Loading...

A Service Matching Model in Cloud Manufacturing Systems Considering the Extended Relationship Among Supply Demand Services Focusing on Metaheuristic Algorithms

Ganji, Narjess | 2019

450 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52020 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Fatahi Valilai, Omid
  7. Abstract:
  8. In recent years, economy globalization has led to a major focus on the needs of customers, Moreover globalization has resulted in geographically distributed suppliers across the globe. In such a space, using cloud manufacturing as one of the novel technologies in manufacturing that deals with resource management has been more and more taken into consideration. one of the most important issues in cloud manufacturing is service composition, in which, after receiving customer’s demand, a combination of cloud services is provided to meet customer’s demand. In fact, the customer's need is decomposed in different tasks, each of which is carried out by one (or more) manufacturing plant. the purpose of service composition problem is to reach the optimal assignment of tasks to manufacturing plants, with objectives such as minimizing cost and time and maximization of factors such as Reputation and Desire factor of suppliers. In cloud manufacturing, due to the nature of manufacturing issues we are dealing with a variety of services, including manufacturing services and transportation services. This adds up to the complexity of these types of problems. These problems are considered as NP-hard problems, and solving them with metaheuristic algorithms such as the genetic algorithm seems to be a good solution to these problems. In this research, the functionality of the genetic algorithm in large scale service composition problem, consisting of two types of manufacturing and logistic services, has been investigated. First with landscape analysis the landscape of the problem is determined. Due to landscape anlaysis results and considering the problem’s business, ideas have been discussed to improve the optimal solution and duration of proposed algorithm. Finally, the results of this approaches are being discussed. Results show that using the proposed approach can affect the optimal solution and the solving time of genetic algorithm in large scale service composition problems positively
  9. Keywords:
  10. Cloud Manufacturing ; Service Composition ; Optimization ; Genetic Algorithm ; Landscape Analysis

 Digital Object List

 Bookmark

No TOC