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Blockchains are attracting the attention of stakeholders in many industrial domains, including the logistics and supply chain industries. Blockchain technology can effectively contribute in recording every single asset throughout its flow on the supply chain, contribute in tracking orders, receipts, and payments, while track digital assets such as warranties and licenses in a unified and transparent way. The paper provides, through its methodology, a detailed analysis of the blockchain fit in the supply chain industry. It defines the specific elements of blockchain that affect supply chain such as scalability, performance, consensus mechanism, privacy considerations, location proof and cost, and details on the impact that blockchains will have in disrupting the supply chain industry. Discussing the tradeoff between consensus cost, throughput and validation time it proceeds with a suggested high-level architectural approach, and concludes as a result with a discussion on changes needed and challenges faced for an in-vivo deployment of blockchains in the supply chain industry. While the technological features of modern blockchains can effectively facilitate supply chain uses cases, the various challenges that still remain, bring in front of us a wide set of needed changes and further research efforts for achieving a global, production level blockchain for the supply chain industry.

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This paper presents PORSCE II, an integrated system that performs automatic Semantic Web service composition exploiting artificial intelligence (AI) techniques, specifically planning. Essential steps in achieving Web service composition include the translation of the Web service composition problem into a solver-ready planning domain and problem, followed by the acquisition of solutions, and the translation of the solutions back to Web service terms. The solutions to the problem, that is, the descriptions of the desired composite service, are obtained by means of external domainindependent planning systems, they are visualized and finally evaluated. Throughout the entire process, the system exploits semantic information extracted from the semantic descriptions of the available Web services and the corresponding ontologies, in order to perform composition under semantic awareness and relaxation. ). Nevertheless, there are no tools utilizing semantic information incorporated in OWL-S to efficiently compose Web services either accurately or approximately, taking into account the actual meaning of Web service inputs/outputs as well as the corresponding ontologies.The PORSCE II framework aims at automated Semantic Web service composition by employing planning under semantic awareness and relaxation. Its contribution focuses on the effective utilization of semantic information present in OWL-S description of Web services to enhance the Web service composition process by facilitating approximate compositions, via planning.The first and decisive step in the process concerns the translation of the Web service composition problem to AI planning terms (Hatzi et al., 2009). This translation is based on the observation that certain correspondences exist between the two domains, which, given the appropriate mapping, enable the application of planning techniques to solve the Web service composition problem effectively. Such correspondences include the available Web services that can be combined to formulate meaningful compositions, which can be mapped to the planning domain, and user requirements about the desired composite service, which can be perceived as a planning problem over this domain.The translation takes place between the most prominent standards in each area: OWL-S for semantic description of Web services (either atomic or composite) and PDDL (Planning Domain Definition Language) (Ghallab et al., 1998) for definition of planning domain and problem. According to user preferences, the translation process may take into account semantics, resulting from the semantic analysis of the domain and the corresponding ontologies; if so, semantically equivalent or relevant concepts are also included, in order to cope with cases when no exact plans can be found and approximations must take place. The result of this phase of the transformation process is a fully formulated, solver-ready planning problem, which incorporates all the required semantic information. PORSCE II consequently exports the planning problem to PDDL and invoke...