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A Service Selection Model for Mutual Logistic and Operation Service Matching in Cloud Manufacturing System Considering Variety in Product Types

Shahkhajeh, Mahla | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51523 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Fatahi Valilai, Omid
  7. Abstract:
  8. Nowadays, global competition has challenged old manufacturing concepts for surviving in today's highly competitive industrial market. Moreover, rapid development of Information Technology advantages and their application in manufacturing systems have led to the emergence of Advanced Manufacturing Systems (AMSs). Hence, using the AMSs, can help manufacturing enterprises to survive in the current competitive environment. Cloud manufacturing is one of the most advanced manufacturing system paradigms that has attracted a wide range of researchers and companies’ attention in recent years. Cloud Manufacturing aims to provide easy and on demand access to the shared pool of manufacturing resources and capabilities through internet. In this regard, Service Composition and optimal allocation of services in cloud manufacturing is one of the most complicated problems in the field of operation research which includes the selection of appropriate shared services to complete the intended production activities and deliver the finished product to the final consumer. In addition, due to the importance of logistic consideration in supply chain such as transportation and considering the necessity of focusing on transportation and production operations simultaneously, this thesis focuses on proposing a service selection model for mutual logistic and operation service matching in cloud manufacturing system considering variety in product types and flexibility in production process by minimizing the total cost of manufacturing and transportation. The Major decision making presented by the model involves choosing the production process and operation process charts (OPC) of each product from existing charts and assigning each of the operation of the chosen operation process chart to the operational service provider and clustering operations with the objective of cost minimization. Due to the complicated nature of the problem and proposed model, the problem has been solved by applying exact methods in the case of small sized problems and using the genetic algorithm(GA) in the case of large scale ones. Conducting a sensitivity analysis on the key factors of the model including operational, logistical, fixed and variable costs of the production systems, the capability of the proposed model in terms of operational cost impact on production process and OPC selection, mutual influence of logistic costs on the manufacturing service allocation, the impact of manufacturing setup costs in convergence of the selected manufacturing services and the influence of the logistic costs on the operations clustering, has been evaluated
  9. Keywords:
  10. Cloud Manufacturing ; Service Composition ; Linear Programming ; Services and Logestic Services Selection ; Genetic Algorithm ; Cloud Service Selection

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