Optimization Models of Production Planning Problems

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 151)


Production planning problems have been formulated and solved as optimization problems since the early 1950s, and an extensive literature has been developed. The widespread use of Enterprise Resource Planning systems and developments in information technology and scientific computing have opened the way for even wider use of these techniques in industry. In this chapter, we review the basic formulations that have been the mainstay of academic research and industrial practice for the last five decades, assess their strengths and weaknesses, and discuss a number of interesting new directions that have emerged recently. We especially focus on models that support decisions on production volumes and order release over time and highlight the related issues on determining planned lead times.


Lead Time Planning Period Work Center Safety Stock Order Release 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research of Reha Uzsoy was supported by the National Science Foundation under Grant DMI-0556136, by the Intel Research Council, by a software grant from Dash Optimization and an equipment grant from Intel Corporation.


  1. Agnew C (1976) Dynamic modeling and control of some congestion prone systems. Oper Res 24(3):400–419CrossRefGoogle Scholar
  2. Andersson H, Axsater S et al. (1981) Hierarchical material requirements planning. Int J Prod Res 19(1):45–57CrossRefGoogle Scholar
  3. Anli OM, Caramanis M et al. (2007). Tractable supply chain production planning modeling non-linear lead time and quality of service constraints. J Manuf Syst 26(2):116–134CrossRefGoogle Scholar
  4. Anthony RN (1966) Planning and control systems: a framework for analysis. Harvard University Press, CambridgeGoogle Scholar
  5. Asmundsson JM, Rardin RL et al. (2006) Tractable nonlinear production planning models for semiconductor wafer fabrication facilities. IEEE Trans Semicond Manuf 19:95–111CrossRefGoogle Scholar
  6. Asmundsson JM, Rardin RL et al. (2009) Production planning models with resources subject to congestion. Naval Res Log 56:142–157CrossRefGoogle Scholar
  7. Baker KR (1993) Requirements planning. In: Graves SC, Rinnooy Kan AHG, Zipkin PH. Logistics of production and inventory. Handbooks in operations research and management science, vol 3. Elsevier Science, Amsterdam, pp 571–627Google Scholar
  8. Bergamaschi D, Cigolini R et al. (1997) Order review and release strategies in a job shop environment: a review and a classification. Int J Prod Res 35:399–420CrossRefGoogle Scholar
  9. Bermon S, Hood SJ (1999) Capacity optimization planning system (CAPS). Interfaces 29(5):31–50CrossRefGoogle Scholar
  10. Bertrand JWM, Wortmann JC (1981) Production control and information systems for component-manufacturing shops. Elsevier, AmsterdamGoogle Scholar
  11. Bertrand JWM, Wortmann JC et al. (1990) Production control: a structural and design oriented approach. Elsevier, AmsterdamGoogle Scholar
  12. Bertsimas D, Gamarnik D et al. (2003) From fluid relaxations to practical algorithms for high-multiplicity job shop scheduling: the holding cost objective. Oper Res 51(5):798–813CrossRefGoogle Scholar
  13. Bertsimas D, Sethuraman J (2002) From fluid relaxations to practical algorithms for job shop scheduling: the makespan objective. Math Program Series A 92:61–102CrossRefGoogle Scholar
  14. Birge JR, Louveaux F (1997) Introduction to stochastic programming. Springer, New YorkGoogle Scholar
  15. Bitran GR, Haas EA et al. (1981) Hierarchical production planning: a single stage system. Oper Res 29(4):717–743CrossRefGoogle Scholar
  16. Bitran GR, Haas EA et al. (1982) Hierarchical production planning: a two-stage system. Oper Res 30(2):232–251CrossRefGoogle Scholar
  17. Bitran GR, Tirupati D (1993) Hierarchical production planning. Graves SC, Rinnooy Kan AHG, Zipkin PH Logistics of production and inventory. Handbooks in operations research and management science, vol. 4. Elsevier Science, Amsterdam, pp 523–568Google Scholar
  18. Blau RA (1974) Stochastic programming and decision analysis: an apparent dilemma. Manage Sci 21(3):271–276CrossRefGoogle Scholar
  19. Bookbinder JH, H’ng BT (1986) Rolling horizon production planning for probabilistic time-varying demands. Int J Prod Res 24(6):1439–1458CrossRefGoogle Scholar
  20. Bookbinder JH, Tan JY (1988) Strategies for the probabilistic lot sizing problem with service level constraints. Manage Sci 34(9):1096–1108CrossRefGoogle Scholar
  21. Bowman EB (1956) Production scheduling by the transportation method of linear programming. Oper Res 4(1):100–103CrossRefGoogle Scholar
  22. Buzacott JA, Shanthikumar JG (1993) Stochastic models of manufacturing systems. Prentice-Hall, Englewood CliffsGoogle Scholar
  23. Byrne MD, Bakir MA (1999) Production planning using a hybrid simulation-analytical approach. Int J Prod Econ 59:305–311CrossRefGoogle Scholar
  24. Byrne MD, Hossain MM (2005) Production planning: an improved hybrid approach. Int J Prod Econ 93–94:225–229CrossRefGoogle Scholar
  25. Caramanis M, Pan H et al. (2001) A closed-loop approach to efficient and stable supply chain coordination in complex stochastic manufacturing. American Control Conference, Arlington, VA, 1381–1388Google Scholar
  26. Carey M (1987) Optimal time-varying flows on congested networks. Oper Res 35(1):58–69CrossRefGoogle Scholar
  27. Carey M, Subrahmanian E (2000) An approach to modelling time-varying flows on congested networks. Transp Res B 34:157–183CrossRefGoogle Scholar
  28. Cassidy M (2003) Traffic flow and capacity. In: Hall RW (ed) Handbook of transportation science. Kluwer Academic, Dordrecht, pp 155–191Google Scholar
  29. Charnes A, Cooper WW (1963) Deterministic equivalents for optimizing and satisficing under chance constraints. Oper Res 11:18–39CrossRefGoogle Scholar
  30. Charnes A, Cooper WW (1983) Response to “decision problems under risk and chance constrained programming: dilemmas in the transition”. Manage Sci 29(6):750–753CrossRefGoogle Scholar
  31. Charnes A, Cooper WW et al. (1955) A model for optimizing production by reference to cost surrogates. Econometrica 23(3):307–323CrossRefGoogle Scholar
  32. Chen HB, Mandelbaum A (1991) Hierarchical modelling of stochastic networks part I: fluid models. In: Yao DD (ed) Stochastic modeling and analysis of manufacturing systems. Springer, New YorkGoogle Scholar
  33. Cohen JW (1969) The Single server queue. North-Holland, AmsterdamGoogle Scholar
  34. Cohen O (1988) The drum-buffer-rope (DBR) approach to logistics. In: Rolstadas A (ed) Computer-aided production management. Springer, New YorkGoogle Scholar
  35. Davidson R, MacKinnon JG (1993) Estimation and inference in econometrics. Oxford University Press, New YorkGoogle Scholar
  36. de Kok AG, Fransoo JC (2003) Planning supply chain operations: definition and comparison of planning concepts. In: de Kok AG, Graves SC (eds) OR Handbook on supply chain management. Elsevier, Amsterdam, pp 597–675Google Scholar
  37. Dessouky MM, Leachman RC (1997) Dynamic models of production with multiple operations and general processing times. J Oper Res Soc 48(6):647–654Google Scholar
  38. Drexl A, Kimms A (1997) Lot sizing and scheduling – survey and extensions. Eur J Oper Res 99:221–235CrossRefGoogle Scholar
  39. Elmaghraby SE (1978) The economic lot scheduling problem (ELSP): review and extensions. Manage Sci 24:587–598CrossRefGoogle Scholar
  40. Eppen G, Martin RK (1988) Determining safety stock in the presence of stochastic lead times. Manage Sci 34:1380–1390CrossRefGoogle Scholar
  41. Fine CH, Graves SC (1989) A tactical planning model for manufacturing subcomponents of mainframe computers. J Manuf Oper Manage 2:4–34Google Scholar
  42. Forrester JW (1962) Industrial dynamics. MIT Press, CambridgeGoogle Scholar
  43. Fredendall LD, Ojha D, Patterson W (2010) Concerning the theory of workload control. Eur J Oper Res 201:99–111CrossRefGoogle Scholar
  44. Gfrerer H, Zäpfel G (1995) Hierarchical model for production planning in the case of uncertain demand. Eur J Oper Res 86:142–161CrossRefGoogle Scholar
  45. Graves SC (1981) A review of production scheduling. Oper Res 29(4):646–675CrossRefGoogle Scholar
  46. Graves SC (1986) A tactical planning model for a job shop. Oper Res 34:552–533CrossRefGoogle Scholar
  47. Graves SC (1988) Safety stocks in manufacturing systems. J Manuf Oper Manage 1:67–101Google Scholar
  48. Gunther HO, Van Beek P (2003) Advanced planning and scheduling solutions in process industry. Springer, HeidelbergGoogle Scholar
  49. Gupta M (2005) Constraints management – recent advances and practices. Int J Prod Res 41(4):647–659CrossRefGoogle Scholar
  50. Gupta SK, Sengupta JK (1977) Decision rules in production planning under chance-constrained sales. Decision Sci 8:521–533CrossRefGoogle Scholar
  51. Hackman S (2008) Production economics. Springer, BerlinGoogle Scholar
  52. Hackman ST, Leachman RC (1989) A general framework for modeling production. Manage Sci 35:478–495CrossRefGoogle Scholar
  53. Hanssmann F, Hess SW (1960) A linear programming approach to production and employment scheduling. Manage Technol 1(1):46–51Google Scholar
  54. Harris FW (1915) Operations and cost. Factory management series. Shaw, ChicagoGoogle Scholar
  55. Hax AC, Candea D (1984) Production and inventory management. Prentice-Hall, Englewood CliffsGoogle Scholar
  56. Haxholdt C, Larsen ER et al. (2003) Mode locking and chaos in a deterministic queueing model with feedback. Manage Sci 49(6):816–830CrossRefGoogle Scholar
  57. Hendry LC, Kingsman BG (1991) A decision support system for job release in make to order companies. Int J Oper Prod Manage 11:6–16CrossRefGoogle Scholar
  58. Hogan AJ, Morris JG et al. (1981) Decision problems under risk and chance constrained programming: dilemmas in the transition. Manage Sci 27(6):698–716CrossRefGoogle Scholar
  59. Holt CC, Modigliani F et al. (1955) A linear decision rule for production and employment scheduling. Manage Sci 2(1):1–30CrossRefGoogle Scholar
  60. Holt CC, Modigliani F et al. (1956) Derivation of a linear rule for production and employment. Manage Sci 2(2):159–177CrossRefGoogle Scholar
  61. Holt CC, Modigliani F et al. (1960) Planning production, inventories and work force. Prentice Hall, Englewood CliffsGoogle Scholar
  62. Hopp WJ, Spearman ML (2001) Factory physics: foundations of manufacturing management. Irwin/McGraw-Hill, BostonGoogle Scholar
  63. Hung YF, Chang CB (1999) Determining safety stocks for production planning in uncertain manufacturing. Int J Prod Econ 58:199–208CrossRefGoogle Scholar
  64. Hung YF, Cheng GJ (2002) Hybrid capacity modelling for alternative machine types in linear programming production planning. IIE Trans 34:157–165Google Scholar
  65. Hung YF, Hou MC (2001) A production planning approach based on iterations of linear programming optimization and flow time prediction. J Chinese Inst Ind Engrs 18(3):55–67CrossRefGoogle Scholar
  66. Hung YF, Leachman RC (1996) A production planning methodology for semiconductor manufacturing based on iterative simulation and linear programming calculations. IEEE Trans Semicond Manufac 9(2):257–269CrossRefGoogle Scholar
  67. Hung YF, Wang QZ (1997) A new formulation technique for alternative material planning – an approach for semiconductor bin allocation. Comput Ind Eng 32(2):281–297CrossRefGoogle Scholar
  68. Hwang S, Uzsoy R (2005) A single stage multi-product dynamic lot sizing model with work in process and congestion. Research report, Laboratory for Extended Enterprises at Purdue, School of Industrial Engineering, Purdue University, West LafayetteGoogle Scholar
  69. Irastorza JC, Deane RH (1974) A loading and balancing methodology for job shop control. AIIE Trans 6(4):302–307Google Scholar
  70. Irdem DF, Kacar NB et al. (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
  71. Jackson JR (1955) Scheduling a production line to minimize maximum tardiness. University of California, Los AngelesGoogle Scholar
  72. Jackson JR (1957) Networks of waiting lines. Opeartions Research 10(4):518–521CrossRefGoogle Scholar
  73. Johnson LA, Montgomery DC (1974) Operations research in production planning, scheduling and inventory control. Wiley, New YorkGoogle Scholar
  74. Kanet JJ (1988) Load-limited order release in job shop scheduling systems. J Oper Manage 7:413–422CrossRefGoogle Scholar
  75. Karmarkar US (1987) Lot sizes, lead times and in-process inventories. Manage Sci 33(3):409–418CrossRefGoogle Scholar
  76. Karmarkar US (1989) Capacity loading and release planning with work-in-progress (WIP) and lead-times. J Manufac Oper Manage 2:105–123Google Scholar
  77. Karmarkar US (1993) Manufacturing lead-times, order release and capacity loading. In: Graves SC, Rinnooy Kan AHG, Zipkin PH (eds) Logistics of production and inventory. Handbooks in operations research & management science, vol. 4. North-Holland, Amsterdam, pp 287–329Google Scholar
  78. Karmarkar US, Kekre S et al. (1985a) Lotsizing in multimachine job shops. IIE Trans 13(3): 290–298CrossRefGoogle Scholar
  79. Karmarkar US, Kekre S et al. (1985b) Lot sizing and lead time performance in a manufacturing cell. Interfaces 15(2):1–9CrossRefGoogle Scholar
  80. Kekre S (1984) The effect of number of items processed at a facility on manufacturing lead time. Working paper series. University of Rochester, RochesterGoogle Scholar
  81. Kekre S (1987) Performance of a manufacturing cell with increased product mix. IIE Trans 19(3):329–339CrossRefGoogle Scholar
  82. Kim B, Kim S (2001) Extended model for a hybrid production planning approach. International J Prod Econ 73:165–173CrossRefGoogle Scholar
  83. Kim JS, Leachman RC (1994) Decomposition method application to a large scale linear programming WIP projection model. Eur J Oper Res 74:152–160CrossRefGoogle Scholar
  84. Kim JS, Leachman RC et al. (1996) Dynamic release control policy for the semiconductor wafer fabrication lines. J Oper Res Soc 47(12):1516–1525Google Scholar
  85. Kistner KP (1999) Lot sizing and queueing models: some remarks on Karmarkar’s model. In: Leopold-Wildburger U, Feichtinger G, Kistner HP (eds) Modelling and Decisions in Economics: Essays in Honor of Franz Ferschl. Physica, Heidelberg, pp 173–188Google Scholar
  86. Kleinrock L (1976) Queueing systems volume II: computer system applications. Wiley, New YorkGoogle Scholar
  87. Koopmans T (ed) (1951) Activity analysis of production and allocation. Wiley, New YorkGoogle Scholar
  88. Krämer W, Langenbach-Belz M (1976) Approximate formulae for the delay in queueing system GI/G/1. 8th International telegraphic congress. Melbourne, pp 235/1–235/8Google Scholar
  89. Lambrecht MR, Chen S et al. (1996) A Lot sizing model with queueing delays: the issue of safety time. Eur J Oper Res 89:269–276CrossRefGoogle Scholar
  90. Lambrecht MR, Luyten R et al. (1984a) Protective inventories and bottlenecks in production systems. Eur J Oper Res 22:319–328CrossRefGoogle Scholar
  91. Lambrecht MR, Muckstadt JA et al. (1984b) Protective stocks in multi-stage production systems. Int J Prod Res 22:1001–1025CrossRefGoogle Scholar
  92. Land M (2004) Workload control in job shops, grasping the tap. Labyrinth, RidderkerkGoogle Scholar
  93. Lasserre JB, Mercé C (1990) Robust hierarchical production planning under uncertainty. Ann Oper Res 26(4):73–87CrossRefGoogle Scholar
  94. Lautenschläger M (1999) Mittelfristige Produktionsprogrammplanung mit auslastungsabhängigen Vorlaufzeiten. Peter Lang, Frankfurt am MainGoogle Scholar
  95. Lautenschläger M, Stadtler H (1998) Modelling lead times depending on capacity utilization. Research report, Technische Universitat DarmstadtGoogle Scholar
  96. Leachman RC (1993) Modeling techniques for automated production planning in the semiconductor industry. In: Ciriani TA, Leachman RC (eds) Optimization in industry: mathematical programming and modelling techniques in practice. Wiley, New York, pp 1–30Google Scholar
  97. Leachman RC, Benson RF et al. (1996) IMPReSS: an automated production planning and delivery quotation system at Harris corporation – semiconductor sector. Interfaces 26:6–37CrossRefGoogle Scholar
  98. Leachman RC, Carmon TF (1992) On capacity modeling for production planning with alternative machine types. IIE Trans 24(4):62–72CrossRefGoogle Scholar
  99. Lejeune MA, Prekopa A (2005) Approximations for and convexity of probabilistically constrained problems with random right hand sides. RUTCOR research report. Rutgers University, New JerseyGoogle Scholar
  100. Liu L, Liu X et al. (2004) Analysis and optimization of multi-stage inventory queues. Manage Sci 50:365–380CrossRefGoogle Scholar
  101. Lu S, Ramaswamy D et al. (1994) Efficient scheduling policies to reduce mean and variance of cycle time in semiconductor plants. IEEE Trans Semicond Manufac 7:374–388CrossRefGoogle Scholar
  102. Luss H (1982) Operations research and capacity expansion problems: a survey. Oper Res 30(5):907–947CrossRefGoogle Scholar
  103. Manne AS (1957) A note on the Modigliani-Hohn production smoothing model. Manage Sci 3(4):371–379CrossRefGoogle Scholar
  104. Manne AS (1960) On the job-shop scheduling problem. Oper Res 8(2):219–223CrossRefGoogle Scholar
  105. Medhi J (1991) Stochastic models in queuing theory. Academic, AmsterdamGoogle Scholar
  106. Merchant DK, Nemhauser GL (1978a) A model and an algorithm for the dynamic traffic assignment problems. Transp Sci 12(3):183–199CrossRefGoogle Scholar
  107. Merchant DK, Nemhauser GL (1978b) Optimality conditions for a dynamic traffic assignment model. Transp Sci 12(3):200–207CrossRefGoogle Scholar
  108. Missbauer H (1997) Order release and sequence-dependent setup times. Int J Prod Econ 49:131–143CrossRefGoogle Scholar
  109. Missbauer H (1998) Bestandsregelung als Basis für eine Neugestaltung von PPS-Systemen. Physica, HeidelbergGoogle Scholar
  110. Missbauer H (1999) Die Implikationen durchlauforientierter Losgrößenbildung für die Komplexität der Produktionsplanung und –steuerung. Zeitschrift für Betriebswirtschaft 69(2): 245–265Google Scholar
  111. Missbauer H (2002a) Aggregate order release planning for time-varying demand. Int J Prod Res 40:688–718CrossRefGoogle Scholar
  112. Missbauer H (2002b) Lot sizing in workload control systems. Prod Plan Control 13:649–664CrossRefGoogle Scholar
  113. Missbauer H (2009) Models of the transient behaviour of production units to optimize the aggregate material flow. Int J Prod Econ 118(2):387–397CrossRefGoogle Scholar
  114. Missbauer H (forthcoming) Order release planning with clearing functions: a queueing-theoretical analysis of the clearing function concept. Int J Prod EconGoogle Scholar
  115. Missbauer H, Hauber W et al. (forthcoming). Developing a computerized scheduling system for the steelmaking - continuous casting process. In: Kempf KG, Keskinocak P, Uzsoy R (eds) Planning in the extended enterprise: a state of the art handbook. Springer, New YorkGoogle Scholar
  116. Modigliani F, Hohn FE (1955) Production planning over time and the nature of the expectation and planning horizon. Econometrica 23(1):46–66CrossRefGoogle Scholar
  117. Neuts MF (1981) Matrix-geometric solutions in stochastic models. Johns Hopkins University Press, BaltimoreGoogle Scholar
  118. Nyhuis P, Wiendahl HP (2003) Logistische Kennlinien. Springer, BerlinGoogle Scholar
  119. Orcun S, Uzsoy R et al. (2006) Using system dynamics simulations to compare capacity models for production planning. Winter simulation conference. Monterey, CAGoogle Scholar
  120. Orlicky J (1975) Material requirements planning: the new way of life in production and inventory management. McGraw-Hill, New YorkGoogle Scholar
  121. Pahl J, Voss S et al. (2005) Production planning with load dependent lead times. 4OR 3:257–302Google Scholar
  122. Parker RG (1995) Deterministic scheduling theory. Chapman and Hall, LondonGoogle Scholar
  123. Parrish SH (1987) Extensions to a model for tactical planning in a job shop environment. Operations Research Center. Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  124. Peeta S, Ziliaskopoulos AK (2001) Foundations of dynamic traffic assignment: the past, the present and the future. Network Spatial Econ 1(3–4):233–265CrossRefGoogle Scholar
  125. Perona M, Portioli A (1998) The impact of parameters setting in load oriented manufacturing control. Int J Prod Econ 55(133–142)Google Scholar
  126. Peters RJ, Boskma K et al. (1977) Stochastic programming in production planning: a case with non-simple recourse. Statistica Neerlandica 31:113–126CrossRefGoogle Scholar
  127. Philipoom RR, Fry TD (1992) Capacity based order review/release strategies to improve manufacturing performance. Int J Prod Res 30:2559–2572CrossRefGoogle Scholar
  128. Pinedo M (1995) Scheduling theory, algorithms, and systems. Prentice-Hall, New JerseyGoogle Scholar
  129. Pinedo M, Chao X (2005) Planning and scheduling in manufacturing and services. Springer, New YorkGoogle Scholar
  130. Powell SG, Schultz KL (2004) Throughput in serial lines with state-dependent behaviour. Manage Sci 50(8):1095–1105CrossRefGoogle Scholar
  131. Prekopa A (1993) Programming under probabilistic constraint and maximizing a probability under constraints. Center for operations Research, Rutgers University, New BrunswickGoogle Scholar
  132. Rakes TR, Franz LS et al. (1984) Aggregate production planning using chance-constrained goal programming. Int J Prod Res 22(4):673–684CrossRefGoogle Scholar
  133. 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, AtlantaGoogle Scholar
  134. Riaño G, Hackman S et al. (2006) Transient behavior of queueing networks. School of Industrial and Systems Engineering, Georgia Institute of Technology, AtlantaGoogle Scholar
  135. Riaño G, Serfozo R et al. (2003) Benchmarking of a stochastic production planning model in a simulation testbed. Winter simulation conferenceGoogle Scholar
  136. Schneeweiß C (2003) Distributed decision making. Springer, BerlinGoogle Scholar
  137. Selçuk B (2007) Dynamic performance of hierarchical planning systems: modeling and evaluation with dynamic planned lead times. Technische Universiteit Eindhoven, EindhovenGoogle Scholar
  138. Selçuk B, Fransoo JC et al. (2007) Work in process clearing in supply chain operations planning. IIE Trans 40:206–220CrossRefGoogle Scholar
  139. Sengupta JK (1972) Decision rules in stochastic programming under dynamic economic models. Swed J Econ 74:370–389CrossRefGoogle Scholar
  140. Sengupta JK, Portillo-Campbell JH (1973) A reliability programming approach to production planning. Int Stat Rev 41:115–127CrossRefGoogle Scholar
  141. Singhal J, Singhal K (2007) Holt, Modigliani, Muth and Simon’s work and its role in the renaissance and and evolution of operations management. J Oper Manage 25:300–309CrossRefGoogle Scholar
  142. Smith SF (1993) Knowledge-based production management: approaches, results and prospects. Prod Plan Control 3(4):350–380CrossRefGoogle Scholar
  143. Spearman ML (1991) An analytic congestion model for closed production systems with IFR processing times. Manage Sci 37(8):1015–1029CrossRefGoogle Scholar
  144. Spearman ML, Woodruff DL et al. (1990) CONWIP: a pull alternative to Kanban. Int J Prod Res 28(5):879–894CrossRefGoogle Scholar
  145. Spitter JM, de Kok AG et al. (2005a) Timing production in LP models in a rolling schedule. Int J Prod Econ 93–94:319–329CrossRefGoogle Scholar
  146. Spitter JM, Hurkens CAJ et al. (2005b) Linear programming models with planned lead times for supply chain operations planning. Eur J Oper Res 163:706–720CrossRefGoogle Scholar
  147. Srinivasan A, Carey M et al. (1988) Resource pricing and aggregate scheduling in manufacturing systems. Graduate School of Industrial Administration, Carnegie-Mellon University, PittsburghGoogle Scholar
  148. Stadtler H (1996) Hierarchische Produktionsplanung. Handwörterbuch der Produktionswirtschaft. Schäffer-Poeschel, Stuttgart, pp 631–641Google Scholar
  149. Stadtler H, Kilger C (eds) (2008) Supply chain management and advanced planning: concepts, models, software and case studies. Springer-Verlag, BerlinGoogle Scholar
  150. Stange K (1964) Die Anlauflösung für den einfachen exponentiellen Bedienungskanal (mit beliebig vielen Warteplätzen), der für t=0 leer ist. Unternehmenforschung 8:1–24CrossRefGoogle Scholar
  151. Stevenson M, Hendry LC (2006) Aggregate load-oriented workload control: a review and a re-classification of a key approach. Int J Prod Econ 104(2):676–693CrossRefGoogle Scholar
  152. Tang L, Liu J et al. (2001) A review of planning and scheduling systems and methods for integrated steel production. Eur J Oper Res 133:1–20CrossRefGoogle Scholar
  153. Tardif V, Spearman ML (1997) Diagnostic scheduling in finite-capacity production environments. Comput Ind Eng 32:867–878CrossRefGoogle Scholar
  154. Tatsiopoulos IP, Kingsman BP (1983) Lead time management. Eur J Oper Res 14:351–358CrossRefGoogle Scholar
  155. Tijms HC (1994) Stochastic models: an algorithmic approach. Wiley, New YorkGoogle Scholar
  156. Uzsoy R, Lee CY et al. (1994) A review of production planning and scheduling models in the semiconductor industry part II: shop-floor control. IIE Trans Scheduling Logistics 26:44–55Google Scholar
  157. Van Ooijen HPG (1996) Load-based work-order release and its effectiveness on delivery performance improvement. Eindhoven University of Technology, EindhovenGoogle Scholar
  158. Van Ooijen HPG, Bertrand JWM (2003) The effects of a simple arrival rate control policy on throughput and work-in-process in production systems with workload dependent processing rates. Int J Prod Econ 85(1):61–68CrossRefGoogle Scholar
  159. Vaughan TS (2006) Lot size effects on process lead time, lead time demand, and safety stock. Int J Prod Econ 100:1–9CrossRefGoogle Scholar
  160. Vepsalainen AP, Morton TE (1987) Priority rules for job shops with weighted tardiness costs. Manage Sci 33(8):1035–1047CrossRefGoogle Scholar
  161. Vepsalainen AP, Morton TE (1988) Improving local priority rules with global lead-time estimates: a simulation study. J Manufac Oper Manage 1:102–118Google Scholar
  162. Vollmann TE, Berry WL et al. (1988) Manufacturing planning and control systems. Richard D. Irwin, BostonGoogle Scholar
  163. Vollmann TE, Berry WL et al. (2005) Manufacturing planning and control for supply chain management. McGraw-Hill, New YorkGoogle Scholar
  164. Voss S, Woodruff DL (2003) Introduction to computational optimization models for production planning in a supply chain. Springer, BerlinGoogle Scholar
  165. Wagner HM, Whitin TM (1958) Dynamic version of the economic lot size model dynamic version of the economic lot size model. Manage Sci 5:89–96CrossRefGoogle Scholar
  166. Wiendahl HP (1995) Load oriented manufacturing control. Springer, HeidelbergGoogle Scholar
  167. Wight O (1983) MRPII: unlocking America’s productivity potential. Oliver Wight, WillistonGoogle Scholar
  168. Wijngaard J, Wortmann JC (1985) MRP and inventories. Eur J Oper Res 20:281–293CrossRefGoogle Scholar
  169. Yano CA, Carlson RC (1988) Safety stocks for assembly systems with fixed production intervals. J Manufac Oper Manage 1:182–201Google Scholar
  170. Zäpfel G, Missbauer H (1993a) Production planning and control (PPC) systems including load-oriented order release – problems and research perspectives. Int J Prod Econ 30:107–122CrossRefGoogle Scholar
  171. Zäpfel G, Missbauer H (1993b) New concepts for production planning and control. Eur J Oper Res 67:297–320CrossRefGoogle Scholar
  172. Zäpfel G, Missbauer Hetal.(1992)PPS-Systeme mit belastungs orientierter Auftragsfreigabe– Operationscharakteristika und Möglichkeiten zur Weiterentwicklung. Zeitschrift für Betriebswirtschaft 62:897–919Google Scholar
  173. Zipkin PH (1986) Models for design and control of stochastic, multi-item batch production systems. Oper Res 34(1):91–104CrossRefGoogle Scholar
  174. Zipkin PH (1997) Foundations of inventory management. Irwin, Burr RidgeGoogle Scholar
  175. Zweben M, Fox M (eds) (1994) Intelligent scheduling systems. Morgan Kaufman, San FranciscoGoogle Scholar

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Information Systems, Production and Logistics ManagementUniversity of InnsbruckInnsbruckAustria

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