• Alexandre Dolgui
  • Dmitry Ivanov
  • Semyon Potryasaev
  • Boris Sokolov
  • Marina Ivanova
  • Frank Werner

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept ‘virtual operation’. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC.

Original languageEnglish
Pages (from-to)2184-2199
Number of pages16
JournalInternational Journal of Production Research
Volume58
Issue number7
Early online date12 Jun 2020
DOIs
StatePublished - 2020

    Research areas

  • Blockchain, cyber-physical supply chain, Industry 4.0, scheduling: flexible flow shop, smart contract, supply chain design, supply chain dynamics, supply chain engineering, SYSTEM, scheduling, flexible flow shop, TECHNOLOGY

    Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Strategy and Management
  • Management Science and Operations Research

ID: 62146043