Enriching Solutions to Combinatorial Problems via Solution Engineering

References

• Adomavicius G, Kwon Y (2014) Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS J. Comput. 26(2):351–369.Link, Google Scholar
• Amilhastre J, Fargier H, Marquis P (2002) Consistency restoration and explanations in dynamic csps application to configuration. Artificial Intelligence 135(1–2):199–234.Crossref, Google Scholar
• Armenakis AA, Bedeian AG (1999) Organizational change: A review of theory and research in the 1990s. J. Management 25(3):293–315.Crossref, Google Scholar
• Balas E, Jeroslow R (1972) Canonical cuts on the unit hypercube. SIAM J. Appl. Math. 23(1):61–69.Crossref, Google Scholar
• Barbieri G, Pachet F, Roy P, Esposti MD (2012) Markov constraints for generating lyrics with style. De Raedt L, Bessiere C, Dubois D, Doherty P, Frasconi P, eds. ECAI 2012—20th Eur. Conf. Artificial Intelligence (IOS Press, Amsterdam), 115–120.Google Scholar
• Beldiceanu N, Simonis H (2011) A constraint seeker: Finding and ranking global constraints from examples. Lee JH, ed. Principles Practice Constraint Programming—CP 2011, Lecture Notes in Computer Science, vol. 6876 (Springer, Berlin), 12–26.Crossref, Google Scholar
• Beldiceanu N, Carlsson M, Demassey S, Petit T (2007) Global constraint catalogue: Past, present and future. Constraints 12(1):21–62.Crossref, Google Scholar
• Bertsimas D, Farias VF, Trichakis N (2012) On the efficiency-fairness trade-off. Management Sci. 58(12):2234–2250.Link, Google Scholar
• Bertsimas D, Natarajan K, Teo C-P (2006) Persistence in discrete optimization under data uncertainty. Math. Programming 108(2–3):251–274.Crossref, Google Scholar
• Bessiere C, Koriche F, Lazaar N, O’Sullivan B (2017) Constraint acquisition. Artificial Intelligence 244(March):315–342.Crossref, Google Scholar
• Bessiere C, Hebrard E, Hnich B, Kiziltan Z, Walsh T (2005) Among, common and disjoint constraints. Hnich B, Carlsson M, Fages F, Rossi F, eds. Recent Adv. Constraints (CSCLP 2005), Lecture Notes in Computer Science, vol. 3978 (Springer, Berlin), 29–43.Google Scholar
• Bessiere C, Hebrard E, Katsirelos G, Kiziltan Z, Picard-Cantin É, Quimper C, Walsh T (2014) The balance constraint family. O’Sullivan B, ed. Principles Practice Constraint Programming (CP 2014), Lecture Notes in Computer Science, vol. 8656 (Springer, Berlin), 174–189.Crossref, Google Scholar
• Box GE, Hunter WG, Hunter JS (2005) Statistics for Experimenters: Design, Innovation, and Discovery, 2nd ed. (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Brown GG, Dell RF, Wood RK (1997) Optimization and persistence. Interfaces 27(5):15–37.Link, Google Scholar
• Cambazard H, Fages J (2015) New filtering for atmostnvalue and its weighted variant: A Lagrangian approach. Constraints 20(3):362–380.Crossref, Google Scholar
• Camm JD (2014) A (very) short course in suboptimization. Interfaces 44(4):428–431.Link, Google Scholar
• Carvajal R, Constantino M, Goycoolea M, Vielma JP, Weintraub A (2013) Imposing connectivity constraints in forest planning models. Oper. Res. 61(4):824–836.Link, Google Scholar
• Chabert G, Jaulin L, Lorca X (2009) A constraint on the number of distinct vectors with application to localization. Gent IP, ed. Principles Practice Constraint Programming—CP 2009, Lecture Notes in Computer Science, vol. 5732 (Springer, Berlin), 196–210.Crossref, Google Scholar
• Chu PC, Beasley JE (1997) A genetic algorithm for the generalised assignment problem. Comput. Oper. Res. 24(1):17–23.Crossref, Google Scholar
• Danna E, Woodruff DL (2009) How to select a small set of diverse solutions to mixed integer programming problems. Oper. Res. Lett. 37(4):255–260.Crossref, Google Scholar
• Danna E, Fenelon M, Gu Z, Wunderling R (2007) Generating multiple solutions for mixed integer programming problems. Fischetti M, Williamson D, eds. Integer Programming Combinatorial Optim. (IPCO 2007), Lecture Notes in Computer Science, vol. 4513 (Springer, Berlin), 280–294.Crossref, Google Scholar
• Dinkelbach W (1967) On non-linear fractional programming. Management Sci. 13(7):492–498.Link, Google Scholar
• Efremov R, Insua DR, Lotov A (2009) A framework for participatory decision support using pareto frontier visualization, goal identification and arbitration. Eur. J. Oper. Res. 199(2):459–467.Crossref, Google Scholar
• Ehrgott M, Gandibleux X (2000) A survey and annotated bibliography of multiobjective combinatorial optimization. OR-Spektrum 22(4):425–460.Crossref, Google Scholar
• Eiter T, Erdem E, Erdogan H, Fink M (2013) Finding similar/diverse solutions in answer set programming. Theory Practice Logic Programming 13(3):303–359.Crossref, Google Scholar
• Fisher ML, Jaikumar R, Van Wassenhove LN (1986) A multiplier adjustment method for the generalized assignment problem. Management Sci. 32(9):1095–1103.Link, Google Scholar
• Glover F, Kuo CC, Dhir KS (1998) Heuristic algorithms for the maximum diversity problem. J. Inform. Optim. Sci. 19(1):109–132.Crossref, Google Scholar
• Glover F, Løkketangen A, Woodruff DL (2000) Scatter search to generate diverse MIP solutions. Laguna M, González-Velarde JL, eds. OR Computing Tools for Modeling, Optimization and Simulation, Operations Research/Computer Science Interfaces Series, vol. 12 (Springer, Boston), 299–317.Crossref, Google Scholar
• Greistorfer P, Løkketangen A, Voß S, Woodruff DL (2008) Experiments concerning sequential versus simultaneous maximization of objective function and distance. J. Heuristics 14(6):613–625.Crossref, Google Scholar
• Hadzic T, Holland A, O’Sullivan B (2009) Reasoning about optimal collections of solutions.Gent IP, ed. Principles Practice Constraint Programming—CP 2009, Lecture Notes in Computer Science, vol. 5732 (Springer, Berlin), 409–423.Google Scholar
• Hebrard E, O’Sullivan B, Walsh T (2007) Distance constraints in constraint satisfaction. Veloso MM, ed. IJCAI 2007 Proc. 20th Internat. Joint Conf. Artificial Intelligence (Morgan Kaufmann, San Francisco), 106–111.Google Scholar
• Hebrard E, Hnich B, O’Sullivan B, Walsh T (2005) Finding diverse and similar solutions in constraint programming. Cohn A, ed. Proc. 20th Natl. Conf. Artificial Intelligence, vol. 1(AAAI Press, Menlo Park, CA), 372–377.Google Scholar
• Hebrard E, Marx D, O’Sullivan B, Razgon I (2011) Soft constraints of difference and equality. J. Artificial Intelligence Res. 41(2):97–130.Crossref, Google Scholar
• Hentenryck PV, Coffrin C, Gutkovich B (2009) Constraint-based local search for the automatic generation of architectural tests. Gent IP, ed. Principles Practice Constraint Programming—CP 2009, Lecture Notes in Computer Science, vol. 5732 (Springer, Berlin), 787–801.Crossref, Google Scholar
• IBM (2017) IBM ILOG CPLEX 12.7 User’s Manual (IBM ILOG CPLEX Division, Incline Village, NV).Google Scholar
• Karasakal E, Köksalan M (2009) Generating a representative subset of the nondominated frontier in multiple criteria decision making. Oper. Res. 57(1):187–199.Link, Google Scholar
• Katsirelos G, Narodytska N, Walsh T (2012) The SeqBin constraint revisited. Milano M, ed. Principles Practice Constraint Programming—CP 2012, Lecture Notes in Computer Science, vol. 7514 (Springer, Berlin), 332–347.Crossref, Google Scholar
• Kiziltan Z, Lippi M, Torroni P (2016) Constraint detection in natural language problem descriptions. Kambhampati S, ed. Proc. 25th Internat. Joint Conf. Artificial Intelligence (IJCAI 2016), (AAAI Press, Menlo Park, CA), 744–750.Google Scholar
• Kuo CC, Glover F, Dhir KS (1993) Analyzing and modeling the maximum diversity problem by zero-one programming. Decision Sci. 24(6):1171–1185.Crossref, Google Scholar
• Lecoutre C, Likitvivatanavong C, Yap RHC (2015) STR3: A path-optimal filtering algorithm for table constraints. Artificial Intelligence 220(March):1–27.Crossref, Google Scholar
• Løkketangen A, Woodruff DL (2005) A distance function to support optimized selection decisions. Decision Support Systems 39(3):345–354.Crossref, Google Scholar
• Morrison T (2010) A new paradigm for robust combinatorial optimization: Using persistence as a theory of evidence. PhD dissertation, University of Colorado Denver, Denver.Google Scholar
• Nadel A (2011) Generating diverse solutions in SAT. Sakallah KA, Simon L, eds. Theory Appl. Satisfiability Testing—SAT 2011, Lecture Notes in Computer Science, vol. 6695 (Springer, Berlin), 287–301.Crossref, Google Scholar
• Narodytska N, Petit T, Siala M, Walsh T (2016) Three generalizations of the FOCUS constraint. Constraints 21(4):495–532.Crossref, Google Scholar
• Ouis S, Jussien N, Boizumault P (2003) k-relevant explanations for constraint programming. Russell I, Haller SM, eds. Proc. 16th Internat. Florida Artificial Intelligence Res. Soc. Conf. (AAAI Press, Menlo Park, CA), 192–196..Google Scholar
• Pesant G, Régin J-C (2005) SPREAD: A balancing constraint based on statistics. van Beek P, ed. Principles Practice Constraint Programming—CP 2005, Lecture Notes in Computer Science, vol. 3709 (Springer, Berlin), 460–474.Crossref, Google Scholar
• Petit T, Petit L (2016) Optimizing molecular cloning of multiple plasmids. Kambhampati S, ed. Proc. 25th Internat. Joint Conf. Artificial Intelligence (IJCAI 2016) (AAAI Press, Menlo Park, CA), 773–779.Google Scholar
• Petit T, Trapp AC (2015) Finding diverse solutions of high quality to constraint optimization problems. Yang Q, Wooldridge M, eds. Proc. 24th Internat. Joint Conf. Artificial Intelligence (IJCAI 2015) (AAAI Press, Menlo Park, CA), 260–267.Google Scholar
• Prud’homme C, Fages J-G, Lorca X (2015) Choco3 documentation. Software documentation, TASC, INRIA Rennes, LINA CNRS UMR 6241, COSLING S.A.S., Nantes, France. Accessed December 1, 2016, http://www.choco-solver.org.Google Scholar
• Quimper C-G, López-Ortiz A, Pesant G (2006) A quadratic propagator for the inter-distance constraint. Gil Y, Mooney RJ, eds. Proc. 21st Natl. Conf. Artificial Intelligence/18th Innovative Appl. Artificial Intelligence Conf. (AAAI-06) (AAAI Press, Menlo Park, CA), 123–128.Google Scholar
• Schaible S (1976) Fractional programming. II, on Dinkelbach’s algorithm. Management Sci. 22(8):868–873.Link, Google Scholar
• Schaus P, Deville Y, Dupont P (2007a) Bound-consistent deviation constraint. Bessiere C, ed. Principles Practice Constraint Programming—CP 2007, Lecture Notes in Computer Science, vol. 4741 (Springer, Berlin), 620–634.Crossref, Google Scholar
• Schaus P, Deville Y, Dupont P, Régin J-C (2007b) The deviation constraint. Hentenryck PV, Wolsey LA, eds. Integration AI OR Techniques Constraint Programming Combinatorial Optim. Problems (CPAIOR 2007), Lecture Notes in Computer Science, vol. 4510 (Springer, Berlin), 260–274.Crossref, Google Scholar
• Schaus P, Hentenryck PV, Régin J (2009) Scalable load balancing in nurse to patient assignment problems. van Hoeve WJ, Hooker JN, eds. Integration AI OR Techniques Constraint Programming Combinatorial Optim. Problems (CPAIOR 2009), Lecture Notes in Computer Science, vol. 5547 (Springer, Berlin), 248–262.Crossref, Google Scholar
• Schell D (2002) Optimality in musical melodies and harmonic progressions: The travelling musician. Eur. J. Oper. Res. 140(2):354–372.Crossref, Google Scholar
• Schittekat P, Sörensen K (2009) Supporting 3PL decisions in the automotive industry by generating diverse solutions to a large-scale location-routing problem. Oper. Res. 57(5):1058–1067.Link, Google Scholar
• Sebbah S, Bagley C, Colena M, Kadioglu S (2016) Service availability optimization in cloud-based in-memory data grids. Rueher M, ed. Principles Practice Constraint Programming—CP 2016, Lecture Notes in Computer Science, vol. 9892 (Springer, Cham, Switzerland), 666–679.Google Scholar
• Shim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C (2002) Past, present, and future of decision support technology. Decision Support Syst. 33(2):111–126.Crossref, Google Scholar
• Simonin G, Artigues C, Hebrard E, Lopez P (2015) Scheduling scientific experiments for comet exploration. Constraints 20(1):77–99.Crossref, Google Scholar
• Tamiz M, Jones D, Romero C (1998) Goal programming for decision making: An overview of the current state-of-the-art. Eur. J. Oper. Res. 111(3):569–581.Crossref, Google Scholar
• Trapp AC (2019) Source code for solution engineering. Accessed January 15, 2019, http://users.wpi.edu/∼atrapp/tools-and-software.htm.Google Scholar
• Trapp AC, Konrad RA (2015) Finding diverse optima and near-optima to binary integer programs. IIE Trans. 47(11):1300–1312.Crossref, Google Scholar
• Tsai JF, Lin MH, Hu YC (2008) Finding multiple solutions to general integer linear programs. Eur. J. Oper. Res. 184(2):802–809.Crossref, Google Scholar
• Voll P, Jennings M, Hennen M, Shah N, Bardow A (2015) The optimum is not enough: A near-optimal solution paradigm for energy systems synthesis. Energy 82(March):446–456.Crossref, Google Scholar