Mass Customization and “Forecasting Options’ Penetration Rates Problem”

Published Online:https://doi.org/10.1287/opre.2018.1795

References

  • Amilhastre J, Fargier H, Marquis P (2002) Consistency restoration and explanations in dynamic CSPs—application to configuration. Artificial Intelligence 135(1):199–234.CrossrefGoogle Scholar
  • Bach F (2013) Learning with submodular functions: A convex optimization perspective. Foundations Trends Machine Learn. 6(2/3):145–373.CrossrefGoogle Scholar
  • Balseiro SR, Feldman J, Mirrokni V, Muthukrishnan S (2014) Yield optimization of display advertising with ad exchange. Management Sci. 60(12):2886–2907.LinkGoogle Scholar
  • Barker VE, O’Connor DE, Bachant J, Soloway E (1989) Expert systems for configuration at digital: XCON and beyond. Comm. ACM 32(3):298–318.CrossrefGoogle Scholar
  • Bertsimas D, Tsitsiklis JN (1997) Introduction to Linear Optimization, vol. 6 (Athena Scientific, Belmont, MA).Google Scholar
  • Caro F, Gallien J (2007) Dynamic assortment with demand learning for seasonal consumer goods. Management Sci. 53(2):276–292.LinkGoogle Scholar
  • Chandru V, Hooker J (2011) Optimization Methods for Logical Inference, Wiley Series in Discrete Mathematics and Optimization, vol. 34 (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Chen Z, Wang L (2010) Personalized product configuration rules with dual formulations: A method to proactively leverage mass confusion. Expert Systems Appl. 37(1):383–392.CrossrefGoogle Scholar
  • Clarkson KL (2010) Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm. ACM Trans. Algorithms (TALG) 6(4):1–63.CrossrefGoogle Scholar
  • Dahllof V, Jonsson P, Wahlstrom M (2005) Counting models for 2SAT and 3SAT formulae. Theoret. Comput. Sci. 332(1):265–291.CrossrefGoogle Scholar
  • Demyanov V, Rubinov A (1970) Approximate Methods in Optimization Problems (American Elsevier Publishing, New York).Google Scholar
  • Feitzinger E, Lee HL (1997) Mass customization at Hewlett-Packard: The power of postponement. Harvard Bus. Rev. 75:116–123.Google Scholar
  • Finger M, De Bona G (2011) Probabilistic satisfiability: Logic-based algorithms and phase transition. Proc. 22nd Internat. Joint Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 528–533.Google Scholar
  • Finger M, Le Bras R, Gomes CP, Selman B (2013) Solutions for hard and soft constraints using optimized probabilistic satisfiability. Järvisalo M, van Gelder A, eds. Theory and Applications of Satisfiability Testing–SAT (Springer, Berlin), 233–249.CrossrefGoogle Scholar
  • Frank M, Wolfe P (1956) An algorithm for quadratic programming. Naval Res. Logist. Quart. 3(1-2):95–110.CrossrefGoogle Scholar
  • Franke N, Piller F (2004) Toolkits for user innovation and design: An exploration of user interaction and value creation. J. Product Innovation Management 21(6):401–415.CrossrefGoogle Scholar
  • Freund RM, Grigas P (2014) New analysis and results for the FrankWolfe method. Math. Programming 155(1-2):1–32.Google Scholar
  • Fohn M, Liau JS, Greef AR, Young RE, O’Grady PJ (1995) Configuration computer systems through constraint-based modeling and interactive constraint satisfaction. Comput. Indust. 27(1):3–21.CrossrefGoogle Scholar
  • Georgakopoulos G, Kavvadias D, Papadimitriou CH (1988) Probabilistic satisfiability. J. Complexity 4(1):1–11.CrossrefGoogle Scholar
  • Glover F, Laguna M (1997) Tabu search. Reeves C, ed. Modern Heuristic Techniques for Combinatorial (John Wiley & Sons, New York), 70–150.Google Scholar
  • Hansen P, Jaumard B, Mathon V (1993) State-of-the-art survey—constrained nonlinear 0–1 programming. ORSA J. Comput. 5(2):97–119.LinkGoogle Scholar
  • Hansen P, Jaumard B, Parreira AD (1999) On the relations between probabilistic logic and p-CMS. Proc. 16th Internat. Joint Conf. Artificial Intelligence (Morgan Kaufmann Publishers Inc., San Francisco), 56–63.Google Scholar
  • Hansen P, Jaumard B, Nguetse GBD, De Aragao MP (1995) Models and algorithms for probabilistic and Bayesian logic. Proc. 14th Internat. Joint Conf. Artificial Intelligence, Montreal, 1862–1868.Google Scholar
  • Hansen P, Jaumard B, De Aragao MP, Chauny F, Perron S (2000) Probabilistic satisfiability with imprecise probabilities. Internat. J. Approximate Reasoning 24(2):171–189.CrossrefGoogle Scholar
  • Hazan E, Kale S (2012) Projection-free online learning. arXiv:1206.4657.Google Scholar
  • Huffman C, Kahn B (1998) Variety for sale: Mass customization or mass confusion. J. Retailing 74(4):491–513.CrossrefGoogle Scholar
  • Jaggi M (2013) Revisiting Frank-Wolfe: Projection-free sparse convex optimization. Proc. Machine Learn. Res. 28(1):427–435.Google Scholar
  • Kavvadias D, Papadimitriou CH (1990) A linear programming approach to reasoning about probabilities. Ann. Math. Artificial Intelligence 1(1-4):189–205.CrossrefGoogle Scholar
  • Kok AG, Fisher ML, Vaidyanathan R (2008) Assortment planning: Review of literature and industry practice. Retail Supply Chain Management (Springer, Boston), 99–153.CrossrefGoogle Scholar
  • Kristianto Y, Helo P, Jiao RJ (2015) A system level product configurator for engineer-to-order supply chains. Comput. Indust. 72:82–91.CrossrefGoogle Scholar
  • Kübler A, Zengler C, Kuchlin W (2010) Model counting in product configuration. arXiv 1007.1024.Google Scholar
  • Lafond J, Wai HT, Moulines E (2015) On the online Frank-Wolfe algorithms for convex and non-convex optimizations. arXiv:1510.01171.Google Scholar
  • Nilsson NJ (1994) Probabilistic logic revisited. Artificial Intelligence 59(1/2):39–42.Google Scholar
  • Ostrosi E, Fougères AJ, Ferney M (2012) Fuzzy agents for product configuration in collaborative and distributed design process. Appl. Soft Comput. 12(8):2091–2105.CrossrefGoogle Scholar
  • Reddi SJ, Sra S, Poczos B, Smola A (2016) Stochastic Frank-Wolfe methods for nonconvex optimization. arXiv:1607.08254.Google Scholar
  • Rodriguez B, Aydin G (2011) Assortment selection and pricing for configurable products under demand uncertainty. Eur. J. Oper. Res. 210(3):635–646.CrossrefGoogle Scholar
  • Roller D, Kreuz I (2003) Selecting and parameterising components using knowledge based configuration and a heuristic that learns and forgets. Comput.-Aided Design 35(12):1085–1098.CrossrefGoogle Scholar
  • Sanchez R, Mahoney JT (1996) Modularity, flexibility, and knowledge management in product and organization design. Strategic Management J. 17(S2):63–76.CrossrefGoogle Scholar
  • Siddique Z, Rosen DW, Wang N (1998) On the applicability of product variety design concepts to automotive platform commonality. Proc. ASME Design Engrg. Tech. Conf.—Design Theory Methodology, Atlanta.CrossrefGoogle Scholar
  • Tseitin G (1970) On the complexity of derivation in propositional calculus. Slisenjo AO, ed. Studies in Constructive Mathematics and Mathematical Logic, Part 2 (Springer, New York), 115–125.CrossrefGoogle Scholar
  • Ulrich K (1994) Fundamentals of Product Modularity (Springer, Dordrecht, Netherlands), 219–231.CrossrefGoogle Scholar
  • Wahlstrom M (2008) A tighter bound for counting max-weight solutions to 2SAT instances. Grohe M, Niedermeier R, eds. Parameterized and Exact Computation (Springer, Berlin), 202–213.CrossrefGoogle Scholar
  • Walker AJ, Bright G (2013) Stabilisation and control of configurable product manufacturing through Biased Decision Feedback decoupling. J. Manufacturing Systems 32(1):271–280.CrossrefGoogle Scholar
  • Wilson JM (1990) Compact normal forms in propositional logic and integer programming formulations. Comput. Oper. Res. 17(3):309–314.CrossrefGoogle Scholar
  • Woehler C (2011) U.S. Patent No. 8,050,957 (U.S. Patent and Trademark Office, Washington, DC).Google Scholar
  • Yan H, Hooker JN (1999). Tight representation of logical constraints as cardinality rules. Math. Programming 85(2):363–377.CrossrefGoogle Scholar
  • Yuan X, Yan S (2012) Forward Basis selection for sparse approximation over dictionary. Proc. Machine Learn. Res. 22:1377–1388.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.