A Discrete Event Simulation Model with Genetic Algorithm Optimisation for Customised Textile Production Scheduling

  • Brahmadeep
  • Sébastien ThomasseyEmail author
Part of the Springer Series in Fashion Business book series (SSFB)


This chapter aims to explain the methodology of the production schedule optimisation for the automatic manufacturing of customised textile products. The data involved in this manufacturing process are huge and constitute many parameters and constraints. The proposed system could be divided into two main modules, the optimisation model and the production floor model. Indeed, the complexity of this scenario demands a hybrid model which involves a combination of an optimisation model (genetic algorithm model) and a production simulation model (discrete event simulation) with a robust link (ActiveX/OLE Automation Server). The system forms a complex synchronised loop which replicates and improves the production schedule in process till the best results are achieved. The expected impacts are to have on-time shipment, increased productivity and profitability with the implementation of lean tools. Indeed, the implementation of this model is very vast. This would permit the use of a powerful discrete event model with an optimisation algorithm which gives numerous possibilities from manufacturing scheduling to the global supply chain, distribution and logistics planning and optimisation.


  1. Acaccia GM, Conte M, Maina D, Michelini RC, Molfino R (1999) Integrated manufacture of high standing dresses for customized satisfaction. In: Globalisation of manufacturing in the digital communication era, pp 511–523Google Scholar
  2. Acaccia GM, Conte M, Maina D, Michelini RC (2003) Computer simulation aids for the intelligent manufacture of quality clothing. Comput Ind 50:71–84CrossRefGoogle Scholar
  3. Azadivar F, Tompkins G (1999) Simulation optimization with qualitative variables and structural model changes. Eur J Oper Res 113:169–182CrossRefGoogle Scholar
  4. Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, OxfordGoogle Scholar
  5. Bigras LP, Gamache M, Savard G (2008) The time-dependent traveling salesman problem and single machine scheduling problems with sequence dependent setup times. Discret Optim 5:685–699CrossRefGoogle Scholar
  6. Brahmadeep, Thomassey S (2014) A simulation based comparison: manual and automatic distribution setup in a textile yarn rewinding unit of a yarn dyeing factory. In: Simulation modelling practice and theory, vol 45, pp 80–90CrossRefGoogle Scholar
  7. Bukchin J, Dar-El EM, Rubinovitz J (2002) Mixed model assembly line design in a make-to-order environment. Comput Ind Eng 41(4):405–421CrossRefGoogle Scholar
  8. Chen EJ, Lee YM, Selikson PL (2002) A simulation study of logistics activities in a chemical plant. Simul Model Pract Theory 10:235–245CrossRefGoogle Scholar
  9. Chen G, Harlock SC (1999) A computer simulation based scheduler for woven fabric production. Text Res J 69:431–439CrossRefGoogle Scholar
  10. Dias LS, Pereira G, Vik P, Oliveira JA (2011) Discrete simulation tools ranking: a commercial software packages comparison based on popularity. In: Proceedings of 9th annual industrial simulation conference. Industrial Simulation Conference, VeniceGoogle Scholar
  11. Edis RS, Ornek A (2009) Simulation analysis of lot streaming in job shops with transportation queue disciplines. Simul Model Pract Theory 17:442–453CrossRefGoogle Scholar
  12. Ekren BY, Ornek AM (2008) A simulation based experimental design to analyze factors affecting production flow time. Simul Model Pract Theory 16:278–293CrossRefGoogle Scholar
  13. Farahani RZ, Elahipanah M (2008) A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. Int J Prod Econ 111:229–243CrossRefGoogle Scholar
  14. Giovanni L, Pezzella F (2010) An improved genetic algorithm for the distributed and flexible job-shop scheduling problem. Eur J Oper Res 200:395–408CrossRefGoogle Scholar
  15. Gonçalves JF, Mendes JJM, Resende MGC (2008) A genetic algorithm for the resource constrained multi-project scheduling problem. Eur J Oper Res 189:1171–1190CrossRefGoogle Scholar
  16. Greasley A (2008) Using simulation for facility design: a case study. Simul Model Pract Theory 16:670–677CrossRefGoogle Scholar
  17. Guo ZX, Wong WK, Leung SYS, Fan JT, Chan SF (2013) Optimizing apparel production order planning scheduling using genetic algorithms. In: Leung S, Guo ZX, Wong WK (eds) Optimizing decision making in the apparel supply chain using artificial intelligence (AI): from production to retail. Woodhead Publishing, pp 55–80CrossRefGoogle Scholar
  18. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Michigan. Second edition (1992). The MIT Press, MassachusettsGoogle Scholar
  19. Kim T, Choi BK (2014) Production system-based simulation for backward on-line job change scheduling. Simul Model Pract Theory 40:12–27CrossRefGoogle Scholar
  20. Korytkowski P, Wisniewski T, Rymaszewski S (2013) An evolutionary simulation-based optimization approach for dispatching scheduling. Simul Model Pract Theory 35:69–85CrossRefGoogle Scholar
  21. Leung S, Wong WK (2013) Optimizing cut order planning in apparel production using evolutionary strategies. In: Leung S, Guo ZX, Wong WK (eds) Optimizing decision making in the apparel supply chain using artificial intelligence (AI): from production to retail. Woodhead Publishing, pp 81–105Google Scholar
  22. Li X, Chehade H, Yalaoui F, Amodeo L (2011) A new method coupling simulation and a hybrid metaheuristic to solve a multiobjective hybrid flowshop scheduling problem. In: Proceedings of the 7th EUSFLAT-LFA conference. Aix-les-Bains, FranceGoogle Scholar
  23. Li Y, Ip W, Wang DW (1998) Genetic algorithm approach to earliness and tardiness production. Int J Prod Econ 54:65–76CrossRefGoogle Scholar
  24. Liang S, Yao X (2008) Multi-level modeling for hybrid manufacturing systems using Arena and Matlab. In: International workshop on modelling, simulation and optimization. Hong-Kong, ChinaGoogle Scholar
  25. Michalewicz Z (1992) Genetic algorithms + data structures = evolutionary programs. Springer, New YorkCrossRefGoogle Scholar
  26. Mitchell M (1996) An introduction to genetic algorithm. The MIT Press, MassachusettsGoogle Scholar
  27. Nahmias S (2005) Production and operations analysis. McGraw-Hill, New YorkGoogle Scholar
  28. Ouabiba M, Mebarki N, Castagna P (2001) Couplage entre des methodes d’optimisation iteratives et des modeles de simulation a evenements discrets. In: 3e Conférence Francophone de MOdélisation et SIMulation, Conception, Analyse et Gestion des Systèmes Industriels. Conference MOSIM, Troyes, FranceGoogle Scholar
  29. Ôzbayrak M, Papadopoulou TC, Akgun M (2007) Systems dynamics modelling of a manufacturing supply chain system. Simul Model Pract Theory 15:1338–1355CrossRefGoogle Scholar
  30. Palencia AER, Delgadillo GEM (2012) A computer application for a bus body assembly line using genetic algorithms. Int J Prod Econ 140:431–438CrossRefGoogle Scholar
  31. Peralta RC, Forghani A, Fayad H (2014) Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow. J Hydrol 511:776–785CrossRefGoogle Scholar
  32. Salomon M, Solomon MM, Wassenhove LNV, Dumas Y (1997) Solving the discrete lotsizing and scheduling problem with sequence dependent set-up costs and set-up times using the Travelling Salesman Problem with time windows. Eur J Oper Res 100:494–513CrossRefGoogle Scholar
  33. Sepulveda JA, Akin HM (2004) Modelling a garment manufacturer’s cash flow using object-oriented simulation. In: Ingalls RG, Rossetti MD, Smith JS, Peters BA (eds) Winter simulation conference, 2004, Proceedings of the winter simulation, vol 2. Association for Computing Machinery, New York, pp 121–128Google Scholar
  34. Shi W, Shang WJ, Liu Z, Zuo X (2014) Optimal design of the auto parts supply chain for JIT operations- Sequential bifurcation factor screening and multi-response surface methodology. Eur J Oper Res 236:664–676CrossRefGoogle Scholar
  35. Thomassey S (2010) Sales forecasts in clothing industry: the key success factor of the supply chain management. Int J Prod Econ 128:470–483CrossRefGoogle Scholar
  36. Toledo CFM, Oliveira L, Pereira RF, França PM, Morabito R (2014) A genetic algorithm mathematical programming approach to solve a two-level soft drink production problem. Comput Oper Res 48:40–52CrossRefGoogle Scholar
  37. Ünal C, Tunali S, Güner M (2009) Evaluation of alternative line configurations in apparel industry using simulation. Text Res J 79:908–916CrossRefGoogle Scholar
  38. Vallada E, Ruiz R (2011) A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. Eur J Oper Res 211:612–622CrossRefGoogle Scholar
  39. Wanga J, Chang Q, Xiao G, Wang N, Li S (2011) Data driven production modeling and simulation of complex automobile general assembly plant. Comput Ind 62:765–775CrossRefGoogle Scholar
  40. Wong WK, Wang XX, Guo ZX (2013a) Optimizing marker planning in apparel production using evolutionary strategies and neural networks. In: Leung S, Guo ZX, Wong WK (eds) Optimizing decision making in the apparel supply chain using artificial intelligence (AI): from production to retail. Woodhead Publishing, pp 106–131CrossRefGoogle Scholar
  41. Wong WK, Kwong CK, Mok PY, Ip WH (2013b) Optimizing fabric spreading and cutting schedules in apparel production using genetic algorithms and fuzzy set theory. In: Leung S, Guo ZX, Wong WK (eds) Optimizing decision making in the apparel supply chain using artificial intelligence (AI): from production to retail. Woodhead Publishing, pp 132–152CrossRefGoogle Scholar
  42. Zülch G, Koruca HI, Börkircher M (2011) Simulation-supported change process for product customization: a case study in a garment company. Comput Ind 62:568–577CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.ENSAITGEMTEXRoubaix Cedex 1France

Personalised recommendations