Daniele Cerri
email: daniele.cerri@polimi.itsergio.terzi@polimi.it

Sergio Terzi

Improving Manufacturing System's Lifecycle: Proposal of a Closed Loop Framework

Keywords: Life Cycle Assessment, LCA, Life Cycle Costing, LCC, Closed Loop, Framework, System Lifecycle

Over recent years, the context where companies operate has dramatically changed, forcing the business models' revision in order to survive in the market. Nowadays, in the manufacturing system's context, customers are focusing its attention more and more on an efficient and effective management of system lifecycle. Methodologies such as Life Cycle Costing and Life Cycle Assessment are useful to evaluate costs and environmental impacts generated along the whole lifecycle, however they are not sufficient to improve system lifecycle. The aim of the paper is to propose a closed loop framework in order to improve lifecycle of manufacturing systems.

Introduction

During the last years, the context where companies operate has dramatically changed. Cost pressure of emerging countries, more strict environmental regulations and new customers' needs have completely changed the market and the leverages that before regulated it.

In order to compete and survive in the global market, companies need to revise their business models, changing their paradigms and including new leverages.

Nowadays, in the manufacturing system's context, customers are focusing its attention more and more on an efficient and effective management of system lifecycle. Indeed, customer evaluates different proposals by different suppliers, in order to choose the best one. They consider different factors: one of the most critical is the lifecycle costs (and the lifecycle environmental impacts).

In this context, suppliers are forced to pursue a product lifecycle approach for their systems, in order to hit the customers' expectations. In order to evaluate costs and environmental impacts along the whole lifecycle, suppliers can be supported by two well-known methodologies: the Life Cycle Costing (LCC), to evaluate the lifecycle costs, and the Life Cycle Assessment (LCA), to evaluate the lifecycle environmental impacts. However, these methodologies are useful only to evaluate the economic and environmental dimensions, but they are not able to support the lifecycle improvement of manufacturing systems.

In order to cover this gap, the paper proposes a closed loop framework to improve lifecycle of manufacturing systems. Section 2 shows the theoretical background, while Section 3 describes the Closed Loop Framework. Section 4 shows a possible application in a real industrial context. Finally, Section 5 concludes the paper, highlighting next steps.

Theoretical Background

This section reports the theoretical background behind the proposed closed loop framework. Figure 1 shows steps to define the theoretical background.

Fig. 1. Theoretical background steps

First of all, product lifecycle has been defined. The lifecycle of a product can be divided into 3 phases:

• Beginning of Life (BoL): design and manufacturing of the product o Disposal: the product is disposed and therefore it doesn't get back into the life cycle flow [START_REF] Terzi | A holonic metamodel for product traceability in PLM[END_REF]. Using the GERA Model [START_REF]IFIP-IPAC Task Force, GERAM Generalised Enterprise Reference Architecture and Methodology[END_REF], instead, it is possible to describe in a more detailed way the lifecycle of manufacturing systems. It identifies the following stages: identification, concept, requirements, preliminary design, detailed design, implementation, operation, possible redesign activities, decommission. Therefore, Beginning of Life phase is represented by identification, concept, requirements, preliminary design, detailed design and implementation stages; Middle of Life is

Product Lifecycle Definition

Lifecycle Methodologies Definition

Gaps Identification represented by operation and possible redesign activities stages; finally, decommission stage represents the End of Life phase.

Usually, the lifecycle of an industrial system starts when the customer sends a request for tender to the supplier (industrial systems' manufacturer). Customer and supplier work together during the first phases (identification, concept and requirements). Then the supplier realizes a preliminary design of the industrial systems, defining the lifecycle costs and environmental impacts. Usually, preliminary design ends with the submission of tender. Customer evaluates different proposals received by different suppliers, and the best one gets the order. Different key factors are used to evaluate the proposal: one of the most critical is the lifecycle costs (and lifecycle environmental impacts). If the order is won, manufacturing system is designed in detail, and then installed at customer plant. Finally, the system fully operates until decommission.

As previously mentioned, lifecycle costs and environmental impacts are key factors to win the order. In order to evaluate costs and environmental impacts generated along the whole lifecycle, two methodologies have been identified: Life Cycle Costing (LCC) and Life Cycle Assessment (LCA).

Lifecycle cost is defined as the total cost of ownership of machinery and equipment, including its cost of acquisition, operation, maintenance, conversion, and/or decommission [START_REF]Reliability and Maintainability: Guideline for Manufacturing Machinery and Equipment[END_REF].

Life Cycle Assessment, instead, is a methodology to assess environmental impacts associated with all the stages of a product's life from-cradle-to-grave [START_REF]Life Cycle Assessment: Principles and Practice[END_REF].

Analysing LCC and LCA methodologies, some gaps have been identified. First of all, LCC and LCA methodologies are very good in comparison and estimation of few products or alternatives; however, when the number of alternatives increases, they are not able to support decisions and decision makers in a good way, especially in the case of manufacturing systems.

Furthermore, LCC and LCA methodologies have to be integrated with systems able to collect data from the field, with the aim to maintain under control costs and environmental impacts generated along the operation phase. Collection of data from the field enables also the extraction of valuable knowledge for designers, in order to improve design and sustainability of next manufacturing systems.

In order to cover these gaps, next section proposes a closed loop framework in order to improve sustainability issues.

Closed Loop Framework

In this section, a closed loop framework to improve sustainability issues is presented. Figure 2 describes the framework in a graphical way. Closed Loop Framework is built on GERA model, considering all the stages identified. Lifecycle stages are divided into three macro-phases: (i) Beginning of Life, which identifies design and manufacturing of the system; (ii) Middle of Life, which identifies the operation stage and related services, besides the possible system re-design; and (iii) End of Life, which identifies the system decommission. Blue arrows identify the information flow, while red boxes report tools associated with the lifecycle stage. Tool about End of Life is in a grey box because it has not been yet developed. Configurator and Data Collection tools have been developed to cover gaps identified in the previous section, in order to support the decision makers in their activities. Briefly, Configurator tool's aim is to support the identification and creation of the optimal lifecycle oriented configuration, in terms of costs and environmental impacts, able to satisfy the customer's requirements. Data Collection tool's aim is, instead, to extract valuable knowledge from the tons of data from the field, both to maintain under control the existing system and to design next manufacturing systems. The aim of the future End of Life tool will be to understand the best End of Life option for each component of the manufacturing system.

As previously mentioned, blue arrows represent the information flow. Till now, the focus was on data and information from the field, during the utilization phase of the system. Indeed, to extract valuable knowledge from the field is useful for both Beginning of Life, Middle of Life, and End of Life phases, because it enables: (i) a design improvement of next manufacturing systems (BoL), (ii) a better management of the system during the operation phase (MoL), and (iii) a better understanding of the wear state of the entire system (EoL).

Identification

Conce pt

Requirements

Fig. 2. Closed Loop Framework

Configurator tool [START_REF] Cerri | Proposal of a multi-objective optimisation of product life cycle costs and environmental impacts[END_REF][START_REF] Cocco | Development and implementation of a Product Life Cycle Optimization model[END_REF] has been developed with the aim to support designers and system engineers in their activities during the preliminary design, in order to create and identify the optimal lifecycle oriented configuration, minimizing lifecycle costs and environmental impacts and satisfying customer's requirements. Therefore, the tool returns the optimal configuration, in terms of which components have to be installed on manufacturing system, and lifecycle costs and environmental impacts values. Configurator tool is built on genetic algorithm, which has been chosen for three main reasons: (i) it is more efficient than others when the number of variables increases (for example, an assembly line for the automo-tive sector can usually be composed up to 100 stations); (ii) it presents no problem with multi-objective optimization and (iii) it is suitable for applications dealing with component-based systems (a product could be seen as a chromosome and its components as genes).

Genetic algorithm has to solve a problem with two objectives, which are the minimization of lifecycle costs and the minimization of lifecycle environmental impacts. The objectives consider only the costs and the environmental impacts specified by the customer, in order to deliver the proposal that better fits with the customer's requirements. Furthermore, it is possible to implement constraints in the problem. As for the objectives, constraints depend on customer's requirements. The algorithm return the optimal configuration of the system, identifying which are the components that minimize the lifecycle costs and the lifecycle environmental impacts, satisfying the constraints. For example, in an assembly line, the algorithm suggests which station, among automatic, semi-automatic or manual, is the optimal one. Being a multi-objective problem, it is possible to have a trade-off between the two objective and, therefore, to have more than one optimal configurations. Decision makers have to choose which optimal configuration better fits with customer's requirements.

The tool has a user interface (Figure 3), divided into two spaces, one to define the objective functions and the other one to define the design of the system (how many components, how many alternatives for each components). In a window user has the possibility to import a *.xls file or to insert data. Data are then validated and checked. Finally tool executes the genetic algorithm. Results are displayed in the Output window, which reports: (i) the value of the objective functions and (ii) all the optimal configurations, showing which components are selected to realize the system.

Fig. 3. Configurator Tool's User Interface

Data collection tool is a simple tool with the aim to extract valuable knowledge from the tons of data collectable from the field, during the operation phase. Indeed, it is possible to collect tons of data from the different sensors installed on the system's components, machines or stations. The issue is how to use these data in a valuable and useful way, returning to decision makers structured knowledge. Decision makers can interrogate the database, through a user interface, in order to receive the desired information, through the back end logic, able to answer to the interrogations. Back end recovers data and information from the PLC databases, which collect information by different system's sensors.

The main benefits of this tool are: (i) the real time monitoring of the system and (ii) valuable knowledge for the design improvement of the next systems.

The real time monitoring enables to understand if lifecycle costs and environmental impacts, besides the technical requirements, have strong deviations rather than the estimated values during the preliminary design phase, through Configurator tool. Furthermore, Data Collection tool helps to find the right corrective actions, in order to reduce the deviations.

The tool returns valuable knowledge, useful for the design of the next system. It enables system improvements, in terms of reduction of costs and environmental impacts, understanding which are the most critical components / stations / machines.

The tool is able to summarize the main system's parameters in order to verify quickly performances of each system machine / station. It is possible to set different indicators, according to the industrial needs, like average cycle time, mean time between failures (MTBF) and mean time to repair (MTTR), availability and overall equipment effectiveness (OEE).

Decision makers can visualize detailed information, visualizing different pages that recap the main performances in a numerical and graphical way. For example, the page about availability shows the parameters of: availability, mean time between two failures (MTBF) and the mean time to repair the problem (MTTR). Furthermore it shows the machine states: working, failure, blocked, starved, excluded, intervention, over cycle time and tool change.

Figure 4 shows a graphical report of the tool.

Application

An Italian global supplier of industrial automation systems and services mainly for the automotive manufacturing sector is applying Closed Loop framework. The company offers its proficiency as system integrator and its complete engineering solutions, from product development and manufacturing, to assistance to the production start-up phases, equipment and full plant maintenance activities. Lifecycle of a manufacturing system follows the GERA Model [10] with a good approximation (see Figure 5). Company is testing Configurator and Data Collection tools. Till now, Configurator tool has been tested on an industrial case provided by the company, which was a fraction of an engine assembly line. Data collection tool, instead, has been tested within the company, in a laboratory that simulates the behaviour of a production system. At the end of the test, an evaluation has been conducted via quali-tative questionnaire. This first evaluation enables to understand strengths and weaknesses of the framework, in order to improve it in the next steps. Furthermore, other tests have been planned, in order to evaluate the goodness of the framework. About Configurator tool, it will be tested on an assembly line composed by 20 stations. Data Collection tool, instead, will be implemented and tested on a real assembly line installed in Poland. At the end of these tests, a complete and extended evaluation of the framework will be conducted.

Conclusions

The aim of this paper is to propose a closed loop framework in order to improve lifecycle of manufacturing systems. Indeed, in the current context, customer evaluates different proposals by different suppliers, choosing the best one. One of the most critical factor considered by customers is the lifecycle costs, and in some cases they consider also the lifecycle environmental impacts.

Section 2 shown the lifecycle of a system and the lifecycle methodologies identified, which are Life Cycle Costing and Life Cycle Assessment. These methodologies need to be supported by other tools, in order to be effective and efficient. For this reason, Section 3 describes the proposed Closed Loop Framework, explaining tools and information flow. Finally, Section 4 briefly presents a work in progress application of the framework on a real industrial context.

Further steps are necessary to complete the framework. First of all, it is necessary to identify or develop a tool for the End of Life of a system, in order to support decision makers in the best end of life option (re-use, re-manufacturing or recycling). Implementing this tool, the framework will be completed, enabling a full lifecycle improvement of manufacturing systems.

About lifecycle, it is necessary to consider also the social pillar, which is arising during the last years. Social Life Cycle Assessment (S-LCA) methodology will be therefore studied, in order to understand if it is possible to add social evaluation to economic and environmental ones.

About environmental impact, instead, it is necessary to understand the impact categories to take into account. It is important to conduct a literature analysis or a survey to collect the main impact categories related to manufacturing systems.

Finally, Closed Loop Framework needs to be tested in the industrial context. Now, it is applied in a company that produce manufacturing systems for the automotive sector, and at the end of the application a first evaluation will be conducted. The goal is to involve other industrial companies, in order to refine and validate the framework.

acknowledge their gratitude to all the partners for their contributions during the development of concepts presented in this paper.

Fig. 4 .

 4 Fig. 4. Example of Data Collection tool's output

Fig. 5 .

 5 Fig. 5. System lifecycle within the company

  The materials can get back in Beginning of Life phase, in Manufacturing phase

• Middle of Life (MoL): use of the product (and all the services connected) • End of Life (EoL): in this phase there can be 4 cycles: o Reuse: the product is reused as is, therefore it gets back in Middle of Life phase o Remanufacturing: the product is remanufactured, therefore it gets back in Beginning of Life phase, in the Manufacturing phase o Recycling: the product is recycled, transforming it into materials.

Acknowledgments. This work was partly funded by the European Commission through Manutelligence (GA_636951) Project. The authors wish to


Muhammad Naeem

Muhammad Fahad

Néjib Moalla

Yacine Ouzrout

Abdelaziz Bouras
email: abdelaziz.bouras@qu.edu.qa

Big Data Perspective with Ontological Modeling for Long Term Traceability of Cultural Heritage 1

Keywords: Big Data, DBSCAN, Cultural Heritage Ontology, Traceability

The safeguarding of cultural heritage has brought forward the generation of heterogeneous, complex, diversified and irreplaceable digital data. It becomes difficult for an object with missing characteristics to perform the premises identification, object identification as well as its movement recording. Therefore, it is an imperative need of traceability of the cultural digital objects. In this study, we have proposed an expert system to address the issues of achieving and maintaining traceability of cultural objects. The system has employed big data technologies as well as ontological modeling capabilities to semantically trace objects. We have designed Cultural Heritage Ontology (CHOnt) that captures all the semantics for inference mechanisms. We have shown that the proposed system is capable of sound expressiveness with an immense potential in offering a scalable solution as a common vehicle through which archaeologists, IT specialists and even a non-professional can trace, evaluate, enhance, analyze and exchange all types of information.

Introduction

Cultural heritage of a civilization bears numerous artifacts, such as folk music, dance, language, folk lore, seer axiom, oral literature, manners, games, etiquette, handicraft, traditional medicine, architecture arts and other preservation methods used for the expressivity of a region. Each artifact mentioned above is either tangible or intangible but strictly continuous in its nature. This intrinsic nature of dynamic behaviors of a culture has motivated the artists, poets, writers and painters to preserve the heritage in the shape of tangible formats. With the advent of modern technology, the concept of Digital Preservation (DP) or knowledge retention was emerged [START_REF] Naeem | An ontology based digital preservation system for enterprise collaboration[END_REF]. The core purpose of DP is the process of ensuring communication between future and past via innovative artifacts. In other words, it points out the persistence of digital resources in such a way that enables their rendering with easiness, availability and comprehensibility for the ancillary contemporary reuse. A big advantage of DP is that it can ensure the process of protecting the continuance concerns of forthcoming generations. There is an imperative motivation to maintain universal knowledge repositories addressing digital museums, communication conduits, digital archives and other type of digital memory systems. The museums and archivists are constantly collecting cultural heritage. According to The World Museum Community, there are more than 55,000 museums in 202 countries [START_REF] Academy | The World Museum Academy[END_REF]. Here a question arises, if a museum receives a digital resource of an artifact, then it may or may not have all of its related information. The completion of a set comprising all relevant information can helps in investigation of the authenticity of antique paintings. The experts usually adopt verification mechanism through stylistic evaluation, objective tests of the ageing of the underlying material or with the help of modern scientific tools. Some researcher have highlighted the problem of retrieval as important issue in the cultural heritage domain [START_REF] Cesarano | The OASYS 2.0 Opinion Analysis System[END_REF]. It has also been reported that the unstructured data handling in the cultural heritage data is a problem [START_REF] Goerz | Adaptation of nlp techniques to cultural heritage research and documentation[END_REF]. Another perspective was how to find out the semantics of the cultural heritage [START_REF] Hardman | Using AI to Access and Experience Cultural Heritage[END_REF] . We have argued in this study about the scalability of all of such techniques handling the cultural data in the wake of mixture of structured and unstructured data. We have formulated a research question: How can the traceability of a new or existing but questionable object can be performed given large amount of structured and unstructured data in the domain of cultural heritage. The proposed system has used various technologies including Big Data, Natural Language Processing (NLP) and Ontological Modeling. We in this study have proposed an expert system which can assist an expert to conclude more precisely and with improved confidence.

The reaming of the paper is organized as below. The section 2 is related the overview of the techniques describing the problem in this context. In section 3, we have introduced the architecture and then discussed it in detail. This section is further divided into numerous sub sections in which we have discussed each component, experimental work carried out and its justifications. The last section is concerned with the conclusive remarks in which we have highlighted how our proposed architecture is useful for the traceability of the cultural heritage domain.

Literature Review

We have reviewed the literature related to the provision of traceability of the cultural data in all of the possible ways. We noticed that most of the research work has been carried out by means of applying "Semantic engineering" on the data itself [START_REF] Hardman | Using AI to Access and Experience Cultural Heritage[END_REF] [START_REF] Naeem | An ontology based digital preservation system for enterprise collaboration[END_REF] . The problem with such an approach is that developing a semantic network on varied level of information produces "less concise" ontologies. We have argued in such situations that prior to feeding the data in the semantic engineering, one must converge the plethora of information into a fine grained dataset. More the data set is precised, better the output of the final semantic expressivity of cultural heritage. As the information era dawned with the advent of modern computational tools, the vast amount of digital data has been reorganized in the structured and unstructured format. This data eventually culminated into the databases grouping the cultural heritage knowledge [START_REF] Nadb | National Archeology DataBase[END_REF]- [START_REF] Museum | Database of the British Museum[END_REF]. Apart from these specialized sources, other online knowledge repositories such as wikis and weblogs could also be observed for the source of information; albeit such sources are less explicit and void of systematic traceability of a query related to newly arrived artifact in a museum. The research question is that how can we introduce new functionalities and opportunities to improve the quality of cultural data, whether using innovative semantic web techniques alone are sufficient? [START_REF] Hyvönen | Semantic portals for cultural heritage[END_REF], [START_REF] Stock | Personalized active cultural heritage: The PEACH experience[END_REF] introduced work employing intelligent engineering, however their approaches were limited to only provision of facilitation of better accessibility to end users of their systems. The usefulness of NLP has also been highlighted in this domain [START_REF] Goerz | Adaptation of nlp techniques to cultural heritage research and documentation[END_REF]. They have used the NLP for the purpose of identifying essential information earlier. They highlighted that most of the systems deal only with the relational data. However, they highlight that there are numerous situations when there is only unstructured data. They proposed "WissKI" tools for the semantic annotations using controlled vocabularies as well as formal ontologies. Their focus was mostly concerned with the recognition of events with the free text of documents. Some previous work related to the digital cultural preservation was related to approached with the aim of proposing techniques for improving the retrieval, organization, and understanding of non-homogenous cultural knowledge through the cross-analyzing multi sources information [START_REF] Cesarano | The OASYS 2.0 Opinion Analysis System[END_REF].

Methodology

Motivated from the discussion in the previous sections, we have introduced an architecture which can handle the research problem addressed in the previous section. Figure 1 shows the flow of the components and the interaction between them. It also illustrates showing the input and output details of each of the three components. We shall discuss each one of them in detail in the following subsections.

Natural Language Processing

We collected data from the online resource (Museum of Archaeology and Anthropology, University of Pennsylvania) [11]. The original data consists of 346,474 object records. Although the data is in comma separated format, however, there is a problem with this large dataset. In many cases, the data is in semi-structured nature with various free text fields. This requires that we should perform natural language initial processing such as stemming, lemmatization, etc. However, the application of natural language processing cannot provide good results unless the semantic annotation is performed using the controlled vocabulary.

Fig. 2. A sample from the forest of semantic graph

In the column data, we faced problem such as "Tempera on wood" available in various dimensions. As the dimensions vary, it gives a unique state. The second problem was the hierarchy problem. "Oil on panel" and "Oil on canvas" both fall under the same category of Oil Painting. We need to provide the hierarchy in such a way that for grouping the hierarchy is useful, but for in depth tracing the more detail granularity is required. In the first part of this step, we take whole of the free text of the column, tagging it into pieces of Nouns, Adjectives, Adverbs and Verbs separated using NLP Library [START_REF] Manning | The Stanford CoreNLP natural language processing toolkit[END_REF]. In the second part, we develop a forest of semantic network which connects all of these concepts realized into the previous part. This semantic network is used as a base of the controlled vocabulary. For example, Figure 2 is showing some of graphs in the forest in which the common technique is "chalk". This word is derived from graphs which have semantics relevant to this concept. Figure 1 is showing only four of such graphs; the conclusive concept out of these graphs is derived by the means of semantic graph matching. In the third part of this step, we perform the semantic similarities between graphs in the semantic forest. This process is useful to identify all of the related concepts in a hierarchy. The outcome of this operation provides us a data by the means of which we have tuned up the granularity level of the distinct states in the column.

Clustering on Big Data

Previously, we discussed that the cultural heritage data is also increasing on the same pace as that of other domain of knowledge. It is a known fact that the extraction of significant structures out of arbitrary high dimensional data has always been a challenging task. Although stratified sampling also serves the purpose of preparing the samples for the input data. However stratified sampling is more or less a random grouping performed on the strata. The clustering technique employs the objective function and refines the data according to the objective function [START_REF] Naeem | KDSSF: A Graph Modeling Approach[END_REF] [START_REF] Naeem | gSemSim: Semantic similarity measure for intra gene ontology terms[END_REF]. In the case of cultural data, we are more focused on aggregating data. Hence, in this case the clustering is more helpful for applying our methodology. Clustering techniques capable of running on Hadoop platform can give the second level solution. We have employed the DBSCAN clustering algorithm using MapReduce approach [START_REF] Kriegel | Density-based clustering of uncertain data[END_REF], [START_REF] Barroso | The datacenter as a computer: An introduction to the design of warehouse-scale machines[END_REF]. The added advantages of using DBSCAN over MapReduce are following:

1. DBSCAN possesses the capability to produce irregular shape clusters which are more close to the distribution nature of data. 2. MapReduce ensures the scalability of the executing functions. This aspect is useful in case of high dimensional data as described earlier in our case. In DBSCAN, each object is clustered given two core parameters. Mathematically, we can define it by equation 1.

{ }

) 1 ( ) , ( | : ) ( ε ε ≤ q p d q p N
Where N denotes the number of objects between two given objects p and q (both are inclusive); ε is radius. On each point scale (as for p in the equation 1), it gives a circular cluster. However as lots of circles individually grows up, collection of tiny circles (sub-clusters) are realized into a dense regions in the data space which is separated by regions of lower object density. This identifies a maximal set of density connected points. DBSCAN is useful for such situations where data is distributed in numerous small density zones. The cultural data by virtue of its nature bears density oriented distribution. The underlying reason is that given a specific type or format, specific cultural information are aligned in a peculiar way. One can notice that DBSCAN is sensitive to external parameters of radius size and number of observations. These parameters are usually found out by means of k th nearest neighbor.

The MapReduce can be mathematically defined by the following definitions.

∈

composed of m number of features; The mapper can be defined as:

{ } ) 3 ( ,..... , , , , 1 / 3 / 2 / 1 / v k v v v k im i M n i i i i i = ←
Notice that the set mapped values for each key is unbounded. This set may be an empty set or a set with arbitrary length.

v v v k v v v k i i i i i i i i

Λ ←

The result of this step ends up in producing cluster samples which can be easily classified. Certainly the data has been reduced but still this is not converged enough to give a precise meaningful notion.

Classification on Cluster Objects

In the previous step, we obtained clustering samples. The clustering of sample serves to reduce data problem complexity by providing users with groups of similar entities. However, clusters are unable to highlight relationships among various features, especially in the case of data analysis on the high dimensional data sets. Therefore, we classify cluster samples in order to reduce a data set. This also bears special relevance in this case as we are motivated to reduce the data at the minimum loss of information. Generally, there are two types of classification (Hard and Soft classification) based on the type of class assignment to each of the clusters while grouping them according to their similar features. With the Hard classification mechanism, one can determine whether an instance can either be or not to be in a particular class. With the Soft classification, one can extrapolate whether an instance can be predicted to be in some specific class with some likelihood and often a probability distribution across all of the classes. We apply soft classification as it is suitable to have a probability distribution that depicts the level of confidence depending on the similarity of their features.

Ontological Modeling and Traceability

In the previous step, we performed classification of clusters which results into grouping the individual clusters characterized by their similarity features. However, the completeness of classification is still arguable; because this classification lacks semantics to be better queried and searched for the traceability of any cultural object. Therefore, we built a Cultural Heritage Ontology (CHOnt) that provides enriched model for the inference of cultural objects as illustrated in Figure 3. Cultural Heritage includes tangible and intangible cultural objects which belong to societies or groups that are inherited from past generations.

Fig. 3. Top level view of Cultural Heritage Ontology (CHOnt)

In CHOnt ontology, each cultural object that belongs to the cultural heritage museum is tracked by its ID, Name and Description, captured by the Cultural-Object concept. In addition, Cultural-Object concept is associated with the Object_Property via a has_features property (illustrated by Figure 4). Each object possesses several properties, such as Form, Type, Period, School, and Technique. Figure 4 below also illustrates the expanded view of Type and Form concepts. We have checked the consistency of this ontology by using Description Logic (DL) Reasoner to avoid any type of inconsistencies [START_REF] Barroso | The datacenter as a computer: An introduction to the design of warehouse-scale machines[END_REF]. This ontological model is the fundamental building block for the traceability of the cultural heritage objects. When an anonymous object with certain known features arrives at a museum, the question for its traceability appears as a challenging task. We brought forward a user interface to describe its known features and provide some more descriptive information if available. This information is captured inside the CHOnt. Next step is to perform the semantic matching making to trace that object. For the match matching we are using our previously build ontology mapper DKP-AOM [START_REF] Fahad | Towards Classification of Web Ontologies for the Emerging Semantic Web[END_REF] to detect the aggregated semantic similarity between the anonymous object features and the underlying classified clusters of objects. DKP-AOM provides different strategies, such as string matching, synonym matching, etc. to find the level of confidence for traceability of cultural object. String matching strategy enables direct matching of anonymous object properties with the concepts of CHOnt. Synonym matching strategy uses WordNet lexical database to find all possible synonyms to able semantic traceability when different terminologies are used for the same type of object properties. Based on the level of confidence aggregated by different similarity measures, we trace cultural object and illustrate its likelihood based on its semantic similarity.

Results and Discussion

In this section, we shall analyze the results obtained from the experiment executed on the proposed architecture (see Figure 1 for the detail). The cultural data usually contains a lot of free text. If we apply the machine learning techniques, the learning model found large number of distinct states. Table 1 is showing the weighted average result of Naïve Bayes classification obtained before and after application of forest of semantic network. Certainly, one can argue that reducing large number of unique states in feature variables as well as reducing classes benefit in reducing error in classification. However, at this point, the strength of the system appears when by means of ontological modeling and forest of semantic network can pin point the missing information accordingly. For example some techniques such as "Brush, brown ink and oil on blue primed paper", "Ceiling painting in oil", "Charcoal and oil on canvas", "Charcoal and oil on cardboard", "Charcoal and pastel on paper" etc. have conceptually alike (although not equivalent). These terms have semantic meanings. Unless, numerous concepts are not clustered, the classification is always prone to give poor results. There was significant margin to increase the classification accuracy and the same was acquired by the proposed hybrid approach in this study. Figure 5 is providing a visualization which is a reflection of prime sections of the data. These include Form, Type, School, Period, and Technique. Every concept has a lot of instances. The challenge in the visualization is to organize the maximum information into a significant shortest description. A glance view over the figure informs about the possible inclusion of any unidentified object. However, still the problem is that this is a manual technique; this might be helpful to validate the ontological model but still we need an ontological based expert system to enable automatic traceability.

Conclusion

The study of culture heritage and its preservation is interesting as well as important for variety of domains including anthropology, psychology, archaeology, museology, sociology, communication, management and business. This research presents an expert system based on heterogeneous architecture with the purpose of traceability and estimation of missing information for a newly arrived artifact in a museum. The technique can be used to eliminate the risk of inclusion of possible inconsistencies, and preserve only significant concise information. We introduced the layout mechanism of the essential functionalities to validate the architecture and the interoperability of various context aware technological modules. The proposed architecture can find answers to interesting research problems by modeling structured and unstructured data for the purpose of traceability.

Fig. 1 .

 1 Fig. 1. Proposed architecture for the traceability of cultural heritage

Definition 1 :Definition 2 :

 12 MapReduce MR is a function of three alternating function such that: where Μ is a mapper, Λ is a reducer and Ω is a sorting function. Given a list of key value pairs

Definition 3 :

 3 Given a list of key value pairs Λ generates the same key with a new set of value list. This value list is unbound; The equation 4 defines it as below.

Fig. 4 .

 4 Fig. 4. Cultural-Object in Cultural Heritage Ontology (CHOnt)

Fig. 5 .

 5 Fig. 5. Visualization of aggregated set of cultural heritage data

Table 1 .

 1 Performance of the Proposed Architecture

		TPR	FPR	Precision	Recall	F-Measure
	Without NLP	0.479 0.063	0.457	0.479	0.46
	With NLP	0.95	0.042	0.954	0.932	0.945


Soumaya Dhib
email: sdhib@iut.univ-paris8.fr

Sid-Ali Addouche

Abderrahman El Mhamdi

Taicir Loukil
email: taicir.loukil@fsegs.run.tn

Performance study for a sustainable strategy: Case of Electrical and Electronic Equipments waste

Keywords: Reverse Logistics, Compromising Strategy, decision making model, WEEE, sustainable performance

Wastes of the Electrical and the Electronic Equipments (WEEE) have been a major danger because of their hazardous composition to the environment and to the human health. Today, their valorization within a reverse supply chain is a reality and it is a good thing. However, stakeholders are often divided on the issue and their positions and decisions about performance enhancement are sometimes ambiguous. A new strategy should look for collective decisions for improving performance considering the sustainable criteria of the economic, the environmental and the social aspects. It became necessary to develop an interactive decision making that considers the conflicting opinion to select the best compromise strategy. Nevertheless, ambiguity and collaboration decision modalities of different decision-makers are not considered in the real case. In this paper, a compromising strategy has been undertaking using an entropic analyze, ambiguity notions and cooperative theory. The proposed model has been applied in a Tunisian industry of Wastes of the Electric and the Electronic Equipment (WEEE).

Introduction

One of the most significant changes in the supply management is the growth of the environmental interest and more recently the corporate social responsibility. Thus, due to the technological development of the WEEE are increased [START_REF] Bereket | WEEE treatement strategy'evaluation using fuzzy LINMAP method[END_REF]. The WEEE must be taken back to reduce its effect on the environmental, the social and the economical consciousness.

Sustainability concept has received a growing attention into reverse supply chain, the treatment of WEEE can improve the environmental image by removing the growing waste [START_REF] Qiang | The closed loop supply chain network with competition, distribution, channel investment and uncertainties[END_REF]. However, reverse supply chain generally involves a complex returned flows due to the growth of kinds and the number of the returned products and decision makers. This can increase the number of decision making in different entity the supply chain (supplier, manufacturer, distributor, costumer…) which differ in their choice of strategy considering the criteria of sustainability performance. Generally, a set of indicators can be used to measure performance providing relevant information on the state of system in order to make an effective decision. A reverse supply chain management needs to integrate all part or decision makers to improve the sustainability with integrating a collective decision to get a compromising strategy. It involves more formal relationships, objectives and actions which are mutual, compatible and common, not necessary a centralized authority [START_REF] Gaudreault | Distributed search for supply chain coordination[END_REF].

Performance is measured by one evaluator based on their position in the assessment. For this reason, evaluators should be assigned to satisfy the majority of partners in supply chain. Thus, different agents can be causes a conflicting opinions which must be considered to select a suitable strategy. Our main objective is to provide a collective decision allows managers to choose a compromising strategy that offers an improvement in performance. Measurement of this is an essential element of the effective planning and control, as well as decision making [START_REF] Chan | A conceptual model of performance measurement for supply chains[END_REF]. The performance evolution in the supply chain is very important to sustain its effectiveness and its efficiency [START_REF] Shepherd | Measuring supply chain performance: current research and future directions[END_REF]. Consequently, the supply chain management has strategic implications which identify the required performance measures on most of the essential criteria and it should be an integral part of any strategy [START_REF] Bhagwat | Performance measurement of supply chain management: A balanced scorecard approach[END_REF].

As far as we know, the implication of the interactive decision-making is not considered literature to select the best strategy which improves the sustainability. In this study, an aided-decision model is proposed to highlight the collective solution that indicates the global strategy of the different preferences of decision makers according to the sustainable performance. This paper is organized as follows: in section 2, literature review presents the different strategies of supply chain management. Section 3 describes the importance of the sustainable performance in reverse Supply Chain. Section 4 introduces our method. In section 5, we present a real case to explain the proposed method. In section 6, we interpret the result. Finally, we present our conclusion and potential further works.

literature review

Supply chain is one of the important challenges which can improve the value of the manufacturer. The rise of management process in the supply chain is not only depending on the deployment of resources, but also by finding the performance of the whole supply chain. A large number of publications are often regarded to improve the organizational management or inter-organization which addresses the lean supply chain, the agile supply chain and the performance supply chain. These paradigms may be combined to enable highly competitive supply chains capable of winning a volatile and cost-conscious environment [START_REF] Christopher | D-R Developing market specific supply chain strategies[END_REF]. The common objective of academics and practitioners is to determine how a firm can achieve a sustainable competitive advantage [START_REF] Chang | Cooperative strategy in supply chain networks[END_REF]. To complete the supply chain level, firms must adopt an appropriate supply chain management strategy [START_REF] Sukatia | The Study of Supply Chain Management Strategy and Practices on Supply Chain Performance[END_REF].

Indeed, the lean management as a combination of measuring the intensity levels of agility enable-attributes, while other measuring methods have not under taken into accounts the measuring of the intensity levels [START_REF] Ren | Organizational competitiveness: Identifying the critical agile attributes using principal component analysis[END_REF]. The agile systems rely on flexible to response to customer when there are very short product life cycles. Corresponding to Christopher and Towill [START_REF] Christopher | D-R Developing market specific supply chain strategies[END_REF], an effective management of supply chain is based on the flexibility and the quality, which can be achieved by the dynamic partnerships and the coordination of flows. In the same way, Hang et al. [START_REF] Hang | Modeling and optimization of inserting order schedule of agile supply chain[END_REF] focus on minimizing the total cost in order to implement agile supply chain in scheduling order. To achieve a level of agility, Lin et al. [START_REF] Lin | Agility index in the supply chain[END_REF] evaluates the supply chain agility on a Taiwanese company which focuses on the application of the linguistic measurement. Aishwarya and Balaji [START_REF] Aishwarya | Supply Chain Transformation Matrix (ASCTM) based supplier-buyer relationships to improve supply chain agility[END_REF] view the point that companies need to be more agile and responsive with it's constructed a validated tool which is carried out by matrix transformation to determine the business relationship between supplier and buyer. In the purpose of developing the management process which eliminates all the wastes, lean management offers companies how to emphasize to minimize the cost with high quality and service level and low lead-time. The uses of the fundamentals of Lean management provide an added value for the customer satisfaction. Achieving quality in all steps of production system is the main goal of Sawhney et al. [START_REF] Sawhney | En-lean: a framework to align lean and green manufacturing in the metal cutting supply chain[END_REF], which can be reached by lean focuses on the environment approach. Simpson and power [17] in develop some practices of lean supplier to be adopted in the environmental practices. In the same context, Carvallo [18] applied a collaborative design from all stages of life cycle of products. Further, Dües et al.

[19] also introduce new practices which have a positive effect on the environment. An appropriate organizational culture is provided by Comm and Mathaisel [20] to change the organizational culture and communication between people. Lean and Agile management provide common elements in the same site and with some of rotation [21], which can meet the need of complex products. Moreover, the researcher and the practitioner look for various challenges from various fields which can be assessed to select the best strategy and to improve performance. Cooper et al. [22] provide coordination between activities and processes that are based on three decision levels: strategic, tactical and operational. Several measuring performance tools are based on economic performance to minimize the cost due to financial piloting indicators. In the same way, Gilmour [23] applied an organizational level to link competences to the information technology and to the organization of chain. Lamouri [24], also in his work, develops a performance model "association for Operations management model" to analyze all the possible-assembly sequences in industrial context. Bou-Lusar et al. [25] propose a model which is used as a guide to TQM (Total Quality Management) implementation. Dominique et al. [26] propose adopting Supply Chain Operation Reference (SCOR), which aimed to manage the supply chain practices by improving the performance of each system. Table 1 summarized the wellknown management strategy organized by the main objectives. However, all mentioned research works show many limitations in order to improve performance in logistic chain. The scope of this paper, proposes a decision aided model to manage the diversity of objectives considering all the aspect of sustainability. Taking into account of conflicting opinion all decision-makers in the supply chain is not her and the ambiguity of judgment.

Sustainable Performances in Reverse Supply Chain

A reverse logistics has been widely tackled because of the importance to protect the environment and to increase the economic profits due to the tack-back obligation [27]. Fleishmann [28] has defined the reverse logistic as: "the process of planning, implementing and controlling the efficient and the effective inbound flow and the storage of the secondary good and the related information which is opposite to the traditional supply chain direction for the purpose of recovering the value or the proper disposal". Indeed, a reverse supply chain process allows opportunity to improve its sustainability. The importance of the sustainability criteria in supply chain is evaluated via the performance measurement [29].

Table 1. Literature review Thus, decision maker seeks to integrate the efficient strategy that promotes the environmental protection, the economic benefits and the social satisfaction. Managing returned products with sustainable issues may be considered as business objectives. Thus, due to the performance indicators one can offer to the manufacturer an opportunity to manage the diversity of objectives and to increase its effectiveness and its efficiency.

The logic of the diversity objectives between decision makers in the same manufacture is explained by the number of activities notably in the reverse network. Furthermore, measures or performance indicators are checked in different ways. It's depending on the preference of the expert. In the reality, numerous ambiguities may be exist due to the human judgments.

Generally speaking, numerous actors have composed the reverse supply chain to get valorized products. Each decision makers involved its performance measurement Improving the agile capabilities by relating the business changes to the supplier-buyer relationship to cover its appropriate purpose. Thus, measuring a sustainable performance which is made more difficult in reverse supply chain to raise three management dimensions that may affect the whole decision, especially that no individual decision since has not been taken in this study. Based on a modified concept of making decision regarding the sustainable performance, a collaborative strategy can be applied to contribute to better decision regarding the conflicting objectives. This paper aims at developing a new model of decision making for compromising the strategy of decision makers. The main contributions of this paper are:

a) The compromising strategy for Collective decision making based on the interactive opinion.

b) The evaluation of the sustainable performance proposed to achieve a process of a continuous improvement considering the weight of indicator which depends on the decision making preferences in the context of reverse supply chain.

c) The problem of selecting the strategy of reverse supply chain coordination has been recovered by group decision makers, which provide a conflicting opinion.

d) A fuzzy method is proposed to solve the problem of ambiguity considered by the human judgment.

Methodology

This paper proposes an aided decision method that guides decision maker to provide a suitable strategy improving the sustainable performance. The proposed methodology is summarized on three steps. Firstly, a mutual influence analysis for selecting performance indicators done to find the most important indicators. Then, a fuzzy performance index has been determined to get a normalized matrix. The last steps consist on generating the different combination of strategy (evaluators / indicators).

Depending on the interactive decision making, the preferences of experts are taking into account the ambiguity in measuring performance. Thus, it is difficult to directly make a decision in the complex situation of the returned products. A specific opinion for each decision maker considered as a player is influenced by his own decision criteria. According to the preference of each decision maker /player, system will be in an inconsistent situation. Regarding to all these challenges, we propose the various steps of proposed decision system which is given as follows (Fig. 1):

Entropy analysis

Selcting sustainble indicators

Mutual influence analysis for selecting performance indicators

The most essential task of sustainable measurement is to choose the appropriate indicators that cover all the main aspects of the system. To avoid clarity and simplicity, performance system should provide information on overall on outcome [30]. A specific relationship can be defined between performance indicators "IP" and its appropriate event "ID". A cause and effect analysis can prove the degree of relation for an effective the performance couple I (IP, ID). To help evaluator in measuring performance, an entropy analysis provide which the main indicators you should choose to assess performance in the system by the cause and effect relationship between objectives and resources using a logical tool such as "If... then … Otherwise". The entropy analysis is based on the concept of theory of information and it is based exclusively an expertise during the drive. This technique could be employed to select the tangible indicators, which implies the cause and effect relationship between performance couple (IP, ID) [31]. A performance indicator could be described as in [START_REF] Bereket | WEEE treatement strategy'evaluation using fuzzy LINMAP method[END_REF]:

Ui::<IPi>,<type>,<unit>,<field> (1)
The objective Oi and the measure are expressed as in ( 2) and (3):

Oi<IPi>=<expression> (2) mi::<IPi>=<expression> (3)
A set of actions with j index could be described as in (4):

Vj::<IDj>,<objet>, <impact>,<action>,<type>,<horizon> (4)
The average mutual information I (IDj,IPi) quantified the correlation between the variable decision IDj and the performance indicator. This is what we can learn about the decision in (5):

IDj= idjg' (5)
We can obtain as in ( 6):

IPi= IPi g (6)
The decisional entropy H(IDj) can be written as follows in [START_REF] Christopher | D-R Developing market specific supply chain strategies[END_REF]:

H(IDj) = ∑g'p(idj g' ) log p(idj g' ) (7)
The calculation of mutual information average is also expressed by the following equation ( 8):

We can generate entropy conditional on the following propriety in (8):

0≤ 1(IDj; IPi ≤ H(IDj) ) (8)

4.2Fuzzy Performance Index Calculation

After selecting the relevant indicators (step 1), experts should calculate performance indicators selected. The importance of the expert's judgment differs from an indicator to another. The incorporation of expert's judgment increases the imprecision of decisions. This is as our case which is composed a wide range of views from various internal and external areas (customer, government, recycler…). However, the dimensions of partners (actors) are differing from each other due to the priority of individual objective and strategy. It is necessary to measure the weight of each expert, which can explain his preferences to the indicator. Due to the relative weight of evaluator's opinions derived from interior and exterior decisions maker can be incorporated to provide a suitable decision. The Analytical hierarchy process (AHP) is a classical method [33] is used for comparing the consistency of the decision-makers for determining the weights of criteria by a hierarchy. Based on scale of Saaty (1980) [34] which is numbered from 1 to 9, we can compare the importance of decision makers between them to get decision weights of each decision makers.

Decision weight provides the most important actor in supply chain which is used to generate all possible strategy.

Furthermore, the procedure of measuring performance can not be effective due to the lack of visibility, which is essential to provide the real performance index. In order to find the performance of the system, the ambiguity of information will influence the decision making, corresponding to the complex relationship between the entities of the logistic supply chain. These players provide several opinions and preferences that have an effect on performance measurement. Due to the appropriate objectives and strategies of expert, we assume that performance index represent the expert's measurement of each indicator. Based on the measurement scale and performance measures, we can express the ambiguity of human judgment by the fuzzy numbers. In order to quantify the ambiguity in human judgment, we propose the Fuzzy set theory. Zadeh [32] defined the fuzzy theory as an approach for an effectively dealing with the inherent imprecision, vagueness and the ambiguity of the human-decision making process. Some basic definitions of Fuzzy set theory are reviewed [32].

According to the fuzzy his theory result, we can determine the performance index of each indicators, considering the human judgment. From the scale (figure 2), we can determine fuzzy measurement result ranged from zero to ten. The relative grade of various fuzzy number, provide a fuzzy vector of performance set G= {A, B, C, D, E, F}, to calibrate the performance index into the different intervals.

In addition, the scale measurement is applied to obtain the perfect and the bottom performance value and to quantify the preference of the evaluators. The index of performance is done by the measure the score of each evaluator. For one performance index which T is the indicator number and x is the evaluator number. Thus, we obtained a normalized matrix Px (µ) that contains a measurement of an appropriate indicator assessed by each evaluator.

Best Strategy of compromise: interactive decision making

The behaviors of decision makers are often interactive to improve the sustainable performance, which is characterized by various interests in supply chain such as economics, environmental and social aspects. The objectives of the various agents are conflicting in supply chain need to be considered in selecting suitable assigning evaluators to measure indicators. With incorporating the preferences of the most important players which take into account of other and the matrix of measurement of indicators we can generate the table of all the possible combinaisons ∆F (s1, s2, s3,s4), it can represent a deffuzifed step of Px (µ) matrix. Each player/actor in this study has vectors of indicators measurement obtained from the index measurement tacked by evaluators (step 2). Based on the concept of the theory game with a 4player game, this step has been inspired to implement cooperative models that reduce the inconsistence between players. For 4-players and 3 evaluators we obtain 81 interactions as shown in table 3. The calculation of each strategy is based on a mathematical equation. Let PTx(µ) is combined matrix by x th evaluators and T th indicators. Wj T is the vector of players' weight and ∑1 4 Wj T is the average of players weight. One player makes the judgment of each evaluator to express the strategy with a relative weight of decisions makers. The strategy function can be expressed as the following:

∆ F (s1, s2, s3,s4) = P T x(µ )× ∑1 4 /Wj T (11)
Regarding the importance of the decision criteria, evaluating the performance of supply chain can be obtained by a compromising strategy [35].This study believes to select the most appropriate combinaison strategy to improve performance of supply chain. A balanced strategy is obtained by the convergence value of strategy combinations which represent the most reducer value of calculated startegy. To express the interactive relationship, a mathematical equation is used to extract the most appropriate combinaisons [35] 3 Real case: Tunisian industry Waste of Electrical and Electronic Equipments (WEEE)

The data collected from the environmental ministry of Agriculture and Environment in Tunisia concerning the waste of the Electric and the Electronic Equipment for the implementation of our proposed model. A multiple agent has composed the chain of treatment of Electrical and Electronic Equipment waste presented by: Government, Processor, Recycler, and Consumer. All players can collaborate together despite their divergent objectives, referring to the information collected in the past. Each measure is considered as a judgment strategy to be taken into account in decision making. In order to improve the sustainable performance of supply chain industries of WEEE and in order reduce the inconsistencies between the different agent's purpose considering its preferences and the ambiguity in his judgments.

Tunisian WEEE industries respect the European Union's Directives 2002/96/EC, which aim at obligating the manufacturer to increase the rate of recovery of WEEE. In the same way, it is necessary to measure the rate of satisfaction of the consumer with a measurement interval that is equal to [53,55] for an average of satisfaction, equal to 54, 4. Thus, we can define the performance indicator IPi "rate of dismantling product". We assume IPi=IP11"ratereached" and IPi=IP12 "rate is not reached". In the disassembly step, all components are in the same condition. Let IDi=ID2 and IDi=ID3are two binary variables of action and it corresponds to the dismantling id21= "active destruction". The second decision variable represents the reversed action of destruction id22= "disabled destruction". In order to determine the location of the returned products to the appropriate center, which is represented by id31= "assignment on" and the conversely action id32 = "assignment off". The main criteria of the studied supply chain are described in details in "table 3".To identify the rules of the mutual influences, we seek to define the logical rules between indicators (PIs) and variables of action (DV) for an entropy analysis to prove the effective relationship between objectives and resources. This criterion represents the main preference of the processor. To define other indicators, in order to develop the sustainability in the field of WEEE industries after a discussion with an expert.

TABLE 2. Diffrent Combinaisons of strategy

CO2of the emissions: it refers to carbon dioxide emitted during the treatment of WEEE from collected center to the final part in the chain of the valorization of waste. This criterion has a significant impact on the environment. The governmental organizations in Tunisia represented by the National Agency for Environmental Protection are responsible for not only to focusing on the effect of CO2 emission in the health of society , but they guide for the future strategic decision makers Government organization has a direct intervention by measuring the rate of CO2 emitted which is "reached" or "not reached".

Disassembly products: The waste of the Electric and the Electronic equipment refers to the unused products (broken, obsolescent, under grantee...) that will be recovered for disposal alternatives. In Tunisia, WEEE must be treated by three waste treatment strategies: reuse, recycle and disposal. Before orienting products for treatment, the processor should control dismantling operation of returned product by the rate of disassembly, in which the processor measure the product if it is able for reprocessing "Enable" or" disabled"

Recycle products: It refers to the recovery of the materiel from the returned products to generate energy to reusing for another product. A product collected for recycle should be speared depending on the composition of the waste. Therefore, the recycle is generally taken into account to the capacity of resources, which should be decided if there is "Enable or "disable" for recycle.

Customer satisfaction: One of the main objectives and which represents a social indicator improves the rate of the customer satisfaction. Thus, this indicator is depending on the quality of the valorized product in the context of WEEE, if the best quality is reach or not.

Results and Discussion

Our first step is based on the analysis of the mutual information, which we can determine the logical rules between indicators and decision variables. The validation of the recovery rate of material indicator is based on the present rules that validate the consistence between performance indicator IP and action variable ID such as in [START_REF] Hang | Modeling and optimization of inserting order schedule of agile supply chain[END_REF]: If ID2=id2 1 and ID3=id3 1 so IP1=IP1 2 If ID2=id2 2 and ID3≠ id3 1 so IP1=IP1 1 If ID2≠id2 2 and ID3= id3 2 soIP1=IP1 2 If ID2≠ d2 1 and ID3= id3 1 so IP1=IP1 2 [START_REF] Hang | Modeling and optimization of inserting order schedule of agile supply chain[END_REF] In the second step, we can determine the other indicators related to the supply chain agent by the same rule, which we can define by the players and their preferences via the selected indicators in the WEEE industries.

Player 1: represents the government (G) which promotes the environmental criteria of "CO2 emissions" Player 2: represents processor (PC) that supports the economic criteria "Rate of recovery products" Player 3: represents the Recycler (R) that promotes the economic criteria "Rate of recycle products" Player 4: represents the consumers(C), which promotes social criteria "Rate of customer satisfaction"

In the next stage, weights of important players are obtained using the AHP method. The results provided are show in table 4.The calculated weights shows that government has the most important weight reaches 0.5. Each player's preferences could be calculated to generate all the fuzzy relation preferences. We take the example of the indicator of "customer satisfaction" to determine the matrix of measurement which can be carried out by three evaluators. The calculated performance index is explained below: • Performance scale1: P3 1 (µ1) = (0, 0, 0, 0, 0.85, 0.15) P3 1 (µ1): is the measurement rate consumer satisfaction of the third evaluator. P (µ1) = P1 1 (µ1): is the rate of the recycled product measured by three evaluators, which expressed the ambiguity of the human judgment. Similarly, using the proposed method, we obtain the result of rate of: CO2 emissions, rate of disassembly, recycled products and rate of customer satisfaction which is bellow.

PA (3) =0 , P B (3) =0, PC (3) =0, PD (3)

Rate of recycle products

Rate of Co2emission

Rate of disassembly

In order to raise performance of managing of the flow of waste, decision makers should select the best strategy collected from evaluators dealing with the ambiguity of judgment. Each measurement of the evaluator is considered as a strategy. Interactive strategies are combined that are shown in "table 5" of the most important players to improve the performance. Thus, all players have an appropriate criterion which should be considered in selecting the strategy. The best result is obtained to reach the convergences of players' opinions via all strategies. Performance measurements obtained from previous step are used to compute the opinions of players for selecting the best strategy which can improve the performance of the organization. From "table 5", we can see the green case the consensus of players. The optimal choice for all payers corresponds to I for carbon emission, II for rate of disassembly product, II for rate of recycled product and I for rate of the satisfied costumer, which is founded by the opinion of the first and the second evaluator.

In order to raise the sustainable performance decision makers should select the best strategy collected from evaluators dealing with the ambiguity of judgment. Each measurement of the evaluator is considered as a strategy. Interactive strategies are combined that are shown in "table 5" of the most important players to improve the performance. Thus, all players have an appropriate criterion which should be considered in selecting the strategy. The best result is obtained to reach the convergences of players' opinions via all strategies. Performance measurements obtained from previous step are used to compute the opinions of players for selecting the best strategy which can improve the performance of the organization. From "table 5", we can see the green case the consensus of players. The optimal choice for all payers corresponds to I for carbon emission, II for rate of disassembly product, II for rate of recycled product and I for rate of the satisfied costumer, which is founded by the opinion of the first and the second evaluator.

Conclusion and further research

This paper has provided a decision aided model for improving sustainable performance. To achieve an effective performance, a cause and effect analysis can prove the degree of relation in order to obtain the most appropriate indicators that cover all the main aspects of the supply chain. Nevertheless, the waste of electric and electronic equipment (WEEE) is often characterized by the complexity to take a suitable decision. This complexity is affected by the various flows, of the returned products, of numbers of decision makers returned products. Consequently, a suitable decision that improves performance should be carried out on the collective decision.

Player 2 I

II III player1 I 0,14 0,21 0,12 0,13 0,12 0,18 0,18 0,24 0,29 0,23 0,29 0,23 0,30 0,21 0,31 0,19 0,35 0,19 0,18 0,17 0,15 0,18 0,16 0,18 0,25 0,25 0,15 0,2 0,15 0,16 0,14 0,25 0,19 0,13 0,15 0,17 0,33 0,21 0,32 0,16 0,14 0,22 0,31 0,21 0,31 0,16 0,36 0,13 0,43 0,17 0,33 0,13 0,48 0,1 0,17 0,28 0,17 0,13 0,14 0,22 0,16 0,21 0,16 0,13 0,2 0,1 0,16 0,17 0,33 0,13 0,48 0,1 0,36 0,21 0,33 0,21 0,35 0,21 0,31 0,2 0,3 0,5 0,3 0,21 0,42 0,18 0,46 0,16 0,35 0,23 0,36 0,21 0,17 0,19 0,36 0,22 0,15 0,19 0,15 0,13 0,14 0,25 0,13 0,17 0,15 0,06 0,23 0 ,08 II 0,36 0,26 0,36 0,26 0,33 0,26 0,37 0,26 0,34 0,26 0,37 0,25 0,41 0,22 0,38 0.17 0,44 0,16 0,16 0,11 0,15 0,02 0,15 0,11 0,13 0,11 0,09 0,09 0,08 0,11 0,19 0,2 0,18 0,13 0,2 0,1 0,38 0,3 0,4 0,32 0,39 0,3 0,35 0,3 0,36 0,3 0,36 0,3 0,48 0,27 0,48 0,2 0,4 0,23 0,3 0,3 0,33 0,08 0,32 0,11 0,31 0,07 0,14 0,08 0,14 0,12 0,28 0,06 0,28 0,09 0,25 0,13 0,4 0,3 0,4 0,3 0,4 0,3 0,43 0,14 0,44 0,14 0,44 0,14 0,39 0,35 0,33 0,35 0,33 0,3 0,33 0,08 0,33 0,1 0,33 0,07 0,17 0,09 0,2 0,1 0,2 0,14 0,35 0,03 0,3 0,05 0,3 0,07 III 0,31 0,25 0,31 0,2 0,31 0,2 0,29 0,25 0,4 0,19 0,41 0,17 0,27 0,25 0,36 0,19 0,47 0,21 0,29 0,2 0,22 0,08 0,22 0,11 0,29 0,2 0,22 0,11 0,19 0,02 0,3 0,22 0,23 0,18 0,21 0,13 0,33 0,21 0,33 0,16 0,33 0,16 0,3 0,16 0,33 0,16 0,32 0,11 0,41 0,17 0,36 0,22 0,26 0,16 0,22 0,16 0,23 0,08 0,23 0,11 0,22 0,16 0,21 0,09 0,17 0,08 0,19 0,09 0,14 0,14 0,18 0,11 0,44 0,16 0,34 0,16 0,16 0,34 0,3 0,16 0,33 0,07 0,12 0,03 0,43 0,18 0,53 0,16 0,42 0,14 0,15 0,12 0,16 0,07 0,11 0,09 0,15 0,12 0,16 0,07 0,11 0,11 0,18 0,14 0,21 0,09 0,19 0,1

Figure 1 .

 1 Figure 1. Structure of the proposed model.

Definition 1 : 9 )

 19 In the universe of discourses of the PG (µ), the membership function of the set G {A, B, C...F}, for each element of Px(µ) each x in X. Where Px(µ): G→[0,1].Which is described as in (9): PG(µ)={ ( Px(µ). µ) , X = A,B,C....F} (Definition 2: A triangular fuzzy number G= {Ã, B̃, C...F}.Theses grades are classified from the perfect to the worst in "Fig.2". All the triangular fuzzy numbers are summarized as follow in (10): Ã= T (8, 10, 10), B̃= T (6, 8, 10) C̃ = T (4, 6, 8), D̃= T (2, 4, 6) Ẽ=T (0,2,4), F=T(0,0,2)

  I,I,I) ∆F (I,I,I,II) ∆F (I,I,I,III) ∆F (II,I,I,I) ∆F (II,I,I,II) ∆F (II,I,I,III) ∆F (III,I,I,II) ∆F (III,I,I,II) ∆F (III,I,I,III) ∆F (I,I,II,I) ∆F (I,I,II,II) ∆F (I,I,II,III) ∆F (II,I,III,I) ∆F (II,I,II,II) ∆F (II,II,II,III) ∆F (III,I,II,II) ∆F (III,I,II,II) ∆F (III,I,II,III) ∆F (I,I,III,I) ∆F (I,I,III,II) ∆F (I,I,III,III) ∆F (II,I,III,III) ∆F (II,II,I,II) ∆F (II,II,I,III) ∆F (III,I,I,I) ∆F (III,I,I,II) ∆F (III,II,I,III) II ∆F (I,II,I,I) ∆F (I,II,I,II) ∆F (I,II,I,III) ∆F (II,II,I,I) ∆F (II,II,I,III) ∆F (II,II,I,III) ∆F (III,I,I,I) ∆F (III,I,I,II) ∆F (III,II,I,III) ∆F (I,II,II,I) ∆F (I,II,II,II) ∆F (I,II,II,III) ∆F (II,II,II,I) ∆F (II,II,II,II) ∆F (II,II,II,III) ∆F (II,II,II,I) ∆F (III,II,II,II) ∆F (III,II,II,III) ∆F (I,II,III,I) ∆F (I,II,III,II) ∆F (I,III,I,III) ∆F (II,III,I,I) ∆F (II,II,III,III) ∆F (II,II,III,III) ∆F (III,II,III,I) ∆F (III,II,III,II) ∆F (III,II,III,III) III,II,III) ∆F (II,III,II,I) ∆F (II,III,II,II) ∆F (II,III,II,III) ∆F (III,III,II,I) ∆F (III,III,II,II) ∆F (III,III,II,III) ∆F (I,III,III,I) ∆F (I,III,III,II) ∆F (I,III,III,III) ∆F (III,III,III,I) ∆F (II,III,III,II) ∆F (II,III,III,III) ∆F (III,III,III,I) ∆F (III,III,III,II) ∆F (III,III,III,II)

  =3-2/4-2=0.5, PE (3)= 4-3/4-2= 0.5, PF (3) =0, P1 1 (µ1) = (0, 0, 0, ½,½,0); P1 1 (µ1): is the measurement of the judgment of customer satisfaction rate of the first evaluator.• performance scale 2 : µ1): is the measurement of the judgment of customer satisfaction rate of the second evaluator• Performance score 3 : 55-54.4/55-53× (10-4) =1.8

TABLE 3 .

 3 Reverse supply chain of WEEE

TABLE 4 .

 4 Weights calculated of decision makers

	Returned	Center type	Dismantling type	Rate of reusable
	products			material
	Under warranty	Recycling center	Total dismantling	85% recycled material
	Damaged	Remanufacturing center	Partial dismantling	0% landfill
	Products
	hazardous	Re-distrubution	No dismantling	90% reusable material
	Products
		Collect Centre

TABLE 5 .

 5 Interactive Strategies

We propose a fuzzy set theory to calculate the preferences of evaluators without the loss of information. A strong interest to focus on the convergence of opinions of the players from various fields: economic, social and environmental. The combined of interactive relation between supply agents has been addressed to reduce the inconsistence which is inspired from the theory game. Future work could include (a) Comparing our proposed method with other decision aided-methods such as TOPSIS to validate the results (b) Developing more criteria to analyze their interaction on the performance evaluation (c) Varying the number of criteria and players and (d) undertaking the analysis of sensitivity.