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Logistics models in information systems describe activities, organi-zations, transportation means, goods, and services being involved in logistics processes. The problem with most current such models, however, is a lack of formal semantics which prevents automated data integration across organi-zational boundaries. In this paper, we take the perspective of supply chain management and employ a well-grounded model which provides core concepts of interorganizational logistics. The contribution is that we (1) propose referring to supply chain management for ontologizing logistics models and (2) provide definitions of core elements of logistics ontologies.
... Various projects and activities aim at describing SCOR into an ontology using RDFS and OWL . The formalization of the SCOR model into an ontology is first addressed in . ...
... Various projects and activities aim at describing SCOR into an ontology using RDFS and OWL . The formalization of the SCOR model into an ontology is first addressed in . The authors analyzed the different conceptualization levels of the model and converted them into OWL classes. ...
The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach.
We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories.
... Leukel  proposed ontologies dealing with the Supply Chain Operations Model (SCOR). The supply chain is a system of entities around the manufacturers, suppliers, transporters, and customers. ...
... The existing ones are mostly focused on supply chain management . These often does not consider the complex logistic problems, like the optimization problems  in logistics. However, the current logistic ontologies has not yet achieved a consensual acceptance and maturity. ...
Logistics problems are often complex combinatorial problems. These may also implicitly refer to the processes, actors, activities, and methods concerning various aspects that need to be considered. Thus the same process may involve the processes of sale/purchase, transport/delivery, and stock management. These processes are so diverse and interconnected that it is difficult for a logistic expert to compete all of them. In this thesis, we propose the explications with the help of ontologies of conceptual ans semantic knowledge concerning the logistic processes. This explicit knowledge is then used to develop a reasoning system to guide the logistic expert for an incremental and semi-automatic construction of a software solution to an instantly posed problem. We define an ontology concerning the inter-connected logistics and associated optimization problem. We, henceforth, establish an explicit semantic link between the domains of logistics and the optimization. It may allow the logistic expert to identify precisely and unambiguously the confronted logistic problem and the associated optimization problem. The identification of the problems then leads to a process to choose the solutions ranging from the choice of the precise logistic process to be implemented to that of the method to solve the combinatorial problem until the discovery of the software component to be invoked and which is implemented by a web service. The approach we have adopted and implemented has been experimented with the "Vehicle Routing Problems", the "Passenger Train Problem" and the "Container Terminal problems".
... In this regard, most of the research work in the literature is focused on ontology definition for the analysis of logistic processes, and the simulation and modeling perspectives. Leukel specifies the logistics model as five process types such as plan, source, make, deliver, and return. Kayikci defines an ontology to recognize similarity between many knowledge written in natural languages. Hoxha discusses different functionalities involved in the logistics domain. ...
... In this regard, most of the research work in the literature is focused on ontology definition for the analysis of logistic processes, and the simulation and modeling perspectives . Leukel specifies the logistics model as five process types such as plan, source, make, deliver, and return . Kayikci defines an ontology to recognize similarity between many knowledge written in natural languages . ...
The logistic processes integrate various interdependent elements such as Vessel, Human resources, Vehicle etc. These elements inherently represent the actors, resources, and activities of a characterised process. A large variety of elements need to be managed to improve a logistics process. In this context, we propose the use of optimization methods. We develop a software framework that use the concepts of logistics and optimization to identify and specify the type of logistics problem and propose the optimal methods for their solution. The ontology definition may help to better understand the terminologies that may assist a logistic expert to specify his problem (using the logistics terms). In this paper, we present a reasoning system, based on logistics ontology. The objective of this work has been to build the shared concepts of logistics and optimization to better specify the logistic processes. Which may further lead to identify the type of logistics problem and propose list of respective solution methods. Moreover, it may also propose the web-services (that implements the corresponding solution method) to execute the evaluated logistic data. In the current work, we consider the Container Terminal Problems for the pragmatic validation of our proposed approach.
... Ainsi, ils utilisent un modèle de situation basé sur la sémantique pour spécifier une situation de produit et déterminer les événements causés par cette situation. ( Leukel et al 2008) a contribué à l'avancement des ontologies logistiques en faisant référence à la gestion de la chaîne logistique et en réutilisant le corpus de connaissances existant contenu dans le modèle SCOR (Supply Chain Operation Reference). Comme le modèle SCOR ne vise pas à couvrir complètement la logistique inter-organisationnelle, il manque à ces ontologies certains concepts caractéristiques de ce domaine. ...
Ce livre scientifique reprend les travaux de la 1ère journée de recherche MOA’2017 (Modélisation et Optimisation et Applications 2017), qui s'est tenue à l’Ecole Nationale des Sciences Appliquées de Tanger, le 4 novembre 2017 et organisée par l'Équipe de Recherche Mathématiques, Informatique et Applications (ERMIA) en partenariat avec le Département Mathématiques et Informatique (MI) de l’ENSA de Tanger.
... We can find many definitions of individual ontology, in the available literature, which are intended to explicit semantic knowledge related to logistics concepts. Hepp  uses industrial taxonomies to define large number of concepts and representation of the especially the taxonomic relationship, Leukel  defines a logistics ontology based on a kind of processes taxonomy. He considers five process types that are: plan, source, make, deliver, and return. ...
In this paper, we discuss the development of an
integrated framework dealing with the deployment of logistics
software solutions based on ontologies. The system involves the
integration of web-services to solve a particular logistics
optimization problem. Moreover, it involves the construction and
execution of a reasoning engine for an eventual identification of
optimal web-services. For instance, we consider the Vehicle
Routing Problem and its variants to validate our approach. The
framework can support a logistics engineer to query the system
and call an optimal web-service in respect of a given logistic
... Thus, they use a situation model based on semantic to specify a situation of product and determine events caused by this situation. Contribute in the advancement of logistic ontologies by referring to supply chain management and reusing the existing body of knowledge contained in the SCOR (Supply Chain Operation Reference) model. As the SCOR model does not aim at covering interorganizational logistics to the full extend, this ontologies lack some characteristic concepts of this domain. ...
Defining the concept of supply chain have been the subject of several proposals. Gathering definitions so that they can be organized and analyzed seems to be an important work at this stage of research. Many perspectives have been identified, namely functional and process oriented perspective, strategic perspective, systemic perspective, structural and network oriented perspective and relational perspective. These perspectives help shaping decisions in this complex organizational configuration. They are analyzed and discussed and a definition is proposed to cope with main elements.
The paradigm of cloud logistics is essentially built upon the virtualization of logistics resources from different logistics service providers. The virtualized resources are pooled and can subsequently be combined and encapsulated within customer-specific modular logistics services. The pooling within bigger logistics networks leads to a high quantity of different available logistics resources and services. Domain-specific structuring with the concept of the logistics service map helps to retrieve specific requested services from that quantity. The structuring of resources and services is a challenging task based on the semantic gap of differing wordings, descriptions used by different providers. The developed ontology design pattern for domain-specific structuring of logistics services can help to close the semantic gap as well as to enable the concept of the logistics service map. Structuring data and information (of services) from different providers can be made available, linked and interchanged easily within the network. Digitalized collaboration is supported and the disruptive paradigm of cloud logistics is enabled.
In recent years, the Internet of Things (IoT) becomes a promising topic of technical social and economic significance, especially with the high number of developed sensors and technologies. Logistic applications are a perfect domain of IoT as it adds new functionalities in identification, traceability, storage and real-time tracking of good in the supply chain. Handling the huge quantities of heterogeneous IoT components and logistic items is an important challenge. We present in this paper a new Internet of Things middleware architecture for logistic transport applications. This architecture focuses mainly on a semantic model that uses ontologies for sensor data representation by describing the main entities involved in the logistic scenario
Enabling communication between human beings as well as between software agents requires a minimal set of shared knowledge and language. The more the ontologies of the respective partners “overlap”, the more efficient the communication process. This paper analyses requirements for the management of distributed ontologies and exemplifies the process of ontology integration for two logistics domains, namely production logistics and hospital logistics. By simulating an adoption process of standard ontologies in a multi-agent system, we show that the topology of the community networks significantly influences the diffusion processes of languages in societies, explaining why, despite of all benefits from standardization, stable clusters of specific ontologies and languages may co-exist, although being perfect substitutes. 1.
The underlying success of logistics depends on the flow of data for effective management. The primary tool for interpreting the meaning of data includes a significant number and variety of mathematical models. In this article, we introduce Semantic Modeling, a set of computer languages and protocols that allow for the free flow of models within a network. This approach will improve the productivity of logistical modeling in practice. †Winner of the 2004 E. Grosvenor Plowman Award given by the Council of Logistics Management Previously published in the Proceedings of the Logistics Educators' Conference (Philadelphia 2004)
Mass Customization (MC) as a modern competitive strategy enables enterprises to act in dynamic markets with high customer satisfaction. But, for a successful implementation of this management concept, effective information logistics is essential. Ontologies are considered as a promising approach for optimizing inter-organizational and distributed cooperation. Although there have been several publications on ontologies and their usage we are going to show that none of these approaches can satisfy the requirements of the Mass Customization domain completely. They either do not consider requirements like natural distribution and inherent heterogeneity of all members along the supply chain nor customer needs sufficiently, which are both typical properties in this domain. In this paper, a formal conceptualization of Mass Customization scenarios within an ontology will be introduced. Doing so, we will firstly illustrate the general concept of ontologies. Then we will discuss the components of an MC-ontology and its sub-models. The originalities of a particular domain are shown for the shoe industry as an example.
Reviews literature from manufacturing strategy, flexibility, agile manufacturing, and aspects of industrial marketing and highlights fragmented and inadequate treatment of fundamental issues relating to product customisation. Through synthesis of the literature and the analysis of four case studies – in the manufacture of fork-lift trucks, electro-mechanical devices, small telecommunications systems and stationery products respectively – presents a novel model of the customisation process. Identifies typologies of customisation problem-solving situations and custom-build option types. Demonstrates the importance of the relationship between the degree of design activity and volume of manufacture, and of the distinction between products that are custom-built from options, and those that involve some custom-designed elements. Proposes a range of potential roles for customised products to support management decision making in the selection of appropriate business activities.
The use of methodologies in software and knowledge engineering is very extensive due to their important advantages. In the case of the development of ontologies, until now, several methodological proposals have been presented for building ontologies. Some of these methodologies are designed for building ontologies from scratch or reusing other ontologies without modifying them, concretely, the following cases can be mentioned: the Cyc methodology, the approach proposed by Uschold and King, Grüninger and Fox's methodology, the KACTUS methodology, METHONTOLOGY and the SENSUS methodology. There is even a proposal for re-engineering ontologies, and several proposals for collaborative construction of ontologies.
This is a comprehensive description of the Enterprise Ontology, a collection of terms and definitions relevant to business enterprises. We state its intended purposes, describe how we went about building it, define all the terms and describe our experiences in converting these into formal definitions. We then describe how we used the Enterprise Ontology and give an evaluation which compares the actual uses with original purposes. We conclude by summarising what we have learned. The Enterprise Ontology was developed within the Enterprise Project, a collaborative effort to provide a framework for enterprise modelling. The ontology was built to serve as a basis for this framework which includes methods and a computer tool set for enterprise modelling. We give an overview of the Enterprise Project, elaborate on the intended use of the ontology, and give a brief overview of the process we went through to build it. The scope of the Enterprise Ontology covers those core concepts required for the project, which will appeal to a wider audience. We present natural language definitions for all the terms, starting with the foundational concepts (e.g. entity, relationship, actor). These are used to define the main body of terms, which are divided into the following subject areas: activities, organisation, strategy and marketing. We review some of the things learned during the formalisation process of converting the natural language definitions into Ontolingua. We identify and propose solutions for what may be general problems occurring in the development of a wide range of ontologies in other domains. We then characterise in general terms the sorts of issues that will be faced when converting an informal ontology into a formal one. Finally, we describe our experiences in using the Enterprise Ontology. We compare these with the intended uses, noting our successes and failures. We conclude with an overall evaluation and summary of what we have learned.
Data about products and services is of paramount importance in most inter-organizational business processes. For business-to-business (B2B) scenarios, a great number of XML-based message specifications are available, which cover various processes and types of transactions. These specifications support the respective data exchange tasks by providing a common representation for products and services in the form of syntactical conventions with some light-weight formal semantics. However, an analysis of the underlying models shows that technical product data and commercial product data are being represented in a fundamentally different manner. In particular show the commercial models both a higher syntactical complexity and more semantic heterogeneity. In this paper, we propose to align the representation of technical and commercial views on product data in message specifications. Our approach is based on the PLIB ontology, which was originally described in ISO 13584. We have evaluated our proposal by applying it to an industrial message specification for electronic catalogs, and can show that the novel approach reduces representational mismatches in B2B processes and simplifies systems integration in such scenarios.
In B2B e-commerce, XML provides means to exchange data between applica- tions. It does not guarantee interoperability. On the syntactic level, this requires an agree- ment on an e-business vocabulary. Even more important, on the semantic level, business partners must share a common view unambiguously constraining the generic document types. In this paper, we present a framework that brings together work in the area of ontologies and work in the area of XML-based data interchange, namely ebXML. The framework uses an ontology based on ebXML corecomponents expressed in RDF to allow for bridging between different e-business vocabularies. Since a bridging mecha- nism is required, but not specified within ebXML, our approach complements ebXML. The integration of the ontology-based approach into ebXML is realized in four major steps. In this paper we exactly identify the requirements and the architecture of each step. This provides exact guidelines for future research towards implementing these steps.
Simulation might be an effective decision support tool in supply chain management. The review of supply chain simulation modeling methodologies revealed some issues one of which is the practicability of simulation in the supply chain environment. The supply chain environment is dynamic, information intensive, geographically dispersed, and heterogeneous. In order to develop usable supply chain simulation models, the models should be feasibly applicable in the supply chain environment. Distributed simulation models have been used by several researchers, however, their complexity and usability hindered their continuation. In this paper, a new approach is proposed. The approach is based on ontologies to integrate several supply chain views and models, which captures the required distributed knowledge to build simulation models. The ontology core is based on the SCOR model as the widely shared supply chain concepts. The ontology can define any supply chain and help the user to build the required simulation models
Business domain ontologies offer great opportunities for facilitating communication between people in business, for improving the enterprise system engineering processes and for creating interoperability between enterprise systems. However despite these opportunities, their use in practice is still limited. This can be partly attributed to the lack of formal representation of these ontologies. This paper proposes a structured approach which uses conceptual models as intermediary representation for formalizing business domain ontologies. The proposed methodology is used for the process level specification of the Resource Event Agent Ontology.