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An Agent-based Architecture for Reverse Logistics in Cloud Manufacturing

Hamidi Moghaddam, Simin | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 50969 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Houshmand, Mahmoud
  7. Abstract:
  8. Cloud Manufacturing (CMfg) which is one of the newest and important business models, can have a great effect in enterprises' success by sharing information and production resources among them. Due to the semi-made products which should be transferred between distributed production centers, Logistics and Transportation concept in CMfg are very important. Reverse Logistics (RL) that is the flow of used products from customer to manufacturer, by its role in recycling and proper disposal of components, has a great impact on industry effect on environment and helps enteprises to make better profit. Despite this importance, working on RL in CMfg has not been paid attention to in former researches. In this thesis, while introducing general applications related to RL that can be added to CMfg, a six layered agent-based architecture is proposed to support these applications and use from reverse flow physical services. The goal of agent-based architecture is to deal with uncertainties in RL flow regarding manufacturing cloud service providers. The introduced layered architecture considers all the steps in reverse flow and also CMfg environment needs. After that, by proposing a model based on genetic algorithm, the transportation resource allocation agent in dynamic cloud is simulated. By proposing a platform to share logistics processes among service providers in CMfg, the model integrates forward and reverse transportation processes. Considering this sharing, some aspects of separated processes are compared to the proposed integrated shared one to discuss the application of the model. The results show that the proposed model is successful in using transportation resources more efficiently and decreasing its costs for cloud as a whole. This efficiency is a result of real-time scheduling based on incoming requests and also making use of free capacity of transportation resources on their trips
  9. Keywords:
  10. Cloud Manufacturing ; Reverse Logistics ; Genetic Algorithm ; Agent Based Architecture ; Transportation Simulation

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