Article Sidebar

Download
PDF

Main Article Content

Abstract

Production and distribution system in a company should be managed carefully. Delay in product delivery not only results in a late penalty due to customer dissatisfaction or breach of contract, but also causes a supply chain failure. Of course, all these impacts will also reduce the reputation of a company. Scheduling integrated production-distribution is classified as NP-Hard problem. Genetic algorithm can be used to solve complex problem. In this paper, genetic algorithm is used for scheduling production-distribution in make to order system where each job has a different deadline and volume (size). This problem is represented on mixed integer programing model. We verify the genetic algorithm’s performance by comparing the results with the total cost calculated by lower bounds of the problems. Experiments show that the traditional initial random cannot produce good result with more than 15 job size problem. We proposed guided initial chromosome to tackle this problem. From further experiments shows that the proposed method approach can increase the performances of genetic algorithm in more than 15 job size problem. In general, proposed genetic algorithm with guided initial chromosome shows better solution quality and better time efficiency compared to previous related research.

Article Details

How to Cite
Rody, R., Mahmudy, W. F., & Tama, I. P. (2019). Using Guided Initial Chromosome of Genetic Algorithm for Scheduling Production-Distribution System. Journal of Information Technology and Computer Science, 4(1), 26–32. https://doi.org/10.25126/jitecs.20194195
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Z.-L. Chen, “Integrated Production and Outbound Distribution Scheduling: Review and Extensions,†Oper. Res., vol. 58, no. 1, pp. 130–148, Feb. 2010.
  2. J. M. Garcia and S. Lozano, “Production and vehicle scheduling for ready-mix operations,†in Computers and Industrial Engineering, 2004, vol. 46, no. 4 SPEC. ISS., pp. 803–816.
  3. J. M. Garcia and S. Lozano, “Production and delivery scheduling problem with time windows,†Comput. Ind. Eng., vol. 48, no. 4, pp. 733–742, Jun. 2005.
  4. D. Wang, H. Guo, and K. Zhu, “Lot sizing and scheduling problem for production-delivery system with job volume and due date considerations to minimise the total cost while guaranteeing a certain customer service level,†Int. J. Manuf. Res., vol. 9, no. 3, p. 294, 2014.
  5. S. Suginouchi, T. Kaihara, D. Kokuryo, and S. Kuik, “A Research on Optimization Method for Integrating Component Selection and Production Scheduling under Mass Customization,†Procedia CIRP, vol. 57, pp. 527–532, 2016.
  6. D. Wang and H. Luo, “Simultaneous Lot-Sizing and Scheduling for Single-Stage Multi-product Production-Distribution System with Due Date Considerations to Minimize Total Logistics Cost,†in 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2016, vol. 1, pp. 200–203.
  7. D. Wang, O. Grunder, and A. EL Moudni, “Using genetic algorithm for lot sizing and scheduling problem with arbitrary job volumes and distinct job due date considerations,†Int. J. Syst. Sci., vol. 45, no. 8, pp. 1694–1707, Aug. 2014.
  8. M. L. Seisarrina, I. Cholissodin, and H. Nurwarsito, “Invigilator Examination Scheduling using Partial Random Injection and Adaptive Time Variant Genetic Algorithm,†J. Inf. Technol. Comput. Sci., vol. 3, no. 2, pp. 113–119, 2018.
  9. W. F. Mahmudy, “Optimisation Of Integrated Multi-Period Production Planning and Scheduling Problem In Flexible Manufacturing System (FMSs) Using Hybrid Genetic Algorithms,†University of South Australia, 2014.
  10. W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 1: Modelling and representation,†in 2013 5th International Conference on Knowledge and Smart Technology (KST), 2013, pp. 75–80.