Time-Varying Lead Times and Iterative Multi-Model Approaches

  • Hubert Missbauer
  • Reha Uzsoy


The planning models in the previous chapter assume the planned lead times to be workload-independent, exogenous parameters that remain constant over the entire planning horizon. We now consider models with exogenous lead times that vary over time, seeking to accommodate time-varying levels of resource utilization. Since, as discussed in Chap.  2, cycle times depend on capacity utilization, which is determined by release decisions, obtaining time-varying estimates of lead time parameters requires observation or prediction of resource utilization across the time periods in the planning horizon. This tight linkage of utilization and cycle time suggests that releases and lead times should be jointly determined, i.e., the lead times should be endogenous to the model.


  1. Albey E, Bilge Ü (2011) A hierarchical approach to FMS planning and control with simulation-based capacity anticipation. Int J Prod Res 49(11):3319–3342CrossRefGoogle Scholar
  2. Albey E, Bilge U (2014) An improved iterative linear programming-simulation approach for production planning. Department of Industrial Engineering, Ozyegin University, IstanbulGoogle Scholar
  3. Armbruster D, Uzsoy R (2012) Continuous dynamic models, clearing functions, and discrete-event simulation in aggregate production planning. INFORMS Tutorials in Operations ResearchGoogle Scholar
  4. Bang JY, Kim YD (2010) Hierarchical production planning for semiconductor wafer fabrication based on linear programming and discrete-event simulation. IEEE Trans Autom Sci Eng 7(2):326–336CrossRefGoogle Scholar
  5. Bazaraa MS, Sherali HD, Jarvis J (1979) Nonlinear programming: theory and algorithms. Wiley, New YorkGoogle Scholar
  6. Ben-Daya M, Raouf A (1994) Inventory models involving lead time as a decision variable. J Oper Res Soc 45(5):579–582CrossRefGoogle Scholar
  7. Bertsimas D, Mourtzinou G (1997) Transient laws of non-stationary queueing systems and their applications. Queue Syst 25:115–155CrossRefGoogle Scholar
  8. Byrne MD, Bakir MA (1999) Production planning using a hybrid simulation-analytical approach. Int J Prod Econ 59:305–311CrossRefGoogle Scholar
  9. Byrne MD, Hossain MM (2005) Production planning: an improved hybrid approach. Int J Prod Econ 93-94:225–229CrossRefGoogle Scholar
  10. Carey M (1992) Nonconvexity of the dynamic traffic assignment problem. Transport Res B 26B(2):127–133CrossRefGoogle Scholar
  11. Carey M, Subrahmanian E (2000) An approach to modelling time-varying flows on congested networks. Transport Res B 34:157–183CrossRefGoogle Scholar
  12. Cheng M, Mukherjee NJ, Sarin SC (2013) A review of lot streaming. Int J Prod Res 51(23/24):7023–7046CrossRefGoogle Scholar
  13. Dauzere-Peres S, Lasserre JB (2002) On the importance of sequencing decisions in production planning and scheduling. Int Trans Oper Res 9:779–793CrossRefGoogle Scholar
  14. Ehteshami B, Petrakian R, Shabe P (1992) Trade-offs in cycle time management: hot lots. IEEE Trans Semicond Manuf 5(2):101–106CrossRefGoogle Scholar
  15. Figueira G, Almada-Lobo B (2014) Hybrid simulation–optimization methods: a taxonomy and discussion. Simul Model Pract Theor 46:118–134CrossRefGoogle Scholar
  16. Fu MC (2002) Optimization for simulation: theory vs practice. INFORMS J Comput 14(3):192–215CrossRefGoogle Scholar
  17. Gong L, de Kok T, Ding J (1994) Optimal leadtimes planning in a serial production system. Manag Sci 40(5):629–632CrossRefGoogle Scholar
  18. Graves SC (2011) In: Kempf KG, Keskinocak P, Uzsoy R (eds) Uncertainty and Production Planning. Planning Production and Inventories in the Extended Enterprise: A State of the Art Handbook, Volume 1, International Series in Operations Research and Management Science, vol 151. Springer, New York and Heidelberg, pp 83–101CrossRefGoogle Scholar
  19. Hackman S (2008) Production economics: integrating the microeconomic and engineering perspectives. Springer, BerlinGoogle Scholar
  20. Hackman ST, Leachman RC (1989) A general framework for modeling production. Manag Sci 35(4):478–495CrossRefGoogle Scholar
  21. Henderson SG, Nelson BL eds (2006) Simulation. In: Handbooks in operations research and management science. North-Holland, AmsterdamGoogle Scholar
  22. Hopp WJ, Spearman ML (2008) Factory physics: foundations of manufacturing management. Irwin/McGraw-Hill, BostonGoogle Scholar
  23. Hopp WJ, Sturgis MLR (2000) Quoting manufacturing due dates subject to a service level constraint. IIE Trans 32(9):771–784Google Scholar
  24. Hung YF, Hou MC (2001) A production planning approach based on iterations of linear programming optimization and flow time prediction. J Chin Inst Indus Eng 18(3):55–67Google Scholar
  25. Hung YF, Leachman RC (1996) A production planning methodology for semiconductor manufacturing based on iterative simulation and linear programming calculations. IEEE Trans Semicond Manuf 9(2):257–269CrossRefGoogle Scholar
  26. Hung YF, Leachman RC (1999) Reduced simulation models of wafer fabrication facilities. Int J Prod Res 37(12):2685–2701CrossRefGoogle Scholar
  27. Ioannou G, Dimitriou S (2012) Lead time estimation in MRP/ERP for make-to-order manufacturing systems. Int J Prod Econ 139(2):551–563CrossRefGoogle Scholar
  28. Irdem DF, Kacar NB, Uzsoy R (2008) An experimental study of an iterative simulation-optimization algorithm for production planning. In: Mason SJ, Hill R, Moench L, Rose O (eds) 2008 Winter Simulation Conference, Miami, FLGoogle Scholar
  29. Irdem DF, Kacar NB, Uzsoy R (2010) An exploratory analysis of two iterative linear programming-simulation approaches for production planning. IEEE Trans Semicond Manuf 23:442–455CrossRefGoogle Scholar
  30. Jonsson P, Matsson SA (2006) A longitudinal study of material planning applications in manufacturing companies. Int J Oper Prod Manag 26(9):971–995CrossRefGoogle Scholar
  31. Kacar NB, Uzsoy R (2015) Estimating clearing functions for production resources using simulation optimization. IEEE Trans Autom Sci Eng 12(2):539–552CrossRefGoogle Scholar
  32. Kacar NB, Irdem DF, Uzsoy R (2012) An experimental comparison of production planning using clearing functions and iterative linear programming-simulation algorithms. IEEE Trans Semicond Manuf 25(1):104–117CrossRefGoogle Scholar
  33. Kanet JJ, Sridharan V (1998) The value of using scheduling information in planning material requirements. Decis Sci 29(2):479–496CrossRefGoogle Scholar
  34. Kayton D, Teyner T, Schwartz C, Uzsoy R (1997) Focusing maintenance improvement efforts in a wafer fabrication facility operating under theory of constraints. Prod Invent Manag 38(Fourth Quarter):51–57Google Scholar
  35. Keskinocak P, Tayur S (2004) Due-date management policies. In: Simchi-Levi D, Wu SD, Shen ZM (eds) Supply chain analysis in the e-business era: handbook of quantitative supply chain analysis. Kluwer Academic, DordrechtGoogle Scholar
  36. Kim B, Kim S (2001) Extended model for a hybrid production planning approach. Int J Prod Econ 73:165–173CrossRefGoogle Scholar
  37. Kim SH, Lee YH (2016) Synchronized production planning and scheduling in semiconductor fabrication. Comput Indus Eng 96:72–85CrossRefGoogle Scholar
  38. Lautenschläger M (1999) Mittelfristige Produktionsprogrammplanung mit auslastungsabhängigen Vorlaufzeiten. Peter Lang, Frankfurt am MainGoogle Scholar
  39. Law AM, Kelton WD (2000) Simulation modeling and analysis, 3rd edn. McGraw Hill, New YorkGoogle Scholar
  40. Law AM, Kelton WD (2004) Simulation modeling and analysis. McGraw-Hill, New YorkGoogle Scholar
  41. Leachman RC, Carmon TF (1992) On capacity modeling for production planning with alternative machine types. IIE Trans 24(4):62–72CrossRefGoogle Scholar
  42. Li M, Yang F, Uzsoy R, Xu J (2016) A metamodel-based monte carlo simulation approach for responsive production planning of manufacturing systems. J Manuf Syst 38:114–133CrossRefGoogle Scholar
  43. Liu J, Li C, Yang F, Wan H, Uzsoy R (2011) Production planning for semiconductor manufacturing via simulation optimization. In: Jain S, Creasey RR, Himmelspach J, White KP, Fu R (eds) Winter simulation conferemce. IEEE, Piscataway, NJGoogle Scholar
  44. Lu S, Ramaswamy D, Kumar PR (1994) Efficient scheduling policies to reduce mean and variance of cycle time in semiconductor plants. IEEE Trans Semicond Manuf 7:374–388CrossRefGoogle Scholar
  45. Milne RJ, Mahapatra S, Wang C-T (2015) Optimizing planned lead times for enhancing performance of MRP systems. Int J Prod Econ 167:220–231CrossRefGoogle Scholar
  46. Missbauer H (2020) Order release planning by iterative simulation and linear programming: theoretical foundation and analysis of its shortcomings. Eur J Oper Res 280:495–507CrossRefGoogle Scholar
  47. Morton TE, Pentico D (1993) Heuristic scheduling systems: with applications to production systems and project management. Wiley, New YorkGoogle Scholar
  48. Narahari Y, Khan LM (1997) Modeling the effect of hot lots in semiconductor manufacturing systems. IEEE Trans Semicond Manuf 10(1):185–188CrossRefGoogle Scholar
  49. Negenman EG (2000) Material coordination under capacity constraints. Industrial engineering. Eindhoven University of Technology, EindhovenGoogle Scholar
  50. Neuts MF (1981) Matrix-geometric solutions in stochastic models. Johns Hopkins University Press, Baltimore, MDGoogle Scholar
  51. O’Regan D, Meehan M, Agarwal RP (2001) Contractions. In: Fixed point theory and applications. Cambridge University Press, Cambridge, pp 1–11Google Scholar
  52. Orcun S, Uzsoy R (2011) The effects of production planning on the dynamic behavior of a simple supply chain: an experimental study. In: Kempf KG, Keskinocak P, Uzsoy R (eds) Planning in the extended enterprise: a state of the art handbook. Springer, Berlin, pp 43–80Google Scholar
  53. Ozturk A, Kayaligil S, Ozdemirel NE (2006) Manufacturing lead time estimation using data mining. Eur J Oper Res 173:683–700CrossRefGoogle Scholar
  54. Peeta S, Ziliaskopoulos AK (2001) Foundations of dynamic traffic assignment: the past, the present and the future. Netw Spat Econ 1(3-4):233–265CrossRefGoogle Scholar
  55. Riaño G (2003) Transient behavior of stochastic networks: application to production planning with load-dependent lead times. School of Industrial and Systems Engineering. Georgia Institute of Technology, Atlanta, GAGoogle Scholar
  56. Riaño G, Hackman S, Serfozo R (2006) Transient behavior of queueing networks. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GAGoogle Scholar
  57. Shanthikumar JG, Sargent RG (1983) A unifying view of hybrid simulation/analytic models and modeling. Oper Res 31(6):1030–1052CrossRefGoogle Scholar
  58. Shortle JF, Thompson JM, Gross D, Harris CM (2018) Fundamentals of queueing theory. Wiley, Hoboken, NJCrossRefGoogle Scholar
  59. Vepsalainen AP, Morton TE (1988) Improving local priority rules with global lead-time estimates: a simulation study. J Manuf Oper Manag 1:102–118Google Scholar
  60. Vollmann T, Berry W, Whybark D (1997) Manufacturing planning and control systems. Irwin, BostonGoogle Scholar
  61. Voss S, Woodruff DL (2003) Introduction to computational optimization models for production planning in a supply chain. Springer, New YorkCrossRefGoogle Scholar
  62. Zaepfel G (1984) Systemanalytische Konzeption der Produktionsplanung und –steuerung für Betriebe der Fertigungsindustrie. In: Zink C (ed) Sozio-Technologische Systemgestaltung als Zukunftsaufgabe, (in German). Carl Hanser Verlag, MunichGoogle Scholar
  63. Zapata JC, Pekny J, Reklaitis GV (2011) Simulation-optimization in support of tactical and strategic enterprise decisions. In: Kempf KG, Keskinocak P, Uzsoy R (eds) Planning production and inventories in the extemnded enterprise: a state of the art handbook, vol 1. Springer, New York, pp 593–628CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Hubert Missbauer
    • 1
  • Reha Uzsoy
    • 2
  1. 1.Department of Information Systems, Production and Logistics ManagementUniversity of InnsbruckInnsbruckAustria
  2. 2.Edward P. Fitts Department of Industrial and Systems EngineeringNorth Carolina State UniversityRaleighUSA

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