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Parallel Machines with Batch Processing

Chaghazardy, Delaram | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 46624 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Salmasi, Nasser
  7. Abstract:
  8. Due to the undeniable importance and applicability of both "batch processing" and "parallel machines" in service and industry, this survey strives to solve a real-world problem in which some identical parallel machine, capable of processing jobs in batches, are available. In batch processing the process of jobs starts and ends simultaneously. In real world the processing time of the batch is a function of some characteristics of the jobs in the batch. in this survey, for simplicity (as calculating the mentioned function is so difficult), we assume that the processing time of a batch equals the sum of the processing time of the jobs in the batch. we also have assumed that the capacity of a batch is limited, and each job has a release time and a different size. The objective function is determined based on the practical applications of the problem. It deals with minimization of sum of the completion time of jobs. In brief, our problem is P_m |s-batch,r_j | ∑▒C_j . Three mathematical models were developed, and the last one was shown to be the best. As the problem is proved to be NP-Hard, a Hybrid PSO algorithm was proposed to solve the problem approximately. the PSO algorithm allocates jobs to machines, then calls for a proposed heuristic algorithm for sequencing and batching the jobs on each machine. In order to strengthen the performance of PSO algorithm, a local search (LS) procedure based on simulated annealing 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 (HPSO1) applies LS for all of the members of society within the PSO structure. In the second (HPSO2) and third (HPSO3) scenarios, LS is applied for only some solutions. The results show that HPSO3 has a weak performance compared to the other two scenarios in solving large size problems
  9. Keywords:
  10. Scheduling ; Parallel Machines ; Mathematical Model ; Batch Processing ; Particles Swarm Optimization (PSO)

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