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A Multi-Product Scheduling Model Focusing on Logistic Service Sharing in Cloud Manufacturing Systems

Akhavan Hariri, Masoumeh | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51692 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Fatahi Valilai, Omid
  7. Abstract:
  8. Cloud computing is a service-driven business model that connects distributed resources and allows multiple service demaders to submit requests simultaneously to a cloud platform via the Internet. An important issue in cloud environments is how to allocate tasks to optimize the performance of a cloud manufacturing system. Allocation of tasks in cloud manufacturing refers to the process of selecting and assigning service providers to manufacturing resources such as design, engineering, machining, testing, and packing to achieve the satisfaction level in terms of time, cost, service availability, and other quality service criteria. Task allocation is an intrinsic part of the cloud manufacturing system and has a major impact on the performance of these system. In this thesis, the planning and schedulingof cloud manufacturing services are considered with consideration of simultaneous planning of logistic and operational services. By conducting a comprehensive literature review, a consiedrable research gap was consodeired related to the sequence of operations planning focusing on sharing resources and sharing costs among users of similar logistics services. Based on the fact that the sharing of logistic services affects the behavior of cloud manufacturing models, a novel model based on previous studies for allocating tasks to production resources focusing on the sharing of logistic services is proposed. This model is categorized as the assignment problems, and its solution can be achieved in short time and small sizes with definite methods, but considering the need to implement this problem in the cloud based manufacturing system and large-scale environment, the efficiency of definite algorithms will be criticized, so the solution approach in the model is to use the meta huristics algorithms with focus on the genetic algorithm. The costs of production, logistics and total cost of the proposed model were compared with the previous model (regardless of the possibility of sharing logistic services), then the sensitivity and efficiency of this model in the case study was observed by changing the effective parameters. It is observed that the proposed model is sensitive to the sharing of logistic services and the use of shared logistics services and cost sharing among users. This capability is the unique feature and ability for Cloud manufacturing system
  9. Keywords:
  10. Cloud Manufacturing ; Services Allocation ; Task Scheduling Algorithm ; Logistic Service Sharing ; Multiple Product ; Sequencing ; Services and Logestic Services Selection

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