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Improved BABC Algorithm for Matching of Remanufacturing Service Resource Module
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51st CIRP Conference on Manufacturing Systems
Improved BABC Algorithm for Matching of Remanufacturing 28th CIRP Design Conference, May 2018, Nantes, France Service Resource Module
A new methodology to analyze the functional and physical architecture of a, , Xu-Hui Xiaoriented *, Jian-Huaproduct Caoa, Xiang Liub existing productsLei forWang ana,bassembly family identification a
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China b Center for Service Science and Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. E-mail address: [email protected] École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
Abstract
Remanufacturing service is a new industrial pattern integrated remanufacturing and service, as well as a new advanced manufacturing mode. In Abstract order to solve the difficulty in module matching caused by large variety and quantity of remanufacturing service resources, this paper presents the concept of remanufacturing service, and then describes the remanufacturing resource module. A matching model for remanufacturing Inservice today’s business environment, the trendbased towards more product variety andArtificial customization is unbroken. Due(IBABC). to this development, of resource module was constructed on Improved Bi-population Bee Colony Algorithm Then, basedthe onneed service agile and period, reconfigurable production emerged to cope with various products and product families. design types and optimize production delivery service quality, and systems service cost as multiple objectives, multiple modules were selected fromTodifferent of remanufacturing systems well asmodules to choose optimal product matches, product needed.was Indeed, most of the known aim to service as resource forthe combination-matching, and the Paretoanalysis solutionmethods set withareIBABC solved. Subsequently, themethods final matching analyze product or one product on the physical of level. productset. families, however, may differ largelywas in terms of theto number schemea was determined throughfamily combined-weighted theDifferent Pareto solution Finally, an application example proposed verifyand the nature of components. Thisoffact efficient comparison and choiceresource of appropriate product family combinations correctness and feasibility the impedes matchingan model of remanufacturing service module and related solving method. for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly product © 2018 The Authors. Publishedoriented by Elsevier B.V.families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. on Datum Chain, the physicalofstructure the products is analyzed. Functional subassemblies are identified, and Peer-review underBased responsibility of Flow the scientific committee the 51stof CIRP Conference on Manufacturing Systems. a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity product Service familiesresources; by providing support to both,Artificial production systemAlgorithm planners(BABC); and product designers. An illustrative Keywords:between Remanufacturing; Moduledesign matching; Bi-population Bee Colony Chaotic optimization example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 1. Introduction researches from the 2018. angle of Web service composition and Keywords: Assembly; Design method; To realize reasonably and Family fully identification use of remanufacturing
resource and achieve the extension of value chain of remanufacturing enterprises [1, 2], remanufacturing service can be used to meet the requirement of individualized 1.mode Introduction remanufacturing services by referring the service-oriented manufacturing concept [3]. Since remanufacturing services Due to the fast development in the domain of involve large amount of service resources [4], and the service communication and an ongoing trend of digitization and requirement and service objects are highly uncertain, it is digitalization, manufacturing enterprises are facing important necessary to seek an effective analysis method. challenges in today’s market environments: a continuing Modularization is a dynamic process, which involves tendency towards reduction of product development times and decomposing system into multiple separated components, shortened product lifecycles. In addition, there is an increasing mixing, matching and integrating the components without demand of customization, being at the same time in a global damaging system function [5]. Moreover, it is also an competition with competitors all over the world. This trend, effective method to solve complex system problems and which is inducing the development from macro to micro improve system efficiency [6]. Optimal matching of service markets, results in diminished lot sizes due to augmenting module plays a key role in realizing remanufacturing services product varieties (high-volume to low-volume production) [1]. combination. Currently, scholars have conducted related To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing 2212-8271 ©system, 2018 The it Authors. Publishedtobyhave Elsevier B.V. knowledge production is important a precise
dynamic matching of service system. For example, in the aspect of Web service composition, Wen JH proposed a taskoriented service discovery algorithm to realize self-adaption and high efficient Web service composition [7]. Regarding the Web problem without WSDL and/or (Web of the service product composition range and characteristics manufactured Services Description Language) document, Pan WF proposed assembled in this system. In this context, the main challenge in a service composition recommendation method based on modelling and analysis is now not only to cope with single service network [8]. Web service composition is a process of products, a limited product range or existing product families, data exchanging or integration, which focuses on realizing but also to be able to analyze and to compare products to define data exchanging or integration of Web service network new product families. It can be observed that classical existing module using service computation/programming technique product families are regrouped in function of clients or features. with customer's requirement as matching purpose. In the However, assembly oriented product families are hardly to find. aspect of dynamic matching of service system, Pan HX with On the product family level, products differ mainly in two regard to the service matching problems involved in multimain characteristics: (i) the number of components and (ii) the stage service process, constructed a multi-stage dynamic type of components (e.g. mechanical, electrical, electronical). service matching model, which can perform multi-stage Classical methodologies considering mainly single products overall optimization using dynamic programming algorithm or solitary, already existing product families analyze the [9]. By regarding service supply as an object and customer's product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems.
2212-8271©©2017 2018The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 51stDesign CIRP Conference Conference2018. on Manufacturing Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2018.03.029
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Lei Wang et al. / Procedia CIRP 72 (2018) 1368–1373 Lei Wang, et al. / Procedia CIRP 00 (2018) 000–000
requirement as a matching goal, optimization algorithm can be used so solve model, so that, an optimal combination of service activity and resource can be realized. As a dynamic matching problem of service system, matching of remanufacturing service resource module is a typical multiobjective optimization problem [10]. By referring related researches on service matching, this paper proposes a matching method of remanufacturing service resource module based on Improved Bi-population Artificial Bee Colony Algorithm (IBABC), providing a referential thought and approach for remanufacturing service research.
service requirement
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Remanufacturing service platform
requirement knowledge
services requester’s retired products RM product
Knowledge of product history parts manufacturer
services requester RM product
Technical knowledge、 service programme
Distributors (again)
parts
RM design service
…… Assembly service
Remanufact urability assessment Cleaning service
Core remanufact urer
Dismantling service provider
Classifica -tion service provider
processing service
Recycling service provider
RMS provider
Scrap processing service provider
customer
retired products
Inventory/transport service provider
Fig.1. Concept model of RMS
Nomenclature Rc number of RMSRMa classes na number of RMSRMai belonging to RMSRMa n number of RMSRMai ωc weight of optimization objective TIME service-delivery time for completing the total process timeai service-delivery time of RMSRMai QUAL service quality presented by RMSRMai combination qualai service quality of RMSRMai, COST service cost presented by RMSRMai combination costai all kinds of cost in the service process of RMSRM rai the price aiof provided amount of service qai the provided amount of service Ns total number of bees Ne number of employed bee No number of onlooker bee D the dimensions of the individual vectors fi the fitness of Xi Pi the selective probability of Xi ci the score of fi Xμ the abandoned solution 2. Remanufacturing Services and Resource Module 2.1. Remanufacturing service ReManufacturing services (RMS) can be interpreted as: customer-facing-service of remanufacturing enterprise cluster based on RMS integration platform as core. In other word, through remanufacturing servitization, RMS provides remanufacturing enterprises with customer related valueadded activities during the operation process on the whole industrial chain. In the implementation process of remanufacturing, remanufacturing production-oriented professional services (remanufacturing recycling, remanufacturing assessment decision, remanufacturing design, remanufacturing processing, and information-based remanufacturing) should be given to damaged or retired products based on productive service. This is a distributed service by integration of resources as well as an integrated service method to increase remanufacturing value [11]. The concept model of RMS shown in Fig.1.
2.2. Remanufacturing services resource module ReManufacturing service resource module (RMSRM) refers to the overall function unit, which plays a basis role for function implementation of remanufacturing service activity [12]. RMSRM is an indivisible unit. A module can be either one single service resource or the combination of multiple service resources. All resources included in each service resource module are corresponding to the only matched service provider. 3. Matching Model of Remanufacturing Service Resource Module 3.1. Model Assumption RMS system contains Rc classes of RMSRMa, and each RMSRMa (a=1,2, …, Rc) contains na RMSRMai (i=1,2, …, na). Therefore, RMS system contains totally
∑
RMSRMai. RMSRMai in each unique class is responsible for realizing service activity in each unique stage. Single matching can only be completed by selecting one module from RMSRMai in the same class, and then combining it with the module from another class. Each RMSRMai is provided exclusively by single service provider (SP). In RMS process multiple RMSRMai are successively realized in serial mode. 3.2. Multi-objective optimization model The goal of RMSRM matching is to realize the shortest Service-delivery time (time), lowest Service Cost (cost), and best Service Quality (qual) achieved by RMSMai combination under service capability (cap). Therefore, the performance expectation index of RMSRMai matchi g is O(TIME, QUAL’, COST), user preference weight of each objective is ωc={ωTIME, ωQUAL, ωCOST}. The multi-objective optimization model can be constructed as equation (1).
Lei Wang et al. / Procedia CIRP 72 (2018) 1368–1373 Lei Wang, et al. / Procedia CIRP 00 (2018) 000–000
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opt
O(TIM E, QUAL' , COST)
RM SRMai (0,1) (qualai ) QUALO a timeai TIM EO a i s.t costai COSTO a i qai capai a 1,2, , Rc ; i 1,2, , n
(1)
where, TIME is the Service-delivery time for completing . remanufacturing service process, TIME ∑ ∑ timeai is the service-delivery time of RMSRMai, which also means the time period from accepting service request to completing service delivery. QUAL’ -QUAL is the transformation form for QUAL as the minimum optimization problem objective. QUAL is the s ervice quality presented by RMSRMai combination in complet , indic ing RMS process under serial mode, QUAL ∏ ating only when all RMSRMai service qualities are 1, can total service quality be 1. If there is a random RMSRMai suffering service failure (qualai =0), the RMS will be failed, and thus tot al service quality will be 0.
qualai, as the service quality of RMSRMai, is the ratio of service performance and expected service performance perceived by service requester. Suppose such ratio can be represented by any number from 0-1, therefore we ca get 0≤ qualai ≤ . COST is the service cost presented by RMSRMai . combination in completing RMS process, O T ∑ ∑ costai refers to all kinds of cost and expenditure in the process of providing service by RMSRMai, costai=rai·qai, in which, rai is the price of provided amount of service, qai is the provided amount of service, which acts as the standard unit of metered services, and its dimension varies upon the class of service. TIMEO, QUALO, and COSTO represent the given target values of service-delivery time, service quality, and service cost, respectively. Constraint condition RMSRMai ∈ (0,1) represents the selection of RMSRM. If RMSRMai is selected, RMSRMai=1, otherwise, RMSRMai=0.
3
All of these advantages make them become a hot spot since they were proposed. Among these swarm intelligence algorithms, ABC is an outstanding one because of its simple operation, less controlling parameters, fast evolution, accurate searching result, strong robustness and easiness of implementation [16]. ABC can not only solve continuous optimization problems and combinational optimization problems, but also has a more superior performance in solve some complex problems than GA, ACO and PSO. Therefore, a multi-target artificial bee colony optimization solution is proposed based on ABC in this study. Assume that the total number of bees is Ns, the population of the employed bee is Ne and the population of the onlooker bee is No. The dimension of the individual vectors is D, S=RD is the search space. If Xi∈S (i≤Ne) contains Ne individuals, then X=(X1, X2, …, XNe) represents a bee population. X(0) represents the initial bee population, X(n) represents the n-th generation. f : S R represents the fitness function fi (i=1, 2,…, Ne), then the ABC algorithm flow can be shown in the Fig.2 and the steps are as follows: Step1: Initialization. Set n=0, Ns feasible solutions (X1, X2, …, XNe) are randomly generated, and Xi can be calculated by j j j X ij X min rand (0,1)( X max X min )
(2)
where j is the j-th dimension of Xi and j∈{1, 2, …, D}. The fitness function value of each feasible solution is calculated and sorted. The top Ne individuals are the initial employment bee population X(0), and the rest are onlooker bee population. Step 2: Employee bee process. Make every individual of the n-th generation employment bee population X(n) search for a new location near its current location
new _ X i j X i j + *( X i j X kj )
(3)
where i, k∈{1, 2, …, Ne} and k≠i, k and j are randomly generated, α is a random number between -1 and 1, . Then the greedy selection method is adopted to select the better individuals between A and B, and the better one is retained to the next generation. Step3: Onlooker bee process. Each Onlooker bee chooses an employee bee according to the selective probability Pi. Then, new locations are searched by formula (3), while k∈ {Ne+1, Ne+2, …,Ns}.
4. Optimization solution 4.1. Artificial bee colony algorithm (ABC) Regarding the multi-objective optimization problems such as the matching of remanufacturing service resource module, current common intelligent algorithms include particle swarm optimization (PSO) [13], ant colony optimization (ACO) [14], artificial bee colony algorithm (ABC), and genetic algorithm (GA) [15], etc. All these swarm intelligence algorithms have low requirement of function information, evolutionary process that is independent from initial value, and fast searching speed.
⁄∑
(
where fi is the fitness of the Xi。 Step4: Scout bee process. When a bee has not found a better position after Limit times, a feasible solution is randomly initialized and the position of employee bee will replaced, then the employee bee turns into a scout bee. Step5: If the stop condition is satisfied, stop the calculation and output the optimal results. Otherwise, go to Step 2.
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Lei Wang et al. / Procedia CIRP 72 (2018) 1368–1373 Lei Wang, et al. / Procedia CIRP 00 (2018) 000–000
Start
rand Pi
Initialize Record the number of searchable solutions, the optimal function value, and the optimal individual
[ 1,1], i k
n=1
calculate the fitness fi
i 1
Y
V ij X ij * ( X ij X kj )
N
calculate the fitness f i
Y
Update the number of searchable solutions, the optimal function value, and the optimal individual
N
i i 1
Y
Calculate the observed bee selection probability
n n1 Max(Times) limit
N
Y
Randomly generate an alternative solution
Y
nN
Y End
i 1,t 0
For sub-population B, a tournament selection strategy based on local competition mechanism is adopted, i.e., let onlooker bee randomly select q individuals from the population for comparison, and then keep the individual with larger fitness value, wherein q represents the scale of the competition. When q=2, the selection probability Pi is determined after comparing the fitness value fi of two individuals. In this tournament selection strategy, the fitness value is regarded as the relative standard, rather than the absolute standard, so that premature convergence can be avoided in certain degree.
i Size( S )
Update the number of searchable solutions, the optimal function value, and the optimal individual
f i f i 1
i Size ( S )
N
Y i1
i i 1
f i f i 1
f i f i 1
f i f i 1
[ 1,1], i k
N
N
Y V ij X ij * ( X ij X kj )
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Fig.2. The flow of ABC
4.2. Improved Bi-population Artificial Bee Colony Algorithm (IBABC) To improve the global searching ability of ABC algorithm and accelerate its convergence speed, an improved Bipopulation artificial bee colony algorithm (IBABC) based on chaos optimization was proposed. Every other certain number of generations, the information between the two populations will be exchanged according to information interaction mechanism. When a certain solution is trapped in local optimum, chaos optimization is used to generate a new solution for jumping out of local optimum. The optimal solution set of above multi-target Pareto can be obtained using IBABC, and concrete steps are shown as follow: Step1: Population initialization. The initial population includes Ns solutions that are corresponding to optimization variables. Each solution Xi (i ∈ {1, 2, … , Ne}) is a D dimensional vector. The initial population is randomly divided into two equal-sized sub-populations: subpopulation A and subpopulation B. The fitness value fi of each solution is calculated. Step2: Determination of selection strategy. The selection strategy of ABC algorithm is applied for subpopulation A, i.e., let onlooker bee select the Xi with larger yield rate as new solution according to the selection probability calculated by equation (4). With such selection strategy, it is easy for us to select the good individual (individual with larger fitness value) and eliminate the bad one. However, with the evolution of population, the fitness values of all individuals will converge, making the selected good individual not so advantageous and the algorithm trapped in local optimum.
⁄∑
(
where, ci is the score of fi. If fi is the better one of the two individuals, then ci=1. Step3: Information interaction. For every other certain number of generations, information exchange between the two sub-populations will be carried out. By comparing the fitness value of optimal individual between population A and B, the optimal individual with larger fitness from one population can be determined, and then used to substitute for the worst individual from the other population, so that the information exchange is completed. Step4: Employee bee process. For both sub-population A and B, the fitness value of new solution is calculated according to equation (3). If the fitness degree of the new solution is better than that of original solution, then the original solution will be replaced by new solution; otherwise, keep the original solution. Step5: Onlooker bee process. The probability Pi of subpopulation A and B is calculated according to equation (4) and (5), respectively. Then, the solution of population A and B is determined according the Pi value. Moreover, field searching of equation (3) is conducted to generate new solution, and the fitness value of the new solution is calculated. If the fitness degree of new solution is better than that of original solution, then the original solution will be replaced by new solution; otherwise; keep the original solution. Step6: Chaos optimization of local optimal solution. For sub-population A and B, when a certain solution is still not improved after m cycles, we can determine that it is trapped in local optimum. For such situation, we should abandon this solution and generate a new one using the ergodicity of chaos for substitution and thus increase the convergence speed of algorithm. Suppose the abandoned solution Xμ=[Xμ1, Xμ2, …, XμD], aμ ≤ Xμr ≤ bμ, r ∈ {1, 2, … , D}, and chaos optimization of solution Xμ is performed: The Xμ is mapped into the definitional domain [0,1] of Logistic equation, i.e., 0μ 0μ , 0μ , , 0μ . Where 0μ ⁄ μ , r∈{1, 2, …, D},μ∈{1, 2, …, Ns}. μ μ μ Iteration is performed according to Logistic equation , ( is control parameters) which results in chaos sequence μ ，t∈{1, 2, …,T}，T is the number of chaos searchings.
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The generated chaos variable μ is returned to original solution space via inverse mapping μ μ μ μ . The fitness value of μ is μ μ μ μ calculated and compared with original solution, and the good one is kept. If the fitness degree of new solution is inferior to that of original solution, then return to previous step of chaos optimization for re-iteration and generating chaos sequence. This step is repeated till reaching maximum number of chaos T and then finishing the chaos optimization. Otherwise turn to previous step for reiteration and generating new chaos sequence. Step7: Optimal solution recording and termination judgement. The best ever solution is recorded. Judging whether the termination condition is met, if met, then the algorithm is finished, otherwise turn to Step2. 4.3. Pareto optimization based on combinatorial weighting In matching process of remanufacturing service resource module, the three optimization targets (i.e. service response time, cost and quality) are often conflicted with each other, and the simultaneous optimization of the three can hardly be achieved. Only one set of optimization solution can be obtained by IBABC, so it is impossible to compare Pareto solution set easily. To realize RMSRM matching, it is needed to select a satisfying optimal solution from Pareto solution set obtained by IBABC algorithm [17]. Therefore, Pareto optimal solution set can be further regarded as a matching scheme set of RMSRM, and then applied with normalization processing to obtain the standard decision matrix B=(bij)n×m with n schemes and m attributes. After that, combinational weighted calculation is performed according to the given objective weight ωc={ωTIME, ωQUAL, ωCOST}, so as to realize the final Pareto optimal decision.
5
RMSRM4i. The cost for recycling/ transportation/ processing a single working roll by RMSRMai is costai yuan/ton, the maximum capacity for recycling/transportation/processing working roll is capai ton/d, and service quality score is qualai. Among them, service quality is evaluated by RMS platform based on the experience data. The data of all RMSRMai such as price, service capacity, and service quality on decision making day is shown in Table 1. Table 1. The data of all RMSRMai RMSRM1 RMSRM11 RMSRM12 RMSRM13 … RMSRM3 RMSRM31 RMSRM32 RMSRM33 …
RMS recycling cost 1800 1770 1850 …
cap 130 100 110 …
qual 0.8 0.85 0.92 …
RMS processing cost 700 680 500 …
cap 30 35 27 …
qual 0.94 0.8 0.81 …
RMSRM2 RMSRM21 RMSRM22 RMSRM23 … RMSRM4 RMSRM41 RMSRM42 RMSRM43 …
RMS decisionmaking & design cost cap qual 200 138 0.95 210 150 0.87 180 110 0.92 … … … RMS logistics cost 320 300 280 …
cap 450 380 350 …
qual 0.88 0.85 0.93 …
Suppose the service provided by RMSRMai is sai, its service quantity is qai, the multi-objective optimization model for services resource module matching is established. The IBABC algorithm is programmed using Matlab 2014a, where the parameter setting for IBABC is： popsize =200, i.e., the size of sub-population A and B is 100, respectively; D=50, maximum number of iterations N=300. In Logistic, ,T ＝ 50. The initial population is generated by random distribution, and the Pareto solution set is obtained as shown in Fig.3.
5. Illustrative Example Assumption of service requirement: CSP plant of a certain steel enterprise needs to remanufacture 13 seriously damaged working rolls (Totally weigh 91.19 tons) with half price of new working rolls as total budget, i.e. means 312 thousand yuan. According to the contract, the remanufactured products shall be delivered to site A of CSP plant within 10 days after the execution of contract, and the total service quality should be maintained at no less than 0.5 (service quality reaches medium level or higher). 5.1. Multi-objective optimization solution Currently, RMS platform contains Rc=4 (a=1,2,3,4) classes and totally 60 RMSRMai including remanufacturing recycling service class accounting for 20 RMSRM 1i, remanufacturable decision and redesignable service class accounting for 10 RMSRM2i, remanufacturing processing service class accounting for 10 RMSRM3i, and remanufacturing transportation service class accounting for 20
Fig.3. RMSRM matched 3-objective Pareto solution set
According to the Pareto solution set, it can be known that the three objectives (service delivery time, service quality, and service cost) are mutual-contradictory. The improvement of one objective is realized at the expense of another objective’s performance. Based on the objective weight of user preference ωc={0.3,0.4,0.3}, the RMSRM matching scheme can be performed by realizing weighted combination of solution set in Pareto solution, and thus the final weighted Pareto solution can be obtained. The Pareto weighted value is determined to be 0.2993, which is obtained by normalizing the satisfying optimal Pareto solution in Pareto frontier. In addition, the corresponding optimal scheme is: RMSRM 119, RMSRM28, RMSRM36, RMSRM49, providing service by 70%
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of total enterprise capacity. The obtained optimal Pareto solution is: 6.452 days as total service delivery time, 27.5842 thousand as total cost and 0.832 as service quality. 5.2. Algorithm performance analysis Using Matlab 2014a, the programming of ABC and NSGA-II is conducted respectively, where the popsize for ABC is 200, other parameters are the same with that of IBABC. The popsize for NSGA-II algorithm is 200, the maximum number of generations (genmax) is 50, the probability of crossover (PC) is 0.8, the probability of mutation (PM) is 0.3. By randomly operating 50 times, the mean value, mean square error, maximum value and minimum value can be obtained, as shown in Table 2. Table 2. Calculation results by different optimization algorithms Optimization algorithms
Mean value
Meansquare deviation
Maximum value
Minimum value
Average calculating time /s
IBABC
0.3030
0.0048
0.3113
0.2993
205.8724
ABC
0.3090
0.0072
0.3190
0.3017
215.7669
NSGA-II
0.3060
0.0066
0.3148
0.2996
259.2494
Table 2 shows that IBABC algorithm is significantly better than ABC and NSGA-II in terms of solution accuracy, result stability, arithmetic and convergence speed. ABC algorithm has faster arithmetic speed, but poorer stability than NSGA-II algorithm. 6. Conclusions This paper proposed the concept of remanufacturing services and defined the RMSRM. By regarding service delivery time, service quality, and service cost as objectives, RMSRM matching multi-objective optimization model was established, which can avoids the one-sided problem in single objective optimization. On the basis of ABC algorithm, a chaos optimization based IBABC was proposed to optimize the solution of multiobjective model. IBABC algorithm adopts bi-population structure to realize parallel operation, which enables subpopulation to have different selection strategies. Therefore, IBABC can effectively maintain population diversity while accelerating convergence rate. When the algorithm is trapped in local optimum, new solution can be generated using the ergodicity of chaotic thought, allowing the algorithm to have sound global searching and thus jump out of local optimum. Taking remanufacturing of working rolls in certain steel enterprise as an example, the feasibility and practicability of the proposed method were verified. The analysis results verified that the optimize performance and the robustness of the IBABC algorithm was superior to ABC algorithm and NSGA-II algorithm. This paper lays a solid foundation for the research on RMS system and the basic theory of resource
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management, and also provides reference for the research of other industries and the RMS application. Acknowledgements This work is supported by the National Natural Science Foundation of China under project No. 71471143. It is also supported by Center for Service Science and Engineering (Wuhan University of Science and Technology) under project No. CSSE2017KA04, and Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resources (Wuhan University of Science and Technology) under project No. 2016zy013. References [1] Wang L, Xia XH, Xiong YQ, et al. Architecture of reverse supply chain service system. Computer Integrated Manufacturing System. 2015; 21(10), 2720-2731. [2] Paul G, Emma R, Jenifer H. A review of the state of the art in tools and techniques used to evaluate remanufacturing feasibility. Journal of Clearner Production. 2014; (81): 1-15. [3] Cheng Y, Tao F, Zhao DM, et al. Modeling of manufacturing service supply–demand matching hypernetwork in service-oriented manufacturing systems. Robotics and Computer-Integrated Manufacturing. 2017; 45:59-72. [4] Deng QW, Liao HL, Xu BW. The Resource Benefits Evaluation Model on Remanufacturing Processes of End-of-Life Construction Machinery under the Uncertainty in Recycling Price[J]. Sustainability, 2017, 9(2): 256. [5] Baldwin CY, Clark KB. Design rules, volume l, the power of modularity. Cambridge MA: MIT Press 2000. [6] Wang PP, Ming XG, Wu ZY, et al. Research on industrial product– service configuration driven by value demands based on ontology modeling. Computers in Industry. 2014; 65(2): 247-257. [7] Wen JH, Jiang Z, Tu LY, et al. Task-oriented web service discovery algorithm using semantic similarity for adaptive service composition. Journal of Southeast University (English Edition). 2009; 25(4): 468-472. [8] Pan WF, Li B, Jiang B, et al. Service composition recommendation based on service networks. Engineering-Theory & Practice. 2014; 34(6): 131142. [9] Pan HX, Wang J. Modeling and Optimization of Multi-Stage Dynamic Service Matchmaking for Service Systems. Industrial Engineering Journal. 2012; 10(15): 112-117. [10] Yu MC, Mark G, Lin HC. Fuzzy multi-objective vendor selection under lean procurement. European Journal of Operational Research. 2012; 219(2, 1): 305-311. [11] Paul P, Stephen LV, Nathan C, et al. The service system is the basic abstraction of service science. Information Systems and e-Business Management. 2009; 7(4): 395-406. [12] Wang L, Xia XH, Xiong YQ, et al. Modular Method of Remanufacturing Service Resources. Computer Integrated Manufacturing System. 2016; 22(9): 2204-2216. [13] Guo JQ, Ya G. Optimal strategies for manufacturing/remanufacturing system with the consideration of recycled products. Computers & Industrial Engineering. 2015; 89: 226-234. [14] Teschemachera U, Reinhart G. Ant Colony Optimization Algorithms to Enable Dynamic Milkrun Logistics. Procedia CIRP. 2017; 63: 762-767. [15] Fu CM, Jiang C, Liu GP, et al. Micro Multi-objective Genetic Algorithm Based on Grid Domination. China Mechanical Engineering. 2015; 26(16): 2208-2214. [16] Ghambari S, Rahati A. An improved artificial bee colony algorithm and its application to reliability optimization problems. Applied Soft Computing. 2018; 62: 736-767. [17] Li N, Wang MH, Ma SG, Li B, et al. Mechanism-parameters Design Method of an Amphibious Transformable Robot Based on Multiobjective Genetic Algorithm. Journal of Mechanical Engineering. 2012; 48(17): 10-20.
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51st CIRP Conference on Manufacturing Systems
Improved BABC Algorithm for Matching of Remanufacturing 28th CIRP Design Conference, May 2018, Nantes, France Service Resource Module
A new methodology to analyze the functional and physical architecture of a, , Xu-Hui Xiaoriented *, Jian-Huaproduct Caoa, Xiang Liub existing productsLei forWang ana,bassembly family identification a
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China b Center for Service Science and Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. E-mail address: [email protected] École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
Abstract
Remanufacturing service is a new industrial pattern integrated remanufacturing and service, as well as a new advanced manufacturing mode. In Abstract order to solve the difficulty in module matching caused by large variety and quantity of remanufacturing service resources, this paper presents the concept of remanufacturing service, and then describes the remanufacturing resource module. A matching model for remanufacturing Inservice today’s business environment, the trendbased towards more product variety andArtificial customization is unbroken. Due(IBABC). to this development, of resource module was constructed on Improved Bi-population Bee Colony Algorithm Then, basedthe onneed service agile and period, reconfigurable production emerged to cope with various products and product families. design types and optimize production delivery service quality, and systems service cost as multiple objectives, multiple modules were selected fromTodifferent of remanufacturing systems well asmodules to choose optimal product matches, product needed.was Indeed, most of the known aim to service as resource forthe combination-matching, and the Paretoanalysis solutionmethods set withareIBABC solved. Subsequently, themethods final matching analyze product or one product on the physical of level. productset. families, however, may differ largelywas in terms of theto number schemea was determined throughfamily combined-weighted theDifferent Pareto solution Finally, an application example proposed verifyand the nature of components. Thisoffact efficient comparison and choiceresource of appropriate product family combinations correctness and feasibility the impedes matchingan model of remanufacturing service module and related solving method. for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly product © 2018 The Authors. Publishedoriented by Elsevier B.V.families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. on Datum Chain, the physicalofstructure the products is analyzed. Functional subassemblies are identified, and Peer-review underBased responsibility of Flow the scientific committee the 51stof CIRP Conference on Manufacturing Systems. a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity product Service familiesresources; by providing support to both,Artificial production systemAlgorithm planners(BABC); and product designers. An illustrative Keywords:between Remanufacturing; Moduledesign matching; Bi-population Bee Colony Chaotic optimization example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 1. Introduction researches from the 2018. angle of Web service composition and Keywords: Assembly; Design method; To realize reasonably and Family fully identification use of remanufacturing
resource and achieve the extension of value chain of remanufacturing enterprises [1, 2], remanufacturing service can be used to meet the requirement of individualized 1.mode Introduction remanufacturing services by referring the service-oriented manufacturing concept [3]. Since remanufacturing services Due to the fast development in the domain of involve large amount of service resources [4], and the service communication and an ongoing trend of digitization and requirement and service objects are highly uncertain, it is digitalization, manufacturing enterprises are facing important necessary to seek an effective analysis method. challenges in today’s market environments: a continuing Modularization is a dynamic process, which involves tendency towards reduction of product development times and decomposing system into multiple separated components, shortened product lifecycles. In addition, there is an increasing mixing, matching and integrating the components without demand of customization, being at the same time in a global damaging system function [5]. Moreover, it is also an competition with competitors all over the world. This trend, effective method to solve complex system problems and which is inducing the development from macro to micro improve system efficiency [6]. Optimal matching of service markets, results in diminished lot sizes due to augmenting module plays a key role in realizing remanufacturing services product varieties (high-volume to low-volume production) [1]. combination. Currently, scholars have conducted related To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing 2212-8271 ©system, 2018 The it Authors. Publishedtobyhave Elsevier B.V. knowledge production is important a precise
dynamic matching of service system. For example, in the aspect of Web service composition, Wen JH proposed a taskoriented service discovery algorithm to realize self-adaption and high efficient Web service composition [7]. Regarding the Web problem without WSDL and/or (Web of the service product composition range and characteristics manufactured Services Description Language) document, Pan WF proposed assembled in this system. In this context, the main challenge in a service composition recommendation method based on modelling and analysis is now not only to cope with single service network [8]. Web service composition is a process of products, a limited product range or existing product families, data exchanging or integration, which focuses on realizing but also to be able to analyze and to compare products to define data exchanging or integration of Web service network new product families. It can be observed that classical existing module using service computation/programming technique product families are regrouped in function of clients or features. with customer's requirement as matching purpose. In the However, assembly oriented product families are hardly to find. aspect of dynamic matching of service system, Pan HX with On the product family level, products differ mainly in two regard to the service matching problems involved in multimain characteristics: (i) the number of components and (ii) the stage service process, constructed a multi-stage dynamic type of components (e.g. mechanical, electrical, electronical). service matching model, which can perform multi-stage Classical methodologies considering mainly single products overall optimization using dynamic programming algorithm or solitary, already existing product families analyze the [9]. By regarding service supply as an object and customer's product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems.
2212-8271©©2017 2018The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 51stDesign CIRP Conference Conference2018. on Manufacturing Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2018.03.029
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requirement as a matching goal, optimization algorithm can be used so solve model, so that, an optimal combination of service activity and resource can be realized. As a dynamic matching problem of service system, matching of remanufacturing service resource module is a typical multiobjective optimization problem [10]. By referring related researches on service matching, this paper proposes a matching method of remanufacturing service resource module based on Improved Bi-population Artificial Bee Colony Algorithm (IBABC), providing a referential thought and approach for remanufacturing service research.
service requirement
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Remanufacturing service platform
requirement knowledge
services requester’s retired products RM product
Knowledge of product history parts manufacturer
services requester RM product
Technical knowledge、 service programme
Distributors (again)
parts
RM design service
…… Assembly service
Remanufact urability assessment Cleaning service
Core remanufact urer
Dismantling service provider
Classifica -tion service provider
processing service
Recycling service provider
RMS provider
Scrap processing service provider
customer
retired products
Inventory/transport service provider
Fig.1. Concept model of RMS
Nomenclature Rc number of RMSRMa classes na number of RMSRMai belonging to RMSRMa n number of RMSRMai ωc weight of optimization objective TIME service-delivery time for completing the total process timeai service-delivery time of RMSRMai QUAL service quality presented by RMSRMai combination qualai service quality of RMSRMai, COST service cost presented by RMSRMai combination costai all kinds of cost in the service process of RMSRM rai the price aiof provided amount of service qai the provided amount of service Ns total number of bees Ne number of employed bee No number of onlooker bee D the dimensions of the individual vectors fi the fitness of Xi Pi the selective probability of Xi ci the score of fi Xμ the abandoned solution 2. Remanufacturing Services and Resource Module 2.1. Remanufacturing service ReManufacturing services (RMS) can be interpreted as: customer-facing-service of remanufacturing enterprise cluster based on RMS integration platform as core. In other word, through remanufacturing servitization, RMS provides remanufacturing enterprises with customer related valueadded activities during the operation process on the whole industrial chain. In the implementation process of remanufacturing, remanufacturing production-oriented professional services (remanufacturing recycling, remanufacturing assessment decision, remanufacturing design, remanufacturing processing, and information-based remanufacturing) should be given to damaged or retired products based on productive service. This is a distributed service by integration of resources as well as an integrated service method to increase remanufacturing value [11]. The concept model of RMS shown in Fig.1.
2.2. Remanufacturing services resource module ReManufacturing service resource module (RMSRM) refers to the overall function unit, which plays a basis role for function implementation of remanufacturing service activity [12]. RMSRM is an indivisible unit. A module can be either one single service resource or the combination of multiple service resources. All resources included in each service resource module are corresponding to the only matched service provider. 3. Matching Model of Remanufacturing Service Resource Module 3.1. Model Assumption RMS system contains Rc classes of RMSRMa, and each RMSRMa (a=1,2, …, Rc) contains na RMSRMai (i=1,2, …, na). Therefore, RMS system contains totally
∑
RMSRMai. RMSRMai in each unique class is responsible for realizing service activity in each unique stage. Single matching can only be completed by selecting one module from RMSRMai in the same class, and then combining it with the module from another class. Each RMSRMai is provided exclusively by single service provider (SP). In RMS process multiple RMSRMai are successively realized in serial mode. 3.2. Multi-objective optimization model The goal of RMSRM matching is to realize the shortest Service-delivery time (time), lowest Service Cost (cost), and best Service Quality (qual) achieved by RMSMai combination under service capability (cap). Therefore, the performance expectation index of RMSRMai matchi g is O(TIME, QUAL’, COST), user preference weight of each objective is ωc={ωTIME, ωQUAL, ωCOST}. The multi-objective optimization model can be constructed as equation (1).
Lei Wang et al. / Procedia CIRP 72 (2018) 1368–1373 Lei Wang, et al. / Procedia CIRP 00 (2018) 000–000
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opt
O(TIM E, QUAL' , COST)
RM SRMai (0,1) (qualai ) QUALO a timeai TIM EO a i s.t costai COSTO a i qai capai a 1,2, , Rc ; i 1,2, , n
(1)
where, TIME is the Service-delivery time for completing . remanufacturing service process, TIME ∑ ∑ timeai is the service-delivery time of RMSRMai, which also means the time period from accepting service request to completing service delivery. QUAL’ -QUAL is the transformation form for QUAL as the minimum optimization problem objective. QUAL is the s ervice quality presented by RMSRMai combination in complet , indic ing RMS process under serial mode, QUAL ∏ ating only when all RMSRMai service qualities are 1, can total service quality be 1. If there is a random RMSRMai suffering service failure (qualai =0), the RMS will be failed, and thus tot al service quality will be 0.
qualai, as the service quality of RMSRMai, is the ratio of service performance and expected service performance perceived by service requester. Suppose such ratio can be represented by any number from 0-1, therefore we ca get 0≤ qualai ≤ . COST is the service cost presented by RMSRMai . combination in completing RMS process, O T ∑ ∑ costai refers to all kinds of cost and expenditure in the process of providing service by RMSRMai, costai=rai·qai, in which, rai is the price of provided amount of service, qai is the provided amount of service, which acts as the standard unit of metered services, and its dimension varies upon the class of service. TIMEO, QUALO, and COSTO represent the given target values of service-delivery time, service quality, and service cost, respectively. Constraint condition RMSRMai ∈ (0,1) represents the selection of RMSRM. If RMSRMai is selected, RMSRMai=1, otherwise, RMSRMai=0.
3
All of these advantages make them become a hot spot since they were proposed. Among these swarm intelligence algorithms, ABC is an outstanding one because of its simple operation, less controlling parameters, fast evolution, accurate searching result, strong robustness and easiness of implementation [16]. ABC can not only solve continuous optimization problems and combinational optimization problems, but also has a more superior performance in solve some complex problems than GA, ACO and PSO. Therefore, a multi-target artificial bee colony optimization solution is proposed based on ABC in this study. Assume that the total number of bees is Ns, the population of the employed bee is Ne and the population of the onlooker bee is No. The dimension of the individual vectors is D, S=RD is the search space. If Xi∈S (i≤Ne) contains Ne individuals, then X=(X1, X2, …, XNe) represents a bee population. X(0) represents the initial bee population, X(n) represents the n-th generation. f : S R represents the fitness function fi (i=1, 2,…, Ne), then the ABC algorithm flow can be shown in the Fig.2 and the steps are as follows: Step1: Initialization. Set n=0, Ns feasible solutions (X1, X2, …, XNe) are randomly generated, and Xi can be calculated by j j j X ij X min rand (0,1)( X max X min )
(2)
where j is the j-th dimension of Xi and j∈{1, 2, …, D}. The fitness function value of each feasible solution is calculated and sorted. The top Ne individuals are the initial employment bee population X(0), and the rest are onlooker bee population. Step 2: Employee bee process. Make every individual of the n-th generation employment bee population X(n) search for a new location near its current location
new _ X i j X i j + *( X i j X kj )
(3)
where i, k∈{1, 2, …, Ne} and k≠i, k and j are randomly generated, α is a random number between -1 and 1, . Then the greedy selection method is adopted to select the better individuals between A and B, and the better one is retained to the next generation. Step3: Onlooker bee process. Each Onlooker bee chooses an employee bee according to the selective probability Pi. Then, new locations are searched by formula (3), while k∈ {Ne+1, Ne+2, …,Ns}.
4. Optimization solution 4.1. Artificial bee colony algorithm (ABC) Regarding the multi-objective optimization problems such as the matching of remanufacturing service resource module, current common intelligent algorithms include particle swarm optimization (PSO) [13], ant colony optimization (ACO) [14], artificial bee colony algorithm (ABC), and genetic algorithm (GA) [15], etc. All these swarm intelligence algorithms have low requirement of function information, evolutionary process that is independent from initial value, and fast searching speed.
⁄∑
(
where fi is the fitness of the Xi。 Step4: Scout bee process. When a bee has not found a better position after Limit times, a feasible solution is randomly initialized and the position of employee bee will replaced, then the employee bee turns into a scout bee. Step5: If the stop condition is satisfied, stop the calculation and output the optimal results. Otherwise, go to Step 2.
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Lei Wang et al. / Procedia CIRP 72 (2018) 1368–1373 Lei Wang, et al. / Procedia CIRP 00 (2018) 000–000
Start
rand Pi
Initialize Record the number of searchable solutions, the optimal function value, and the optimal individual
[ 1,1], i k
n=1
calculate the fitness fi
i 1
Y
V ij X ij * ( X ij X kj )
N
calculate the fitness f i
Y
Update the number of searchable solutions, the optimal function value, and the optimal individual
N
i i 1
Y
Calculate the observed bee selection probability
n n1 Max(Times) limit
N
Y
Randomly generate an alternative solution
Y
nN
Y End
i 1,t 0
For sub-population B, a tournament selection strategy based on local competition mechanism is adopted, i.e., let onlooker bee randomly select q individuals from the population for comparison, and then keep the individual with larger fitness value, wherein q represents the scale of the competition. When q=2, the selection probability Pi is determined after comparing the fitness value fi of two individuals. In this tournament selection strategy, the fitness value is regarded as the relative standard, rather than the absolute standard, so that premature convergence can be avoided in certain degree.
i Size( S )
Update the number of searchable solutions, the optimal function value, and the optimal individual
f i f i 1
i Size ( S )
N
Y i1
i i 1
f i f i 1
f i f i 1
f i f i 1
[ 1,1], i k
N
N
Y V ij X ij * ( X ij X kj )
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Fig.2. The flow of ABC
4.2. Improved Bi-population Artificial Bee Colony Algorithm (IBABC) To improve the global searching ability of ABC algorithm and accelerate its convergence speed, an improved Bipopulation artificial bee colony algorithm (IBABC) based on chaos optimization was proposed. Every other certain number of generations, the information between the two populations will be exchanged according to information interaction mechanism. When a certain solution is trapped in local optimum, chaos optimization is used to generate a new solution for jumping out of local optimum. The optimal solution set of above multi-target Pareto can be obtained using IBABC, and concrete steps are shown as follow: Step1: Population initialization. The initial population includes Ns solutions that are corresponding to optimization variables. Each solution Xi (i ∈ {1, 2, … , Ne}) is a D dimensional vector. The initial population is randomly divided into two equal-sized sub-populations: subpopulation A and subpopulation B. The fitness value fi of each solution is calculated. Step2: Determination of selection strategy. The selection strategy of ABC algorithm is applied for subpopulation A, i.e., let onlooker bee select the Xi with larger yield rate as new solution according to the selection probability calculated by equation (4). With such selection strategy, it is easy for us to select the good individual (individual with larger fitness value) and eliminate the bad one. However, with the evolution of population, the fitness values of all individuals will converge, making the selected good individual not so advantageous and the algorithm trapped in local optimum.
⁄∑
(
where, ci is the score of fi. If fi is the better one of the two individuals, then ci=1. Step3: Information interaction. For every other certain number of generations, information exchange between the two sub-populations will be carried out. By comparing the fitness value of optimal individual between population A and B, the optimal individual with larger fitness from one population can be determined, and then used to substitute for the worst individual from the other population, so that the information exchange is completed. Step4: Employee bee process. For both sub-population A and B, the fitness value of new solution is calculated according to equation (3). If the fitness degree of the new solution is better than that of original solution, then the original solution will be replaced by new solution; otherwise, keep the original solution. Step5: Onlooker bee process. The probability Pi of subpopulation A and B is calculated according to equation (4) and (5), respectively. Then, the solution of population A and B is determined according the Pi value. Moreover, field searching of equation (3) is conducted to generate new solution, and the fitness value of the new solution is calculated. If the fitness degree of new solution is better than that of original solution, then the original solution will be replaced by new solution; otherwise; keep the original solution. Step6: Chaos optimization of local optimal solution. For sub-population A and B, when a certain solution is still not improved after m cycles, we can determine that it is trapped in local optimum. For such situation, we should abandon this solution and generate a new one using the ergodicity of chaos for substitution and thus increase the convergence speed of algorithm. Suppose the abandoned solution Xμ=[Xμ1, Xμ2, …, XμD], aμ ≤ Xμr ≤ bμ, r ∈ {1, 2, … , D}, and chaos optimization of solution Xμ is performed: The Xμ is mapped into the definitional domain [0,1] of Logistic equation, i.e., 0μ 0μ , 0μ , , 0μ . Where 0μ ⁄ μ , r∈{1, 2, …, D},μ∈{1, 2, …, Ns}. μ μ μ Iteration is performed according to Logistic equation , ( is control parameters) which results in chaos sequence μ ，t∈{1, 2, …,T}，T is the number of chaos searchings.
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The generated chaos variable μ is returned to original solution space via inverse mapping μ μ μ μ . The fitness value of μ is μ μ μ μ calculated and compared with original solution, and the good one is kept. If the fitness degree of new solution is inferior to that of original solution, then return to previous step of chaos optimization for re-iteration and generating chaos sequence. This step is repeated till reaching maximum number of chaos T and then finishing the chaos optimization. Otherwise turn to previous step for reiteration and generating new chaos sequence. Step7: Optimal solution recording and termination judgement. The best ever solution is recorded. Judging whether the termination condition is met, if met, then the algorithm is finished, otherwise turn to Step2. 4.3. Pareto optimization based on combinatorial weighting In matching process of remanufacturing service resource module, the three optimization targets (i.e. service response time, cost and quality) are often conflicted with each other, and the simultaneous optimization of the three can hardly be achieved. Only one set of optimization solution can be obtained by IBABC, so it is impossible to compare Pareto solution set easily. To realize RMSRM matching, it is needed to select a satisfying optimal solution from Pareto solution set obtained by IBABC algorithm [17]. Therefore, Pareto optimal solution set can be further regarded as a matching scheme set of RMSRM, and then applied with normalization processing to obtain the standard decision matrix B=(bij)n×m with n schemes and m attributes. After that, combinational weighted calculation is performed according to the given objective weight ωc={ωTIME, ωQUAL, ωCOST}, so as to realize the final Pareto optimal decision.
5
RMSRM4i. The cost for recycling/ transportation/ processing a single working roll by RMSRMai is costai yuan/ton, the maximum capacity for recycling/transportation/processing working roll is capai ton/d, and service quality score is qualai. Among them, service quality is evaluated by RMS platform based on the experience data. The data of all RMSRMai such as price, service capacity, and service quality on decision making day is shown in Table 1. Table 1. The data of all RMSRMai RMSRM1 RMSRM11 RMSRM12 RMSRM13 … RMSRM3 RMSRM31 RMSRM32 RMSRM33 …
RMS recycling cost 1800 1770 1850 …
cap 130 100 110 …
qual 0.8 0.85 0.92 …
RMS processing cost 700 680 500 …
cap 30 35 27 …
qual 0.94 0.8 0.81 …
RMSRM2 RMSRM21 RMSRM22 RMSRM23 … RMSRM4 RMSRM41 RMSRM42 RMSRM43 …
RMS decisionmaking & design cost cap qual 200 138 0.95 210 150 0.87 180 110 0.92 … … … RMS logistics cost 320 300 280 …
cap 450 380 350 …
qual 0.88 0.85 0.93 …
Suppose the service provided by RMSRMai is sai, its service quantity is qai, the multi-objective optimization model for services resource module matching is established. The IBABC algorithm is programmed using Matlab 2014a, where the parameter setting for IBABC is： popsize =200, i.e., the size of sub-population A and B is 100, respectively; D=50, maximum number of iterations N=300. In Logistic, ,T ＝ 50. The initial population is generated by random distribution, and the Pareto solution set is obtained as shown in Fig.3.
5. Illustrative Example Assumption of service requirement: CSP plant of a certain steel enterprise needs to remanufacture 13 seriously damaged working rolls (Totally weigh 91.19 tons) with half price of new working rolls as total budget, i.e. means 312 thousand yuan. According to the contract, the remanufactured products shall be delivered to site A of CSP plant within 10 days after the execution of contract, and the total service quality should be maintained at no less than 0.5 (service quality reaches medium level or higher). 5.1. Multi-objective optimization solution Currently, RMS platform contains Rc=4 (a=1,2,3,4) classes and totally 60 RMSRMai including remanufacturing recycling service class accounting for 20 RMSRM 1i, remanufacturable decision and redesignable service class accounting for 10 RMSRM2i, remanufacturing processing service class accounting for 10 RMSRM3i, and remanufacturing transportation service class accounting for 20
Fig.3. RMSRM matched 3-objective Pareto solution set
According to the Pareto solution set, it can be known that the three objectives (service delivery time, service quality, and service cost) are mutual-contradictory. The improvement of one objective is realized at the expense of another objective’s performance. Based on the objective weight of user preference ωc={0.3,0.4,0.3}, the RMSRM matching scheme can be performed by realizing weighted combination of solution set in Pareto solution, and thus the final weighted Pareto solution can be obtained. The Pareto weighted value is determined to be 0.2993, which is obtained by normalizing the satisfying optimal Pareto solution in Pareto frontier. In addition, the corresponding optimal scheme is: RMSRM 119, RMSRM28, RMSRM36, RMSRM49, providing service by 70%
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of total enterprise capacity. The obtained optimal Pareto solution is: 6.452 days as total service delivery time, 27.5842 thousand as total cost and 0.832 as service quality. 5.2. Algorithm performance analysis Using Matlab 2014a, the programming of ABC and NSGA-II is conducted respectively, where the popsize for ABC is 200, other parameters are the same with that of IBABC. The popsize for NSGA-II algorithm is 200, the maximum number of generations (genmax) is 50, the probability of crossover (PC) is 0.8, the probability of mutation (PM) is 0.3. By randomly operating 50 times, the mean value, mean square error, maximum value and minimum value can be obtained, as shown in Table 2. Table 2. Calculation results by different optimization algorithms Optimization algorithms
Mean value
Meansquare deviation
Maximum value
Minimum value
Average calculating time /s
IBABC
0.3030
0.0048
0.3113
0.2993
205.8724
ABC
0.3090
0.0072
0.3190
0.3017
215.7669
NSGA-II
0.3060
0.0066
0.3148
0.2996
259.2494
Table 2 shows that IBABC algorithm is significantly better than ABC and NSGA-II in terms of solution accuracy, result stability, arithmetic and convergence speed. ABC algorithm has faster arithmetic speed, but poorer stability than NSGA-II algorithm. 6. Conclusions This paper proposed the concept of remanufacturing services and defined the RMSRM. By regarding service delivery time, service quality, and service cost as objectives, RMSRM matching multi-objective optimization model was established, which can avoids the one-sided problem in single objective optimization. On the basis of ABC algorithm, a chaos optimization based IBABC was proposed to optimize the solution of multiobjective model. IBABC algorithm adopts bi-population structure to realize parallel operation, which enables subpopulation to have different selection strategies. Therefore, IBABC can effectively maintain population diversity while accelerating convergence rate. When the algorithm is trapped in local optimum, new solution can be generated using the ergodicity of chaotic thought, allowing the algorithm to have sound global searching and thus jump out of local optimum. Taking remanufacturing of working rolls in certain steel enterprise as an example, the feasibility and practicability of the proposed method were verified. The analysis results verified that the optimize performance and the robustness of the IBABC algorithm was superior to ABC algorithm and NSGA-II algorithm. This paper lays a solid foundation for the research on RMS system and the basic theory of resource
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management, and also provides reference for the research of other industries and the RMS application. Acknowledgements This work is supported by the National Natural Science Foundation of China under project No. 71471143. It is also supported by Center for Service Science and Engineering (Wuhan University of Science and Technology) under project No. CSSE2017KA04, and Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resources (Wuhan University of Science and Technology) under project No. 2016zy013. References [1] Wang L, Xia XH, Xiong YQ, et al. Architecture of reverse supply chain service system. Computer Integrated Manufacturing System. 2015; 21(10), 2720-2731. [2] Paul G, Emma R, Jenifer H. A review of the state of the art in tools and techniques used to evaluate remanufacturing feasibility. Journal of Clearner Production. 2014; (81): 1-15. [3] Cheng Y, Tao F, Zhao DM, et al. Modeling of manufacturing service supply–demand matching hypernetwork in service-oriented manufacturing systems. Robotics and Computer-Integrated Manufacturing. 2017; 45:59-72. [4] Deng QW, Liao HL, Xu BW. The Resource Benefits Evaluation Model on Remanufacturing Processes of End-of-Life Construction Machinery under the Uncertainty in Recycling Price[J]. Sustainability, 2017, 9(2): 256. [5] Baldwin CY, Clark KB. Design rules, volume l, the power of modularity. Cambridge MA: MIT Press 2000. [6] Wang PP, Ming XG, Wu ZY, et al. Research on industrial product– service configuration driven by value demands based on ontology modeling. Computers in Industry. 2014; 65(2): 247-257. [7] Wen JH, Jiang Z, Tu LY, et al. Task-oriented web service discovery algorithm using semantic similarity for adaptive service composition. Journal of Southeast University (English Edition). 2009; 25(4): 468-472. [8] Pan WF, Li B, Jiang B, et al. Service composition recommendation based on service networks. Engineering-Theory & Practice. 2014; 34(6): 131142. [9] Pan HX, Wang J. Modeling and Optimization of Multi-Stage Dynamic Service Matchmaking for Service Systems. Industrial Engineering Journal. 2012; 10(15): 112-117. [10] Yu MC, Mark G, Lin HC. Fuzzy multi-objective vendor selection under lean procurement. European Journal of Operational Research. 2012; 219(2, 1): 305-311. [11] Paul P, Stephen LV, Nathan C, et al. The service system is the basic abstraction of service science. Information Systems and e-Business Management. 2009; 7(4): 395-406. [12] Wang L, Xia XH, Xiong YQ, et al. Modular Method of Remanufacturing Service Resources. Computer Integrated Manufacturing System. 2016; 22(9): 2204-2216. [13] Guo JQ, Ya G. Optimal strategies for manufacturing/remanufacturing system with the consideration of recycled products. Computers & Industrial Engineering. 2015; 89: 226-234. [14] Teschemachera U, Reinhart G. Ant Colony Optimization Algorithms to Enable Dynamic Milkrun Logistics. Procedia CIRP. 2017; 63: 762-767. [15] Fu CM, Jiang C, Liu GP, et al. Micro Multi-objective Genetic Algorithm Based on Grid Domination. China Mechanical Engineering. 2015; 26(16): 2208-2214. [16] Ghambari S, Rahati A. An improved artificial bee colony algorithm and its application to reliability optimization problems. Applied Soft Computing. 2018; 62: 736-767. [17] Li N, Wang MH, Ma SG, Li B, et al. Mechanism-parameters Design Method of an Amphibious Transformable Robot Based on Multiobjective Genetic Algorithm. Journal of Mechanical Engineering. 2012; 48(17): 10-20.