Abstract
Textile manufacturing consists of yarn production, fabric formation, and finishing and dyeing stages. The subject of this paper is the yarn production planning problem, although the approach is directly applicable to the fabric production planning problem due to similarities in the respective models. Our experience at an international textile manufacturer indicates that demand uncertainty is a major challenge in developing yarn production plans. We develop a stochastic programming model that explicitly includes uncertainty in the form of discrete demand scenarios. This results in a large-scale mixed integer model that is difficult to solve with off-the-shelf commercial solvers. We develop a two-step preprocessing algorithm that improves the linear programming relaxation of the model and reduces its size, consequently improving the computational requirements. We illustrate the benefits of a stochastic programming approach over a deterministic model and share our initial application experience.
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References
Barbarosoǧlu G and Arda Y (2004). A two-stage stochastic programming framework for transportation planning in disaster response. J Opl Res Soc 55: 43–53.
Birge J and Louveaux F (1997). Introduction to Stochastic Programming. Springer-Verlag: Berlin.
Christie RM and Wu SD (2002). Semiconductor capacity planning: Stochastic modeling and computational studies. IIE Trans 34(2): 131–143.
Darby-Dowman K, Barker S, Audsley E and Parsons D (2000). A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture. J Opl Res Soc 51(1): 83–89.
Dempster MAH, Pedrón NH, Medova EA, Scott JE and Sembos A (2000). Planning logistics operations in the oil industry. J Opl Res Soc 51(11): 1271–1288.
Eppen G, Martin R and Schrage L (1989). A scenario approach to capacity planning. Opns Res 37(4): 517–527.
Escudero LF, Kamesam PV, King AJ and Wets RJ-B (1993). Production planning via scenario modeling. Ann Opns Res 43: 311–335.
Gassmann HI and Ireland AM (1995). Scenario formulation in an algebraic modeling language. Ann Opns Res 59: 45–75.
ILOG (2001). Cplex optimization software. http://www.ilog.com/products/cplex.
Jonsbråten TW (1998). Oil field optimization under price uncertainty. J Opl Res Soc 49: 811–818.
Kall P and Wallace S (1994). Stochastic Programming. Wiley: Chichester.
Karabuk S and Wu SD (2003). Coordinating strategic capacity planning in the semiconductor industry. Opns Res 51(6): 839–849.
Maatman A, Schweigman C, Ruijs A and van der Vlerk MH (2002). Modeling farmers' response to uncertain rainfall in burkina faso: A stochastic programming approach. Opns Res 50(3): 399–414.
Pochet Y (2001). Mathematical programming models and formulations for deterministic production planning models. In: Junger M and Naddef D (eds). Computational Combinatorial Optimization, Optimal or Provably Near-Optimal Solutions. Lecture Notes in Computer Science. Springer: Berlin, pp. 57–111.
Shapiro JF (1993). Mathematical programming models and methods for production planning and scheduling. In: Graves SC, Kan R and Zipkin PH (eds). Handbooks in OR & MS vol. 4. Elsevier: Amsterdam, pp. 371–443.
Thomas LJ and McClain JO (1993). An overview of production planning. In: Graves SC, Kan R and Zipkin PH (eds). Handbooks in OR & MS vol. 4. Elsevier: Amsterdam, pp. 333–370.
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Karabuk, S. Production planning under uncertainty in textile manufacturing. J Oper Res Soc 59, 510–520 (2008). https://doi.org/10.1057/palgrave.jors.2602370
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DOI: https://doi.org/10.1057/palgrave.jors.2602370