Team Orienteering with Time-Varying Profit

Published Online:https://doi.org/10.1287/ijoc.2020.1026

References

  • Abramson M, Audet C, Couture G, Dennis J Jr, Le Digabel S, Tribes C (2019) The NOMAD project, https://www.gerad.ca/nomad/.Google Scholar
  • Adulyasak Y, Cordeau JF, Jans R (2014) Formulations and branch-and-cut algorithms for multivehicle production and inventory routing problems. INFORMS J. Comput. 26(1):103–120.LinkGoogle Scholar
  • Afsar H, Labadie N (2013) Team orienteering problem with decreasing profits. Electron. Notes Discrete Math. 41:285–293.CrossrefGoogle Scholar
  • Audet C, Hare W (2017) Derivative-free and Blackbox Optimization. (Springer, Cham, Switzerland).CrossrefGoogle Scholar
  • Camm JD, Magazine MJ, Kuppusamy S, Martin K (2017) The demand weighted vehicle routing problem. Eur. J. Oper. Res. 262(1):151–162.CrossrefGoogle Scholar
  • Chao I, Golden B, Wasil E (1996) The team orienteering problem. Eur. J. Oper. Res. 88(3):464–474.CrossrefGoogle Scholar
  • Chen L, Miller-Hooks E (2012) Optimal team deployment in urban search and rescue. Transportation Res. Part B: Methodological 46(8):984–999.CrossrefGoogle Scholar
  • Chen HK, Hsueh CF, Chang MS (2009) Production scheduling and vehicle routing with time windows for perishable food products. Comput. Oper. Res. 36(7):2311–2319.CrossrefGoogle Scholar
  • Chitsaz M, Divsalar A, Vansteenwegen P (2016) A two-phase algorithm for the cyclic inventory routing problem. Eur. J. Oper. Res. 254(2):410–426.CrossrefGoogle Scholar
  • Codato G, Fischetti M (2006) Combinatorial Benders’ cuts for mixed-integer linear programming. Oper. Res. 54(4):756–766.LinkGoogle Scholar
  • Coutinho WP, Fliege J, Battarra M (2019) Glider routing and trajectory optimisation in disaster assessment. Eur. J. Oper. Res. 274(3):1138–1154.CrossrefGoogle Scholar
  • Edmonds J, Karp RM (1972) Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM 19(2):248–264 (JACM).CrossrefGoogle Scholar
  • Ekici A, Retharekar A (2013) Multiple agents maximum collection problem with time dependent rewards. Comput. Indust. Engrg. 64(4):1009–1018.CrossrefGoogle Scholar
  • Erkut E, Zhang J (1996) The maximum collection problem with time-dependent rewards. Naval Res. Logist. 43(5):749–763.CrossrefGoogle Scholar
  • Erdoğan G, Laporte G (2013) The orienteering problem with variable profits. Networks 61(2):104–116.CrossrefGoogle Scholar
  • Fermi E, Metropolis N (1952) Numerical solution of a minimum problem. Los Alamos Unclassified Report LA–1492, Los Alamos National Laboratory, Los Alamos, NM.Google Scholar
  • Fischetti M, González JJS, Toth P (1998) Solving the orienteering problem through branch-and-cut. INFORMS J. Comput. 10(2):133–148.LinkGoogle Scholar
  • Golden BL, Levy L, Vohra R (1987) The orienteering problem. Naval Res. Logist. 34(3):307–318.CrossrefGoogle Scholar
  • Goncharov S, Frolova N (2011) Casualty estimation due to earthquakes: Injury. Spence R, So E, Scawthorn C, eds. Human Casualties in Earthquakes: Progress in Modelling and Mitigation (Springer, Dordrecht, Netherlands), 141–152.CrossrefGoogle Scholar
  • Gunawan A, Lau HC, Vansteenwegen P (2016) Orienteering problem: A survey of recent variants, solution approaches and applications. Eur. J. Oper. Res. 255(2):315–332.CrossrefGoogle Scholar
  • Gunawan A, Ng KM, Kendall G, Lai J (2018) An iterated local search algorithm for the team orienteering problem with variable profits. Engrg. Optim. 50(7):1148–1163.CrossrefGoogle Scholar
  • Horst R, Thoai NV (1999) DC programming: Overview. J. Optim. Theory Appl. 103(1):1–43.CrossrefGoogle Scholar
  • Ibaraki T, Imahori S, Kubo M, Masuda T, Uno T, Yagiura M (2005) Effective local search algorithms for routing and scheduling problems with general time-window constraints. Transportation Sci. 39(2):206–232.LinkGoogle Scholar
  • Johnson SG (2019) The NLopt nonlinear-optimization package. http://github.com/stevengj/nlopt.Google Scholar
  • Kraft D (1994) Algorithm 733: TOMP-Fortran modules for optimal control calculations. ACM Trans. Math. Software 20(3):262–281.CrossrefGoogle Scholar
  • Kuwata Y, Takada S (2000) Rescue ability for earthquake causality during the 1995 Kobe earthquake, http://www.lib.kobe-u.ac.jp/repository/00231322.pdf.Google Scholar
  • Laporte G, Louveaux FV (1993) The integer L-shaped method for stochastic integer programs with complete recourse. Oper. Res. Lett. 13(3):133–142.CrossrefGoogle Scholar
  • Le Digabel S (2011) Algorithm 909: Nomad: Nonlinear optimization with the mads algorithm. ACM Trans. Math. Software 37(4):44.CrossrefGoogle Scholar
  • Lefever W, Aghezzaf EH, Hadj-Hamou K (2016) A convex optimization approach for solving the single-vehicle cyclic inventory routing problem. Comput. Oper. Res. 72:97–106.CrossrefGoogle Scholar
  • Murakami H, Takemoto T, Sakamoto K (2000) Study of search and rescue operations in the 1995 hanshin-awaji earthquake: analysis of labor work in relation with building types. Proc. 12th World Conf. Earthquake Engrg. (IAEE, Tokyo, Japan), 272:1–8.Google Scholar
  • Nayeri S, Asadi-Gangraj E, Emami S (2019) Metaheuristic algorithms to allocate and schedule of the rescue units in the natural disaster with fatigue effect. Neural Comput. Appl. 31(11):7517–7537.CrossrefGoogle Scholar
  • Petal M (2011) Earthquake casualties research and public education. Spence R, So E, Scawthorn C, eds. Human Casualties in Earthquakes: Progress in Modelling and Mitigation (Springer, Dordrecht, Netherlands), 25–50.CrossrefGoogle Scholar
  • Pietz J, Royset JO (2013) Generalized orienteering problem with resource dependent rewards. Naval Res. Logist. 60(4):294–312.CrossrefGoogle Scholar
  • Powell MJD (1994) A direct search optimization method that models the objective and constraint functions by linear interpolation. Gomez S, Hennart J-P, eds. Advances in Optimization and Numerical Analysis (Springer, Dordrecht, Netherlands), 51–67.CrossrefGoogle Scholar
  • Rahmaniani R, Crainic TG, Gendreau M, Rei W (2017) The Benders decomposition algorithm: A literature review. Eur. J. Oper. Res. 259(3):801–817.CrossrefGoogle Scholar
  • Rauchecker G, Schryen G (2019) An exact branch-and-price algorithm for scheduling rescue units during disaster response. Eur. J. Oper. Res. 272(1):352–363.CrossrefGoogle Scholar
  • Runarsson TP, Yao X (2005) Search biases in constrained evolutionary optimization. IEEE Trans. Systems Man Cybernetics C. 35(2):233–243.CrossrefGoogle Scholar
  • Schryen G, Rauchecker G, Comes T (2015) Resource planning in disaster response. Bus. Inform. Systems Engrg. 57(4):243–259.CrossrefGoogle Scholar
  • Svanberg K (2002) A class of globally convergent optimization methods based on conservative convex separable approximations. SIAM J. Optim. 12(2):555–573.CrossrefGoogle Scholar
  • Tang H, Miller-Hooks E, Tomastik R (2007) Scheduling technicians for planned maintenance of geographically distributed equipment. Transportation Res., Part E Logist. Transportation Rev. 43(5):591–609.CrossrefGoogle Scholar
  • Thorsteinsson ES (2001) Branch-and-check: A hybrid framework integrating mixed integer programming and constraint logic programming. Walsh T, ed. Proc. Internat. Conf. Principles Practice Constraint Programming (Springer, Berlin), 16–30.Google Scholar
  • Tsiligirides T (1984) Heuristic methods applied to orienteering. J. Oper. Res. Soc. 35(9):797–809.CrossrefGoogle Scholar
  • Vansteenwegen P, Gunawan A (2019) Orienteering Problems: Models and Algorithms for Vehicle Routing Problems with Profits (Springer, Cham, Switzerland).CrossrefGoogle Scholar
  • Vansteenwegen P, Souffriau W, Oudheusden DV (2011) The orienteering problem: A survey. Eur. J. Oper. Res. 209(1):1–10.CrossrefGoogle Scholar
  • Vigerske S, Gleixner A (2018) SCIP: Global optimization of mixed-integer nonlinear programs in a branch-and-cut framework. Optim. Methods Software 33(3):563–593.CrossrefGoogle Scholar
  • Wächter A, Biegler LT (2006) On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math. Programming 106(1):25–57.CrossrefGoogle Scholar
  • Wex F, Schryen G, Feuerriegel S, Neumann D (2014) Emergency response in natural disaster management: Allocation and scheduling of rescue units. Eur. J. Oper. Res. 235(3):697–708.CrossrefGoogle Scholar
  • Yu J, Aslam J, Karaman S, Rus D (2015) Anytime planning of optimal schedules for a mobile sensing robot. Proc. Internat. Conf. Intelligent Robots Systems (IROS) (IEEE, New York), 5279–5286.Google Scholar
  • Yu Q, Fang K, Zhu N, Ma S (2019) A matheuristic approach to the orienteering problem with service time dependent profits. Eur. J. Oper. Res. 273(2):488–503.CrossrefGoogle Scholar
  • Zhong Y, Aghezzaf EH (2011) Combining DC-programming and steepest-descent to solve the single-vehicle inventory routing problem. Comput. Indust. Engrg. 61(2):313–321.CrossrefGoogle Scholar
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