Abstract
The previous chapters have motivated the need for more advanced anticipation functions that can reflect, at least to a reasonable level of accuracy, the nonlinear relations between the workload of a production resource and its expected throughput. Whatever their academic interest, one would hope that the clearing function formalism could provide new insights or performance advantages over the models based on fixed exogenous lead times described in Chap. 5. This chapter presents several studies where clearing functions have been applied to different problems related to production systems. In several cases, the use of clearing functions provides interesting insights that would be difficult to obtain using the conventional approach of exogenous planned lead times and maximum capacity loading.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The authors thank Stefan Häussler for his contribution to this idea.
References
Albey E, Uzsoy R (2015) Lead time modeling in production planning. In: Winter simulation conference, IEEE, Huntington Beach
Albey E, Uzsoy R (2016) A chance constraint based multi-item production planning model using simulation optimization. In: Winter simulation conference, Arlington
Albey E, Norouzi A, Kempf KG, Uzsoy R (2015) Demand modeling with forecast evolution: an application to production planning. IEEE Trans Semicond Manuf 28(3):374–384
Anzanello MJ, Fogliatto FS (2011) Learning curve models and applications: literature review and research directions. Int J Ind Ergon 41:573–583
Aouam T, Uzsoy R (2012) An Exploratory Analysis of Production Planning in the Face of Stochastic Demand and Workload-Dependent Lead Times. Decision Policies for Production Networks. K. G. Kempf and D. Armbruster. Boston, Springer: 173–208
Aouam T, Uzsoy R (2015) Zero-order production planning models with stochastic demand and workload-dependent lead times. Int J Prod Res 53(6):1–19
Asmundsson J, Rardin RL, Uzsoy R (2006) Tractable nonlinear production planning models for semiconductor wafer fabrication facilities. IEEE Trans Semicond Manuf 19(1):95–111
Asmundsson J, Rardin RL, Turkseven CH, Uzsoy R (2009) Production planning with resources subject to congestion. Nav Res Logist 56(2):142–157
Baker KR (1977) An experimental study of the effectiveness of rolling schedules in production planning. Decis Sci 8:19–27
Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53
Birge JR, Louveaux F (1997) Introduction to stochastic programming. Springer, New York
Blackburn JD, Millen RA (1980) Heuristic lot-sizing performance in a rolling-schedule environment. Decis Sci 11(4):691–701
Blackburn JD, Kropp DH, Millen RA (1985) MRP system nervousness: causes and cures. Eng Costs Prod Econ 9(1–3):141–146
Chand S (1982) A note on dynamic lot sizing in a rolling-horizon environment. Decis Sci 13(1):113–119
Chand S, Morton TE (1986) Minimal forecast horizon procedures for dynamic lot size models. Nav Res Logist 33:111–122
Denardo EV, Lee C-Y (1991) Error bound for the dynamic lot size model with backlogging. Ann Oper Res 28(1):213–227
Dolgui A, Prodhon C (2007) Supply planning under uncertainties in MRP environments: a state of the art. Annu Rev Control 31:269–279
Dolgui A, Ben Ammar O, Hnaien F, Louly MA (2013) A state of the art on supply planning and inventory control under lead time uncertainty. Stud Inf Control 22(3):255–268
Fowler J, Robinson J (2012). https://www.sim.uni-hannover.de/~Svs/Wise0809/Pds/Masmlab/Factory_Data-Sets/
Fu MC (ed) (2015) Handbook of simulation optimization. Springer, New York
Gopalswamy K, Uzsoy R (2019) A data-driven iterative refinement approach for estimating clearing functions from simulation models of production systems. Int J Prod Res 57(19):6013–6030
Graves SC, Meal H, Dasu S, Qui Y (1986) Two-stage production planning in a dynamic environment. In: Axsater S, Schneeweiss C, Silver E (eds) Multi-stage inventory planning and control. Springer, Berlin
Graves SC, Kletter DB, Hetzel WB (1998) Dynamic model for requirements planning with application to supply chain optimization. Oper Res 46(3):35–49
Häussler S, Missbauer H (2019) Comparison of two optimization-based order release models with fixed and variable lead times. Department of Information Systems, Production and Logistics Management, University of Innsbruck, Innsbruck
Heath DC, Jackson PL (1994) Modeling the evolution of demand forecasts with application to safety stock analysis in production/distribution systems. IIE Trans 26(3):17–30
Holt CC, Modigliani F, Muth JF, Simon HA (1960) Planning production, inventories and work force. Prentice Hall, Englewood Cliffs
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):257–269
Kacar NB, Uzsoy R (2015) Estimating clearing functions for production resources using simulation optimization. IEEE Trans Autom Sci Eng 12(2):539–552
Kacar NB, Moench L, Uzsoy R (2013) Planning wafer starts using nonlinear clearing functions: a large-scale experiment. IEEE Trans Semicond Manuf 26(4):602–612
Kacar NB, Moench L, Uzsoy R (2016) Modelling cycle times in production planning models for wafer fabrication. IEEE Trans Semicond Manuf 29(2):153–167
Karmarkar US (1989) Capacity loading and release planning with work-in-progress (WIP) and lead-times. J Manuf Oper Manag 2(1):105–123
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):51–57
Kim S, Uzsoy R (2008a) Exact and approximate algorithms for capacity expansion problems with congestion. IIE Trans Schedul Logist 40(12):1185–1197
Kim S, Uzsoy RM (2008b) Integrated planning of production and engineering process improvement. IEEE Trans Semicond Manuf 21(3):390–398
Kim S, Uzsoy R (2013) Modeling and analysis of integrated planning of production and engineering process improvement. IEEE Trans Semicond Manuf 26(3):414–422
Kunreuther H (1971) Production-Planning Algorithms for the Inventory-Overtime Tradeoff. Operations Research 19(7):1717–1729
Kunreuther HC, Morton TE (1973) Planning Horizons for Production Smoothing with Deterministic Demands. Management Science 20(1):110–125
Leachman RC, Ding S (2007) Integration of speed economics into decision-making for manufacturing management. Int J Prod Econ 107:39–55
Lin PC, Uzsoy R (2016a) Chance-constrained formulations in rolling horizon production planning: an experimental study. Int J Prod Res 54(13):3927–3942
Lin PC, Uzsoy R (2016b) Estimating the costs of planned changes implied by freezing production plans. In: Rabadi G (ed) Heuristics, metaheuristics and approximate methods in planning and scheduling. Springer, New York, pp 17–44
Lundin RA, Morton TE (1975) Planning horizons for the dynamic lot size model: zabel vs. protective procedures and computational results. Oper Res 23(4):711–734
Manda, A. B. and R. Uzsoy (2018). Simulation optimization for planning product transitions in semiconductor manufacturing facilities. In: Rabe M, Juan AA, Mustafee N, Skoogh A, Jain S, Johansson B. Wither simulation conference. IEEE, Gothenburg
Manda AB, Uzsoy R, Kempf KG, Kim S (2016) Modeling the impact of new product introduction on the output of semiconductor wafer fabrication facilities. In: Winter simulation conference, Arlington
Miller LW (1979) Using Linear Programming to Derive Planning Horizons for a Production Smoothing Problem. Management Science 25(12):1232–1244
Modigliani F, Hohn FE (1955) Production Planning over Time and the Nature of the Expectation and Planning Horizon. Econometrica 23(1):46–66
Mula J, Poler R, Garcia-Sabater JP, Lario FC (2006) Models for production planning under uncertainty: a review. Int J Prod Econ 103:271–285
Norouzi, A. (2013). The effect of forecast evolution on production planning with resources subject to congestion. PhD, E. P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh
Norouzi A, Uzsoy R (2014) Modeling the evolution of dependency between demands, with application to production planning. IIE Trans 46(1):55–66
Orcun S, Uzsoy R, Kempf KG (2009) An integrated production planning model with load-dependent Lead-times and safety stocks. Comput Chem Eng 33(12):2159–2163
Pürgstaller P (2009) Ein Vergleich von Regel- und Optimierungsbasierten Bestandsregelungskonzepten bei Kundenauftragsfertigung. PhD, University of Innsbruck, Innsbruck
Pürgstaller P, Missbauer H (2012) Rule-based vs. optimization-based order release in workload control: a simulation study of an MTO manufacturer. Int J Prod Econ 140:670–680
Sahin F, Narayanan A, Robinson EP (2013) Rolling horizon planning in supply chains: review, implications and directions for future research. Int J Prod Res 51(18):5413–5436
Srinivasan A, Carey M, Morton TE (1988) Resource pricing and aggregate scheduling in manufacturing systems. Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh
Stadtler H (2000) Improved rolling schedules for the dynamic single-level lot sizing problem. Manag Sci 46:318–326
Turkseven CH (2005) Computational evaluation of production planning formulations using clearing functions. School of Industrial Engineering, Purdue University, West Lafayette
Upasani A, Uzsoy R (2008) Incorporating manufacturing lead times in joint production-marketing models: a review and further directions. Ann Oper Res 161:171–188
Upasani A, Uzsoy R (2014) Integrated production planning and pricing decisions in congestion-prone capacitated production systems. In: Pulat SP, Sarin SC, Uzsoy R (eds) Essays in planning, scheduling and optimization: a festschrift in honor of Prof. S. E. Elmaghraby. Springer, New York
Van den Heuvel W, Wagelmans APM (2005) A comparison of methods for lot sizing in a rolling horizon environment. Oper Res Lett 33:486–496
Wijngaard J (2004) The effect of foreknowledge of demand in case of a restricted capacity: the single-stage, single-product case. Eur J Oper Res 159:95–109
Wijngaard J, Karaesmen F (2007) Advance Demand Information and a Restricted Production Capacity: On the Optimality of Order Base-Stock Policies. OR Spectrum 29(4):643–660
Yelle LE (1979) The learning curve: historical review and comprehensive survey. Decis Sci 10:302–329
Ziarnetzky T, Moench L, Uzsoy R (2018) Rolling horizon, multiproduct production planning with chance constraints and forecast evolution for wafer fabs. Int J Prod Res 56(18):6112–6134
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this chapter
Cite this chapter
Missbauer, H., Uzsoy, R. (2020). Applications of Clearing Functions. In: Production Planning with Capacitated Resources and Congestion. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-0354-3_10
Download citation
DOI: https://doi.org/10.1007/978-1-0716-0354-3_10
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-0716-0352-9
Online ISBN: 978-1-0716-0354-3
eBook Packages: Business and ManagementBusiness and Management (R0)