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A Mutual Predictive Scheduling Model for Customer Order Prioritizing & Manufacture Scheduling Inspired by Industry 4.0

Sharifisari, Amir Hossein | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 53191 (51)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Fatahi Valilai, Omid
  7. Abstract:
  8. This study, with integrated approach to the POM problem, has developed a linear mutual MILP model with the hybrid MTS / MTO operational approach on the OAS problem in industry 4.0 environment for the Shahin Plastic Manufacturing Company. This enabled an integrated PM with the production in the scheduling model. The model objective function has minimized costs and maximized the company profits. It also maximized customer satisfaction by minimizing the tardiness and earliness time in producing orders. To implement this model in the factory decision-making system, measures and modifications have been made to the MIS structure of the corporate departments. Measures include the creation of cloud infrastructure for enabling the factory from the benefit of CMfg and E-marketing subsequently. The use of CMfg and the presence of E-marketing have increased the demand fulfillment for production. The study categorized customers into two categories including regular customer and cloud customer. The CMfg has changed the approach of firm maintenance by using CPS tools and techniques from a PM to a PdM. Thereafter the model measures were incorporated into the firm's decision-making and scheduling system to create an optimal scheduling plan. The model was solved for a production scheduling horizon with using real data of Shahin Plastic Manufacturing Company and exact solution method. Two scenarios were implemented to ensure the validity and dynamics of model decision logic in acceptance and prioritization of highly economical cloud orders. The resources utilization and productivity coefficients of the company were calculated by comparing a production scheduling period related to the prior and after this research. This schedule was optimized to maximize productivity by allocating cloud orders to three parallel calender machines in the idle time period after regular orders were produced. Overall, order preparation and ordering operations as well as idle time machines were significantly reduced. Also, changing the PM approach to the PdM approach resulted in reduced calender machines maintenance operation time and reduced unnecessary maintenance operation. The value of ready time index and setup times and overall maintenance operation time were minimized. Along with the available freed up time arising from maintenance, ready and setups, the use of machine idle times also made the idle time of all calender machines minimalized and subsequently processing time of calender machines were maximized. Furthermore, time wastage was minimized and the utilization coefficient of factory production resources was maximized. As a result, factory productivity was optimized in real terms and scope of this research
  9. Keywords:
  10. Optimization ; Production Scheduling ; Mixed Integer Linear Programming ; Make-to-Stock ; Make-to-Order ; Order Acceptance and Scheduling (OAS)

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