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A Two Criteria Objective Function Flexible Flowshop Scheduling Problem With Machine Eligibility Constraint

Tadayon, Bita | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40683 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Salmasi, Nasser
  7. Abstract:
  8. This study strives to minimize a two-criterion objective function of a flexible flow shop scheduling problem with receipt and delivery of jobs in groups and processing them individually. Every group of jobs has a release time, which means that all jobs are not available at the beginning of the planning horizon. Moreover, due to the special characteristics of every job in a group, only a subset of machines at each stage are eligible to process that job. This problem has many applications in production and service industries, such as restaurants and ceramic tile manufacturing companies. The objective function is determined based on the practical applications of the problem. It deals with minimization of sum of the completion time of groups on one hand and sum of the differences among the completion time of jobs and the delivery time of group containing that job (waiting period) on the other hand. In this research, a mathematical model has been developed for the research problem. As the problem is shown to be NP-complete, a PSO algorithm as well as a heuristic method to calculate the objective function has been proposed to solve the problem approximately. In order to strengthen the performance of PSO algorithm, a local search (LS) procedure is applied within the structure of algorithm. Based on the frequency of using this procedure, four scenarios of PSO have been developed. The first scenario (PSO1) is a pure PSO algorithm without applying any local search procedure. In the second (PSO2) and third (PSO3) scenarios, LS is applied for only some solutions. The forth scenario (PSO4) applies LS for all of the members of society within the PSO structure. These four scenarios are compared by considering two different solving time periods (short-time and long-time) and applying experimental design techniques on random test problems of different sizes (defined based on the application). The results show that PSO4 has a weak performance compared to the other three scenarios in solving large size problems. Moreover, it is proved that increasing the solution time, significantly improves the quality of PSO solutions for large size problems. In case of small size problems, there is no significant difference between the four algorithms. Increasing solution time has also no impact on the quality of solution for problems in this size.

  9. Keywords:
  10. Sequencing ; Particles Swarm Optimization (PSO) ; Flexible Flow Shop ; Scheduling ; Machine Eligibility Constraint

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