Managing Product Transitions: A Bilevel Programming Approach

Published Online:https://doi.org/10.1287/ijoc.2022.1210

References

  • Aksen D, Aras N (2012) A bilevel fixed charge location model for facilities under imminent attack. Comput. Oper. Res. 39(7):1364–1381.CrossrefGoogle Scholar
  • Altman E, Wynter L (2004) Equilibrium, games, and pricing in transportation and telecommunication networks. Networks Spatial Econom. 4(1):7–21.CrossrefGoogle Scholar
  • Arslan O, Jabali O, Laporte G (2018) Exact solution of the evasive flow capturing problem. Oper. Res. 66(6):1625–1640.LinkGoogle Scholar
  • Audet C, Savard G, Zghal W (2007) New branch-and-cut algorithm for bilevel linear programming. J. Optim. Theory Appl. 134(2):353–370.CrossrefGoogle Scholar
  • Aussel D, Sagratella S (2017) Sufficient conditions to compute any solution of a quasivariational inequality via a variational inequality. Math. Methods Oper. Res. 85(1):3–18.CrossrefGoogle Scholar
  • Bansal A, Uzsoy R, Kempf K (2020) Iterative combinatorial auctions for managing product transitions in semiconductor manufacturing. IISE Trans. 52(4):413–431.CrossrefGoogle Scholar
  • Bard JF (2013) Practical Bilevel Optimization: Algorithms and Applications, Nonconvex Optimization and Its Applications, Vol. 30 (Springer Science & Business Media, Dordrecht, Netherlands).Google Scholar
  • Bard JF, Moore JT (1990) A branch and bound algorithm for the bilevel programming problem. SIAM J. Sci. Statist. Comput. 11(2):281–292.CrossrefGoogle Scholar
  • Bard JF, Moore JT (1992) An algorithm for the discrete bilevel programming problem. Naval Res. Logist. 39(3):419–435.CrossrefGoogle Scholar
  • Bertsimas D, Tsitsiklis JN (1997) Introduction to Linear Optimization (Athena Scientific, Belmont, MA).Google Scholar
  • Bhaskaran SR, Goel A, Ramachandran K (2015) Managing product transitions under technology uncertainty. Preprint, submitted October 29, https://ssrn.com/abstract=1775430.Google Scholar
  • Bilginer Ö, Erhun F (2010) Managing product introductions and transitions Cochran JJ, Cox LA Jr., Keskinocak P, Kharoufeh JP, Smith JC, eds. Wiley Encyclopedia of Operations Research and Management Science, online ed. (John Wiley & Sons, Oxford, UK), https://doi.org/10.1002/9780470400531.eorms0489.Google Scholar
  • Calvete HI, Galé C (2007) Linear bilevel multi-follower programming with independent followers. J. Global Optim. 39(3):409–417.CrossrefGoogle Scholar
  • Calvete HI, Domínguez C, Galé C, Labbé M, Marin A (2019) The rank pricing problem: Models and branch-and-cut algorithms. Comput. Oper. Res. 105(May):12–31.CrossrefGoogle Scholar
  • Campelo M, Dantas S, Scheimberg S (2000) A note on a penalty function approach for solving bilevel linear programs. J. Global Optim. 16(3):245–255.CrossrefGoogle Scholar
  • Colson B, Marcotte P, Savard G (2007) An overview of bilevel optimization. Ann. Oper. Res. 153(1):235–256.CrossrefGoogle Scholar
  • Dempe S (2002) Foundations of Bilevel Programming (Kluwer Academic Publishers, Dordrecht, Netherlands).Google Scholar
  • DeNegre ST, Ralphs TK (2009) A branch-and-cut algorithm for integer bilevel linear programs. Chinneck JW, Kristjansson B, Saltzman MJ, eds. Operations Research and Cyber-Infrastructure (Springer, New York), 65–78.CrossrefGoogle Scholar
  • Dreves A, Facchinei F, Kanzow C, Sagratella S (2011) On the solution of the KKT conditions of generalized Nash equilibrium problems. SIAM J. Optim. 21(3):1082–1108.CrossrefGoogle Scholar
  • Druehl CT, Schmidt GM, Souza GC (2009) The optimal pace of product updates. Eur. J. Oper. Res. 192(2):621–633.CrossrefGoogle Scholar
  • Facchinei F, Kanzow C (2010) Generalized Nash equilibrium problems. Ann. Oper. Res. 175(1):177–211.CrossrefGoogle Scholar
  • Facchinei F, Kanzow C, Sagratella S (2014) Solving quasi-variational inequalities via their KKT conditions. Math. Programming 144(1):369–412.CrossrefGoogle Scholar
  • Ferrer G, Swaminathan JM (2006) Managing new and remanufactured products. Management Sci. 52(1):15–26.LinkGoogle Scholar
  • Fischetti M, Ljubić I, Monaci M, Sinnl M (2017) A new general-purpose algorithm for mixed-integer bilevel linear programs. Oper. Res. 65(6):1615–1637.LinkGoogle Scholar
  • Fischetti M, Ljubić I, Monaci M, Sinnl M (2018) On the use of intersection cuts for bilevel optimization. Math. Programming 172(1–2):77–103.CrossrefGoogle Scholar
  • Hansen P, Jaumard B, Savard G (1992) New branch-and-bound rules for linear bilevel programming. SIAM J. Sci. Statist. Comput. 13(5):1194–1217.CrossrefGoogle Scholar
  • Hemmati M, Smith JC (2016) A mixed-integer bilevel programming approach for a competitive prioritized set covering problem. Discrete Optim. 20(May):105–134.CrossrefGoogle Scholar
  • Huppmann D, Siddiqui S (2018) An exact solution method for binary equilibrium problems with compensation and the power market uplift problem. Eur. J. Oper. Res. 266(2):622–638.CrossrefGoogle Scholar
  • Karabuk S, Wu SD (2003) Coordinating strategic capacity planning in the semiconductor industry. Oper. Res. 51(6):839–849.LinkGoogle Scholar
  • Karabuk S, Wu SD (2005) Incentive schemes for semiconductor capacity allocation: A game theoretic analysis. Production Oper. Management 14(2):175–188.CrossrefGoogle Scholar
  • Klastorin T, Tsai W (2004) New product introduction: Timing, design, and pricing. Manufacturing Service Oper. Management 6(4):302–320.LinkGoogle Scholar
  • Koca E, Souza GC, Druehl CT (2010) Managing product rollovers. Decision Sci. 41(2):403–423.CrossrefGoogle Scholar
  • Köppe M, Queyranne M, Ryan CT (2010) Parametric integer programming algorithm for bilevel mixed integer programs. J. Optim. Theory Appl. 146(1):137–150.CrossrefGoogle Scholar
  • Labbé M, Marcotte P, Savard G (1998) A bilevel model of taxation and its application to optimal highway pricing. Management Sci. 44(12, Part 1):1608–1622.Google Scholar
  • Lavigne D, Loulou R, Savard G (2000) Pure competition, regulated and Stackelberg equilibria: Application to the energy system of Quebec. Eur. J. Oper. Res. 125(1):1–17.CrossrefGoogle Scholar
  • Le Cadre H, Jacquot P, Wan C, Alasseur C (2020) Peer-to-peer electricity market analysis: From variational to generalized Nash equilibrium. Eur. J. Oper. Res. 282(2):753–771.CrossrefGoogle Scholar
  • Li H, Graves SC, Huh WT (2013) Optimal capacity conversion for product transitions under high service requirements. Manufacturing Service Oper. Management 16(1):46–60.LinkGoogle Scholar
  • Liang C, Çakanyıldırım M, Sethi SP (2014) Analysis of product rollover strategies in the presence of strategic customers. Management Sci. 60(4):1033–1056.LinkGoogle Scholar
  • Liao S, Seifert RW (2015) On the optimal frequency of multiple generation product introductions. Eur. J. Oper. Res. 245(3):805–814.CrossrefGoogle Scholar
  • Lim WS, Tang CS (2006) Optimal product rollover strategies. Eur. J. Oper. Res. 174(2):905–922.CrossrefGoogle Scholar
  • Liu X, Kwon C (2020) Exact robust solutions for the combined facility location and network design problem in hazardous materials transportation. IISE Trans. 52(10):1156–1172.CrossrefGoogle Scholar
  • Lobel I, Patel J, Vulcano G, Zhang J (2015) Optimizing product launches in the presence of strategic consumers. Management Sci. 62(6):1778–1799.LinkGoogle Scholar
  • Lozano L, Smith JC (2017) A value-function-based exact approach for the bilevel mixed-integer programming problem. Oper. Res. 65(3):768–786.LinkGoogle Scholar
  • McCormick GP (1976) Computability of global solutions to factorable nonconvex programs: Part I—Convex underestimating problems. Math. Programming 10(1):147–175.CrossrefGoogle Scholar
  • Nishi T, Hiranaka Y, Grossmann IE (2011) A bilevel decomposition algorithm for simultaneous production scheduling and conflict-free routing for automated guided vehicles. Comput. Oper. Res. 38(5):876–888.CrossrefGoogle Scholar
  • Özaltın OY, Prokopyev OA, Schaefer AJ (2018) Optimal design of the seasonal influenza vaccine with manufacturing autonomy. INFORMS J. Comput. 30(2):371–387.LinkGoogle Scholar
  • Rash E, Kempf K (2012) Product line design and scheduling at Intel. Interfaces 42(5):425–436.LinkGoogle Scholar
  • Sagratella S (2017) Algorithms for generalized potential games with mixed-integer variables. Comput. Optim. Appl. 68(3):689–717.CrossrefGoogle Scholar
  • Sagratella S (2019) On generalized Nash equilibrium problems with linear coupling constraints and mixed-integer variables. Optimization 68(1):197–226.CrossrefGoogle Scholar
  • Sagratella S, Schmidt M, Sudermann-Merx N (2020) The noncooperative fixed charge transportation problem. Eur. J. Oper. Res. 284(1):373–382.CrossrefGoogle Scholar
  • Saharidis GK, Ierapetritou MG (2009) Resolution method for mixed integer bi-level linear problems based on decomposition technique. J. Global Optim. 44(1):29–51.CrossrefGoogle Scholar
  • Shi C, Zhou H, Lu J, Zhang G, Zhang Z (2007) The Kth-best approach for linear bilevel multifollower programming with partial shared variables among followers. Appl. Math. Comput. 188(2):1686–1698.CrossrefGoogle Scholar
  • Stein O, Sudermann-Merx N (2018) The noncooperative transportation problem and linear generalized Nash games. Eur. J. Oper. Res. 266(2):543–553.CrossrefGoogle Scholar
  • Sun L, Karwan MH, Kwon C (2018) Generalized bounded rationality and robust multicommodity network design. Oper. Res. 66(1):42–57.LinkGoogle Scholar
  • Sun L, Karwan MH, Kwon C (2019) Path-based approaches to robust network design problems considering boundedly rational network users. Transportation Res. Record 2673(3):637–645.CrossrefGoogle Scholar
  • Tavaslıoğlu O, Prokopyev OA, Schaefer AJ (2019) Solving stochastic and bilevel mixed-integer programs via a generalized value function. Oper. Res. 67(6):1659–1677.Google Scholar
  • Ulrich KT, Eppinger SD (2016) Product Design and Development (McGraw-Hill, New York).Google Scholar
  • Vicente L, Savard G, Judice J (1996) Discrete linear bilevel programming problem. J. Optim. Theory Appl. 89(3):597–614.CrossrefGoogle Scholar
  • Wang L, Xu P (2017) The watermelon algorithm for the bilevel integer linear programming problem. SIAM J. Optim. 27(3):1403–1430.CrossrefGoogle Scholar
  • Wu L, De Matta R, Lowe TJ (2009) Updating a modular product: How to set time to market and component quality. IEEE Trans. Engrg. Management 56(2):298–311.CrossrefGoogle Scholar
  • Wu SD, Erkoc M, Karabuk S (2005) Managing capacity in the high-tech industry: A review of literature. Engrg. Econom. 50(2):125–158.CrossrefGoogle Scholar
  • Xu P, Wang L (2014) An exact algorithm for the bilevel mixed integer linear programming problem under three simplifying assumptions. Comput. Oper. Res. 41(January):309–318.CrossrefGoogle Scholar
  • Yue D, You F (2017) Stackelberg-game-based modeling and optimization for supply chain design and operations: A mixed integer bilevel programming framework. Comput. Chem. Engrg. 102(July):81–95.CrossrefGoogle Scholar
  • Yue D, Gao J, Zeng B, You F (2019) A projection-based reformulation and decomposition algorithm for global optimization of a class of mixed integer bilevel linear programs. J. Global Optim. 73(1):27–57.CrossrefGoogle Scholar
  • Zeng B, An Y (2014) Solving bilevel mixed integer program by reformulations and decomposition. Working paper, University of South Florida, Tampa. http://www.optimization-online.org/DB_FILE/2014/07/4455.pdf.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.