toward attractive Firefly: A firefly attractiveness is proportional to the light intensity seen by adjacent fireflies [45]. Each firefly has its distinctive attractiveness β which implies how strong it attracts other members of the swarm. However, the attractiveness is relative; it will vary with the distance between two fireflies. The attractiveness function β(r) of the firefly is determined via Eq. (7) [45, 46, 47]:where, β _{0} is the attractiveness at r = 0, and γ is the light absorption coefficient which controls the decrease of the light intensity.$$ \beta = \beta_{0} e^{{  \gamma r_{ij}^{2} }} $$(7)
 The movement of a firefly i at location x _{ i } attracted to another more attractive (brighter) firefly j at location x _{ j } is determined based on Eq. (8) [45, 46, 47]:where, the first term is the current position of a firefly, the second term is used for considering a firefly’s attractiveness to light intensity seen by adjacent fireflies, and the third term is randomization with the vector of random variables ɛ _{ i } being drawn from a Gaussian distribution, in case there are not any brighter ones. The coefficient α is a randomization parameter determined by the problem of interest.$$ x_{i} \left( {t + 1} \right) = x_{i} \left( t \right) + \beta_{0} e^{{  \gamma r_{ij}^{2} }} \left( {x_{j}  x_{i} } \right) + \alpha \varepsilon_{i} $$(8)

Special Cases: From Eq. (8), it is easy to see that there exit two limit cases when γ is small or large, respectively [45, 46, 47]. When γ tends to zero, the attractiveness and brightness are constant β = β _{0} which means the light intensity does not decrease as the distance r between two fireflies increases. Therefore, a firefly can be seen by all other fireflies, a single local or global optimum can be easily reached. This limiting case corresponds to the standard particle swarm optimization algorithm. On the other hand, when γ is very large, then the attractiveness (and thus brightness) decreases dramatically, and all fireflies are shortsighted or equivalently fly in a deep foggy sky. This means that all fireflies move almost randomly, which corresponds to a random search technique.
In general, the FA corresponds to the situation between these two limit cases, and it is thus possible to finetune these parameters, so that FA can find the global optima as well as all the local optima simultaneously in a very effective manner. A further advantage of FA is that different fireflies will work almost independently, it is thus particular suitable for parallel implementation. It is even better than genetic algorithm and particle swarm optimization because fireflies aggregate more closely around each optimum. It can be anticipated that the interactions between different subregions are minimal in parallel implementation. Nowadays, mechanisms of firefly communication via luminescent flashes and their synchronization has been used effectively to solve the problems in various areas, such as in continuous constrained optimization [32], economic emissions load dispatch [3], image compression [18, 20], mixed variable structural optimisation [15], remachining parameter optimization [43], scheduling [36], clustering [19, 37], parameter tuning [48], wireless network design [31], dynamic marketing pricing [22].
5.3 Benchmark Test Function
Benchmark test function (adapted from [35])
Benchmark test function  

Minimize:  
\( \begin{array}{*{20}c} {f\left( {\mathbf{x}} \right) \, = } \hfill & {\left( {x_{1}  10} \right)^{2} \,+\, 5\left( {x_{2}  12} \right)^{2} \,+\, x_{3}^{4} + 3\left( {x_{4}  11} \right)^{2} } \hfill \\ {} \hfill & { + 10x_{5}^{6} + 7x_{6}^{2} + x_{7}^{4}  4x_{6} x_{7}  10x_{6}  8x_{7} } \hfill \\ \end{array} \)  
Subject to:  
\( \begin{aligned} g_{1} \left( {\mathbf{x}} \right) &=  127 + 2x_{1}^{2} + 3x_{2}^{4} + x_{3} + 4x_{4}^{2} + 5x_{5} \le 0 \hfill \\ g_{2} \left( {\mathbf{x}} \right) &=  282 + 7x_{1} + 3x_{2} + 10x_{3}^{2} + x_{4}  x_{5} \le 0 \hfill \\ g_{3} \left( {\mathbf{x}} \right) &=  196 + 23x_{1} + x_{2}^{2} + 6x_{6}^{2}  8x_{7} \le 0 \hfill \\ g_{4} \left( {\mathbf{x}} \right) &= 4x_{1}^{2} + x_{2}^{2}  3x_{1} x_{2} + 2x_{3}^{2} + 5x_{6}  11x_{7} \le 0 \hfill \\ \end{aligned} \)  
where:  
\(  10 \le x_{i} \le 10\,(i = 1,2, \ldots 7) \) 

TLBO: Population size is 50, generations are 2,000, total number of function evaluations are 100,000;

FA: Population size is 20, generations are 5,000, total number of function evaluations are 100,000.
Comparison of results (10 runs) obtained by using FA and TLBO for Benchmark test function
Benchmark test function  

FA  TLBO  
f(x ^{*})—best  680.7249  680.6305 
f(x ^{*})—worst  681.1270  680.6325 
f(x ^{*})—mean  680.8900  680.6318 
x ^{*}—best  \( \left( {\begin{array}{*{20}c} {2.349317} \\ {1.948980} \\ {  0.442630} \\ {4.366729} \\ {  0.638468} \\ {1.027457} \\ {1.614927} \\ \end{array} } \right) \)  \( \left( {\begin{array}{*{20}c} {2.331588} \\ {1.951348} \\ {  0.477926} \\ {4.365752} \\ {  0.626203} \\ {1.032656} \\ {1.593086} \\ \end{array} } \right) \) 
The optimum solution is at x ^{*} = (2.330499, 1.951372, −0.4775414, 4.365726, −0.6244870, 1.1038131, 1.594227) with objective function value f(x ^{*}) = 680.6300573 [35]. Although, we only test ten runs on each method, it can be observed from Table 3, both FA and TLBO work fine on solving benchmark test function.
5.4 Benchmark Engineering Design Optimization Problem
Benchmark engineering design problem (adapted from [35])
Benchmark engineering design problem  

Minimize  
\( \begin{aligned} f(x)=&\;0.6224{d_{1}}rL+1.7781{d_{2}}{R^{2}}\\&+3.1661\,{D_{I}^{2}}L+19.84{d_{I}^{2}}r \end{aligned} \)  
Subject to  
\( \begin{aligned} g_{1} \left( {\mathbf{x}} \right) &=  d_{1} + 0.0193r \le 0 \hfill \\ g_{2} \left( {\mathbf{x}} \right) &=  d_{2} + 0.00954r \le 0 \hfill \\ g_{3} \left( {\mathbf{x}} \right) &=  \pi r^{2} L  \frac{4}{3}\pi r^{3} + 1296000 \le 0 \hfill \\ g_{4} \left( {\mathbf{x}} \right) &= L  240 \le 0 \hfill \\ \end{aligned} \)  
where  
\( \begin{array}{*{20}c} {0 \le d_{1} \le 99} \hfill & {0 \le d_{2} \le 99} \hfill \\ {10 \le r \le 200} \hfill & {10 \le L \le 200} \hfill \\ \end{array} \) 
Comparison of results (10 runs) obtained by using FA and TLBO for Benchmark engineering design problem
Benchmark engineering design problem  

FA  TLBO  
f(x ^{*})—best  6117.2432  5885.3330 
f(x ^{*})—worst  6418.8358  5885.3890 
f(x ^{*})—mean  6263.9090  5885.3453 
x ^{*}—Best  \( \left( {\begin{array}{*{20}l} {0.828478} \\ {0.425984} \\ {42.619529} \\ {172.301498} \\ \end{array} } \right) \)  \( \left( {\begin{array}{*{20}l} {0.778169} \\ {0.384649} \\ {40.319619} \\ {200} \\ \end{array} } \right) \) 
As it shown in Table 5, according to other similar studies such as [9], both solutions obtained through TLBO and FA are reasonable in which TLBO can generate a slightly better results than FA on the test problem.
From the above mentioned two examples, we can see that TLBO and FA are very good optimizer and suitable for many applications.
6 Experimental Study by Using TLBO and FA
Data used for RFID system
Stage  10 ^{ 5 } α _{ i }  β _{ i }  v _{ i }  w _{ i }  V  C  W  T 

1  1.0  1.5  1  6  200  500  450  2000 
2  2.3  1.5  2  6  –  –  –  – 
3  0.3  1.5  3  8  –  –  –  – 
4  2.3  1.5  2  7  –  –  –  – 
Convergence results (10 runs) obtained by using FA and TLBO
Convergence results of f(r, n)  

FA  TLBO  
f(r, n)—best  99.6152  99.6156 
f(r, n)—worst  99.6118  99.6153 
f(r, n)—mean  99.6139  99.6155 
Best results (10 runs) obtained by using FA and TLBO for RFID system
RFID system  

FA  TLBO  
f ( r , n )  99.6152  99.6156 
n _{ 1 }  5  5 
n _{ 2 }  5  5 
n _{ 3 }  4  4 
n _{ 4 }  5  5 
r _{ 1 }  72.0518  72.4646 
r _{ 2 }  74.0182  73.9552 
r _{ 3 }  82.1285  81.9053 
r _{ 4 }  74.8874  74.7276 
From Table 8, we can see that in order to keep our RFID system reliability at the highest level, the components number and the corresponding reliability should be designed based on the results obtained via TLBO and FA.
7 Future Research Directions
Since our targeted question belongs to a class of constrained nonlinear mixedinteger programming problem which means the solution of this kind of problems consists two parts, i.e., a real part and an integer part. As the searching space and complexity of these two parts are different, it might be more promising to use different searching mechanisms to obtain individual optimal solution for each of these two parts. Therefore one possible future research direction is to employ two algorithms to search the real part and the integer part, respectively.
In order to maximize a system’s reliability, except the reliabilityredundancy allocation solution, one can also consider other options such as enhancing the component reliability [30]. Since there are many other communication systems may utilize the similar frequencies within the communication range of RFID which in turn could interfere the reliability of the RFID system, a good filter design for RFID receiver is always a necessary. Therefore an immediate extension of the current research would be employing suitable CI methods to optimize the performance of signal filter component within a RFID reader.
8 Conclusions
Remanufacturability classification based on radio frequency identification (RFID) system is a great concept transition and innovation. The idea is to “take the initiative to prevent problems”, which can greatly save resources and energy of the whole world and bring enormous economic benefits as well as social benefits. However, recently the growing interest in cyber physical remanufacturability preevaluation faces major challenges due to the error prone nature of RFID devices. The focus of our work is complementary to the inherent unreliability of RFID systems, and ask whether the reliability can be improved using more redundant components (i.e., RFID readers) in wide range type series. In this chapter, we first formulate our focal scenario as a reliabilityredundancy allocation problem (RRAP). Then, two of the recently developed computational intelligence approaches called teaching—learningbased optimization (TLBO) which is based on the effect of the influence of a teacher on the output of learners in a class, and firefly algorithm (FA) which is based on the social (flashing) behaviour of fireflies, or lighting bugs, in the summer sky in the tropical temperature regions, are employed to address our focal problem. Simulation results suggest that the proposed TLBO and FA are viable optimization techniques in improving the RFID classification system’s reliability.
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