Abstract
In this chapter, we present optimization models for order release using exogenous planned lead times that remain constant (stationary) over the planning horizon. We describe the material flow dynamics implied by these models, beginning by assuming lead times that are integer multiples of the underlying planning period. We construct a series of linear programming models for this problem and examine their dual, noting several implications that are inconsistent with insights from the queueing models discussed in Chap. 2. We then extend this approach to consider fractional lead times and a more general formulation where a production order may consume capacity in multiple, not necessarily consecutive, periods.
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References
Baker KR (1993) Requirements planning. In: Graves SC, Kan AHGR, Zipkin PH (eds) Handbooks in operations research and management science. Logistics of production and inventory, vol 3. Elsevier Science, Amsterdam, pp 571–627
Bazaraa MS, Jarvis J, Sherali HD (2004) Linear programming and network flows. Wiley, New York
Bertsimas D, Tsitsiklis JN (1997) Introduction to linear optimization. Scientific, Athena
Billington PJ, Mcclain JO, Thomas JL (1983) Mathematical programming approaches to capacity-constrained MRP Systems: review, formulation and problem reduction. Manag Sci 29:1126–1141
Bowman EB (1956) Production scheduling by the transportation method of linear programming. Oper Res 4(1):100–103
de Kok AG, Fransoo JC (2003) Planning supply chain operations: definition and comparison of planning concepts. In: de Kok AG, Graves SC (eds) Handbooks in operations research and management science. Supply chain management: design, coordination and operation, vol 11. Elsevier, Amsterdam, pp 597–675
Hackman S (1990) An axiomatic framework of dynamic production. J Prod Anal 1:309–324
Hackman S (2008) Production economics. Springer, Berlin
Hackman S, Leachman RC (1989a) An aggregate model of project oriented production. IEEE Trans Syst Man Cybern 19(2):220–231
Hackman ST, Leachman RC (1989b) A general framework for modeling production. Manag Sci 35(4):478–495
Hanssmann F, Hess SW (1960) A linear programming approach to production and employment scheduling. Manag Technol 1(1):46–51
Hendry L, Huang Y, Stevenson M (2013) Workload control: successful implementation taking a contingency-based view of production planning and control. Int J Oper Prod Manag 33(1):69–103
Holt CC, Modigliani F, Simon HA (1955) A linear decision rule for production and employment scheduling. Manag Sci 2(1):1–30
Hopp WJ, Spearman ML (2008) Factory physics: foundations of manufacturing management. Irwin/McGraw-Hill, Boston
Jacobs FR, Berry WL, Whybark DC, Vollmann TE (2011) Manufacturing planning and control for supply chain management. McGraw-Hill Irwin, New York
Jansen B, de Jong JJ, Roos C, Terlaky T (1997) Sensitivity analysis in linear programming: just be careful! Eur J Oper Res 101(1997):15–28
Johnson LA, Montgomery DC (1974) Operations research in production planning, scheduling and inventory control. Wiley, New York
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):104–117
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
Kefeli A (2011) Production planning models with clearing functions: dual behavior and applications. Unpublished Ph.D. Dissertation. Edward P. Fitts Department of Industrial and Systems Engineering. North Carolina State University, Raleigh, NC
Koltai T, Terlaky T (2000) The difference between the managerial and mathematical interpretation of sensitivity results in linear programming. Int J Prod Econ 65:257–274
Leachman RC (2001) Semiconductor production planning. In: Pardalos PM, Resende MGC (eds) Handbook of applied optimization. Oxford University Press, New York, pp 746–762
Leachman RC, Carmon TF (1992) On capacity modeling for production planning with alternative machine types. IIE Trans 24(4):62–72
Manne AS (1957) A note on the Modigliani-Hohn production smoothing model. Manag Sci 3(4):371–379
Missbauer H (2014) From cost-oriented input-output control to stochastic programming? Some reflections on the future development of order release planning models. In: Gössinger R, Zäpfel G (eds) Management Integrativer Leistungserstellung. Festschrift für Hans Corsten. Duncker & Humblot GmbH, Berlin, pp 525–544
Missbauer H, Uzsoy R (2011) Optimization models of production planning problems. In: Planning production and inventories in the extended enterprise: a state of the art handbook. Springer, Boston, pp 437–508
Modigliani F, Hohn FE (1955) Production planning over time and the nature of the expectation and planning horizon. Econometrica 23(1):46–66
Orlicky J (1975) Material requirements planning: the new way of life in production and inventory management. McGraw-Hill, New York
Pochet Y, Wolsey LA (2006) Production planning by mixed integer programming. Springer Science and Business Media, New York
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
Rubin DS, Wagner HM (1990) Shadow prices: tips and traps for managers and instructors. Interfaces 20(4):150–157
Schneeweiss C (2003) Distributed decision making. Springer-Verlag, Berlin
Spitter JM, Hurkens CAJ, de Kok AG, Lenstra JK, Negenman EG (2005) Linear programming models with planned lead times for supply chain operations planning. Eur J Oper Res 163(3):706–720
Vollmann TE, Berry WL, Whybark DC, Jacobs FR (2005) Manufacturing planning and control for supply chain management. McGraw-Hill, New York
Voss S, Woodruff DL (2003) Introduction to computational optimization models for production planning in a supply chain. Springer, Berlin, New York
Voss S, Woodruff DL (2006) Introduction to computational optimization models for production planning in a supply chain. Springer, New York
Wight O (1970) Input-output control: a real handle on lead times. Prod Invent Manag J 11(3):9–31
Zipkin PH (2000) Foundations of inventory management. Burr Ridge, IL, Irwin
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Missbauer, H., Uzsoy, R. (2020). Planning Models with Stationary Fixed Lead Times. In: Production Planning with Capacitated Resources and Congestion. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-0354-3_5
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DOI: https://doi.org/10.1007/978-1-0716-0354-3_5
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