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  3. ارائه مدل‌های ریاضی چندهدفه به منظور ترکیب و زمان بندی پایدار سرویس‌ها در تولید ابری

Multi-Objective Mathematical Models for Sustainable Service Selection and Scheduling in Cloud Manufacturing

Akbaripour Yasar, Hossein | 2018

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 51606 (01)
  4. University: Industrial Engineering Department
  5. Department: Industrial Engineering
  6. Advisor(s): Houshmand, Mahmoud
  7. Abstract:
  8. Cloud manufacturing (CMfg) as an emerging service–oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. In this study, a prospective conceptual model for CMfg was developed which can overcome or mitigate the issues and risks associated with supply chain processes on a global scale. Morever, three important problems in CMfg were investigated in this work: 1) Service Composition and Optimal Selection (SCOS), 2) Service Selection Optimization and Scheduling (SSOS), and 3) Sustainable Service Selection and Scheduling (4S). We proposed a new mixed–integer programming (MIP) model for solving the SCOS problem with the sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services were considered. In order to solve the SCOS problem, for the first time and based on analyzing the landscape of solution space, the basic Imperialist Competitive Algorithm (ICA) was hybridized with a Local Search (LS) algorithm resulting in the Hybrid ICA (HICA). The results revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function, the stability of solutions and convergence speed. We also suggested new MIP models for solving the SSOS problem with basic composition structures (i.e. sequential, parallel, loop and selective). Through incorporation of the proposed MIP models, the SSOS with a mixed composition structure can be tackled. The models also optimize routing decisions within a given hybrid hub-and-spoke transportation network. Unlike the majority of previous research undertaken in CMfg, it was assumed that manufacturing resources are not continuously available for processing but the start time and end time of their occupancy interval are known in advance. The performance of the proposed models was evaluated through solving different scenarios in the SSOS. The outcomes demonstrated that the consideration of transportation and availability not only can change the results of the SSOS significantly, but also is necessary for obtaining more realistic solutions. Finally, a new multi-objective MIP model for solving the 4S problem was proposed in which the total cost and environmental impacts are two objective functions of the model. Moreover, a hybrid solution approach based on lexicographic optimization and augmented ε-constraint methods was used for solving the model for an example of online motorcycle production. Decision maker (that can be a customer of a CMfg system) preferences are required for suitable trade-off among total costs, and environmental impacts
  9. Keywords:
  10. Cloud Manufacturing ; Service Scheduling ; Service Occupancy ; Hub and Spoke Transportation Network ; Mixed Integer Programming ; Imperialist Competitive Algorithm ; Service Composition

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