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New Heuristics to Stochastic Dynamic Lot Sizing Problem

Year 2009, Volume: 22 Issue: 2, 97 - 106, 22.03.2010

Abstract

Simultaneous consideration of both demand and price uncertainties is not studied extensively in the literature.  This problem is mathematically intractable for cases where complex problem structure exists.  This study proposes new heuristics that consider demand and purchasing price uncertainties simultaneously. When all the costs are constant over time, this is the classical dynamic lot sizing problem for which the optimal solution can be obtained by the Wagner-Whitin algorithm. Purchasing decisions are made on a rolling horizon basis rather than fixed planning horizon. Well-known Least Unit Cost and Silver-Meal algorithms are modified for both time varying purchasing price and rolling horizon. The proposed heuristic is basically based on a cost-benefit evaluation at decision points. A numerical example is explained for showing how heuristics are working in detail. The aim of study is to enlighten about problem that is taken into account.

 

Key Words: Inventory; Stochastic lot sizing; Rolling horizon; Simulation.,

 

 

References

  • Silver, E.A., “Inventory control under a probabilistic time-varying demand pattern”, AIIE Transactions, 10: 371-379 (1978).
  • Baker, K.R., “An experimental study of the effectiveness of rolling schedules in production planning”, Decision Science, 8 (1): 19-27 (1977).
  • Bookbinder, J.H., Tan J.Y., “Strategies for the probabilistic lot-sizing problem with service level constraints”, Management Science, 34: 1096-1108 (1988).
  • Tarim, S.A., Kingsman, B.G., “The stochastic dynamic production/inventory lot-sizing problem with service- level Production Economics, 88: 105-119 (2004). Journal of
  • Chan, G.H., Xia, Z.H., Choo, E.U., “The critical cut- off value approach for dynamic lot sizing problems with time varying cost parameters”, Computers & Operations Research, 26: 179-188 (1999).
  • Stadtler, H., “Multilevel lot sizing with setup times and
  • multiple constrained resources: internally rolling schedules with lot sizing Windows”, Operations Research, 51: 487-502 (2003).
  • Gavirneni, S., Tayur, S., “An efficient procedure for nonstationary inventory control”, IEE Transactions, 33: 83-89 (2001).
  • Iida, T., “The infinite horizon non-stationary stochastic inventory problem: Near myopic policies and weak ergodicity”, European Journal of Operational Research, 116: 405-422 (1999).
  • Martel, A., Diaby, M., Boctor, F., “Multiple items procurement under stochastic nonstationary demands”, European Journal of Operational Research, 87:74-92 (1995).
  • Sobel, M.J., Zhang, R.Q., “Inventory policies for systems with stochastic and deterministic demand”, Operations Research, 49: 157-162 (2001).
  • Sox, C.A., “Dynamic lot-sizing with random demand and non-stationary costs”, Operations Research Letters, 20: 155-164 (1997).
  • Berling, P., Rosling, K., “The effects of financial risks on inventory policy”, Management Science, 51: 1804- 1815 (2005).
  • Berling, P., “The capital cost of holding with stochastically mean- reverting purchase price”, European Journal of Operational Research, 186: 620-636 (2008).
  • Jans, R., Degraeve, Z., “Meta-Heuristics for dynamic lot sizing: A review and comparison of solution approaches”, European Journal of Operational Research, 177: 1855-1875 (2007).
  • Mula, J., Poler, R., Garcia-Sabater, J. P., Lario, F.C., “Models for production planning under uncertainty: A review”, International Journal of Production Economics, 103:271-285 (2006).

Problem

Year 2009, Volume: 22 Issue: 2, 97 - 106, 22.03.2010

Abstract

References

  • Silver, E.A., “Inventory control under a probabilistic time-varying demand pattern”, AIIE Transactions, 10: 371-379 (1978).
  • Baker, K.R., “An experimental study of the effectiveness of rolling schedules in production planning”, Decision Science, 8 (1): 19-27 (1977).
  • Bookbinder, J.H., Tan J.Y., “Strategies for the probabilistic lot-sizing problem with service level constraints”, Management Science, 34: 1096-1108 (1988).
  • Tarim, S.A., Kingsman, B.G., “The stochastic dynamic production/inventory lot-sizing problem with service- level Production Economics, 88: 105-119 (2004). Journal of
  • Chan, G.H., Xia, Z.H., Choo, E.U., “The critical cut- off value approach for dynamic lot sizing problems with time varying cost parameters”, Computers & Operations Research, 26: 179-188 (1999).
  • Stadtler, H., “Multilevel lot sizing with setup times and
  • multiple constrained resources: internally rolling schedules with lot sizing Windows”, Operations Research, 51: 487-502 (2003).
  • Gavirneni, S., Tayur, S., “An efficient procedure for nonstationary inventory control”, IEE Transactions, 33: 83-89 (2001).
  • Iida, T., “The infinite horizon non-stationary stochastic inventory problem: Near myopic policies and weak ergodicity”, European Journal of Operational Research, 116: 405-422 (1999).
  • Martel, A., Diaby, M., Boctor, F., “Multiple items procurement under stochastic nonstationary demands”, European Journal of Operational Research, 87:74-92 (1995).
  • Sobel, M.J., Zhang, R.Q., “Inventory policies for systems with stochastic and deterministic demand”, Operations Research, 49: 157-162 (2001).
  • Sox, C.A., “Dynamic lot-sizing with random demand and non-stationary costs”, Operations Research Letters, 20: 155-164 (1997).
  • Berling, P., Rosling, K., “The effects of financial risks on inventory policy”, Management Science, 51: 1804- 1815 (2005).
  • Berling, P., “The capital cost of holding with stochastically mean- reverting purchase price”, European Journal of Operational Research, 186: 620-636 (2008).
  • Jans, R., Degraeve, Z., “Meta-Heuristics for dynamic lot sizing: A review and comparison of solution approaches”, European Journal of Operational Research, 177: 1855-1875 (2007).
  • Mula, J., Poler, R., Garcia-Sabater, J. P., Lario, F.C., “Models for production planning under uncertainty: A review”, International Journal of Production Economics, 103:271-285 (2006).
There are 16 citations in total.

Details

Primary Language English
Journal Section Industrial Engineering
Authors

Ercan Senyığıt

Publication Date March 22, 2010
Published in Issue Year 2009 Volume: 22 Issue: 2

Cite

APA Senyığıt, E. (2010). New Heuristics to Stochastic Dynamic Lot Sizing Problem. Gazi University Journal of Science, 22(2), 97-106.
AMA Senyığıt E. New Heuristics to Stochastic Dynamic Lot Sizing Problem. Gazi University Journal of Science. March 2010;22(2):97-106.
Chicago Senyığıt, Ercan. “New Heuristics to Stochastic Dynamic Lot Sizing Problem”. Gazi University Journal of Science 22, no. 2 (March 2010): 97-106.
EndNote Senyığıt E (March 1, 2010) New Heuristics to Stochastic Dynamic Lot Sizing Problem. Gazi University Journal of Science 22 2 97–106.
IEEE E. Senyığıt, “New Heuristics to Stochastic Dynamic Lot Sizing Problem”, Gazi University Journal of Science, vol. 22, no. 2, pp. 97–106, 2010.
ISNAD Senyığıt, Ercan. “New Heuristics to Stochastic Dynamic Lot Sizing Problem”. Gazi University Journal of Science 22/2 (March 2010), 97-106.
JAMA Senyığıt E. New Heuristics to Stochastic Dynamic Lot Sizing Problem. Gazi University Journal of Science. 2010;22:97–106.
MLA Senyığıt, Ercan. “New Heuristics to Stochastic Dynamic Lot Sizing Problem”. Gazi University Journal of Science, vol. 22, no. 2, 2010, pp. 97-106.
Vancouver Senyığıt E. New Heuristics to Stochastic Dynamic Lot Sizing Problem. Gazi University Journal of Science. 2010;22(2):97-106.