A Study of Demand Stochasticity in Service Network Design

Published Online:https://doi.org/10.1287/trsc.1090.0265

References

  • Andersen J., Crainic T. G., Christiansen M. Service network design with asset management: Formulations and comparative analyses. Transportation Res. Part C: New Tech. (2009a) 17(2):197–207CrossrefGoogle Scholar
  • Andersen J., Crainic T. G., Christiansen M. Service network design with management and coordination of multiple fleets. Eur. J. Oper. Res. (2009b) 193(2):377–389CrossrefGoogle Scholar
  • Armacost A. P., Barnhart C., Ware K. A. Composite variable formulations for express shipment service network design. Transportation Sci. (2002) 36(1):1–20LinkGoogle Scholar
  • Barnhart C., Schneur R. R. Network design for express freight service. Oper. Res. (1996) 44(6):852–863LinkGoogle Scholar
  • Buedenbender K., Grünert T., Sebastian H.-J. A hybrid tabu search/branch and bound algorithm for the direct flight network design problem. Transportation Sci. (2000) 34(4):364–380LinkGoogle Scholar
  • Christiansen M., Fagerholt K., Ronen D. Ship routing and scheduling: Status and perspectives. Transportation Sci. (2004) 38(1):1–18LinkGoogle Scholar
  • Christiansen M., Fagerholt K., Nygreen B., Ronen D., Barnhart C., Laporte G. Maritime transportation. Handbooks in Operations Research and Management Science, Transportation (2007) 14(North-Holland, Amsterdam) 189–284Google Scholar
  • Cordeau J.-F., Toth P., Vigo D. A survey of optimization models for train routing and scheduling. Transportation Sci. (1998) 32(4):380–404LinkGoogle Scholar
  • Crainic T. G. Service network design in freight transportation. Eur. J. Oper. Res. (2000) 122(2):272–288CrossrefGoogle Scholar
  • Crainic T. G., Hall R. W. Long haul freight transportation. Handbook of Transportation Science (2003) 2nd ed.(Kluwer Academic Publishers, Norwell, MA) 451–516CrossrefGoogle Scholar
  • Crainic T. G., Kim K. H., Barnhart C., Laporte G. Intermodal transportation. Handbooks in Operations Research and Management, Transportation (2007) 14(North-Holland, Amsterdam) 467–537Google Scholar
  • Crainic T. G., Rousseau J.-M. Multicommodity, multimode freight transportation: A general modeling and algorithmic framework for the service network design problem. Transportation Res. B: Methodology (1986) 20B:225–242CrossrefGoogle Scholar
  • Crainic T. G., Roy J. O. R. tools for tactical freight transportation planning. Eur. J. Oper. Res. (1988) 33(3):290–297CrossrefGoogle Scholar
  • Crainic T. G., Ferland J.-A., Rousseau J.-M. A tactical planning model for rail freight transportation. Transportation Sci. (1984) 18(2):165–184LinkGoogle Scholar
  • Dupačová J., Consigli G., Wallace S. W. Scenarios for multistage stochastic programs. Ann. Oper. Res. (2000) 100(1–4):25–53CrossrefGoogle Scholar
  • Farvolden J. M., Powell W. B. A dynamic network model for less-than-truckload motor carrier operations. (1991) . Working Paper 90-05, Department of Industrial Engineering, University of Toronto, Ontario, CAGoogle Scholar
  • Farvolden J. M., Powell W. B. Subgradient methods for the service network design problem. Transportation Sci. (1994) 28(3):256–272LinkGoogle Scholar
  • Farvolden J. M., Powell W. B., Lustig I. J. A primal partitioning solution for the arc-chain formulation of a multicommodity network flow problem. Oper. Res. (1992) 41(4):669–694LinkGoogle Scholar
  • Gorman M. F. An application of genetic and tabu searches to the freight railroad operating plan problem. Ann. Oper. Res. (1998a) 78:51–69CrossrefGoogle Scholar
  • Gorman M. F. Santa Fe railway uses an operating-plan model to improve its service design. Interfaces (1998b) 28(4):1–12LinkGoogle Scholar
  • Grünert T., Sebastian H.-J. Planning models for long-haul operations of postal and express shipment companies. Eur. J. Oper. Res. (2000) 122:289–309CrossrefGoogle Scholar
  • Grünert T., Sebastian H.-J., Thärigen M., Sprague R. The design of a letter-mail transportation network by intelligent techniques. Proc. 32nd Annual Hawaii Internat. Conf. System Sci. (1999) 32Maui, HI(1CrossrefGoogle Scholar
  • Haghani A. E. Formulation and solution of combined train routing and makeup, and empty car distribution model. Transportation Res. B: Methodological (1989) 23B(6):433–452CrossrefGoogle Scholar
  • Higle J. L., Wallace S. W. Sensitivity analysis and uncertainty in linear programming. Interfaces (2003) 33:53–60LinkGoogle Scholar
  • Høyland K., Wallace S. W. Generating scenario trees for multistage decision problems. Management Sci. (2001) 47(2):295–307LinkGoogle Scholar
  • Høyland K., Kaut M., Wallace S. W. A heuristic for moment-matching scenario generation. Computational Optim. Appl. (2003) 24(2–3):169–185CrossrefGoogle Scholar
  • Kall P., Wallace S. W.Stochastic Programming (1994) (John Wiley and Sons, New York) Google Scholar
  • Kaut M., Wallace S. W. Evaluation of scenario-generation methods for stochastic programming. Pacific J. Optim. (2007) 3(2):257–271Google Scholar
  • Keaton M. H. Designing optimal railroad operating plans: Lagrangian relaxation and heuristic approaches. Transportation Res. B: Methodological (1989) 23B(6):415–431CrossrefGoogle Scholar
  • Keaton M. H. Service-Cost tradeoffs for carload freight traffic in the U.S. rail industry. Transportation Res. A: Policy and Practice (1991) 25A(6):363–374Google Scholar
  • Keaton M. H. Designing optimal railroad operating plans: A dual adjustment method for implementing Lagrangian relaxation. Transportation Sci. (1992) 26(4):263–279LinkGoogle Scholar
  • Kim D., Barnhart C., Ware K., Reinhardt G. Multimodal express package delivery: A service network design application. Transportation Sci. (1999) 33(4):391–407LinkGoogle Scholar
  • Lamar B. W., Sheffi Y., Powell W. B. A capacity improvement lower bound for fixed charge network design problems. Oper. Res. (1990) 38(4):704–710LinkGoogle Scholar
  • Lium A.-G., Crainic T. G., Wallace S. W. Correlations in stochastic programming: A case from stochastic service network design. Asia-Pacific J. Oper. Res. (2007) 24(2):161–179CrossrefGoogle Scholar
  • Newton H. N. Network design under budget constraints with application to the railroad blocking problem. (1996) . Doctoral dissertation, Industrial and Systems Engineering, Auburn University, Auburn, ALGoogle Scholar
  • Newton H. N., Barnhart C., Vance P. H. Constructing railroad blocking plans to minimize handling costs. Transportation Sci. (1998) 32(4):330–345LinkGoogle Scholar
  • Pedersen M. B., Crainic T. G., Madsen O. B. G. Models and tabu search meta-heuristics for service network design with asset-balance requirements. Transportation Sci. (2008) . ePub ahead of print July 31, http://dx.doi.org/10.1287/trsc.1080.0234Google Scholar
  • Powell W. B. A local improvement heuristic for the design of less-than-truckload motor carrier networks. Transportation Sci. (1986) 20(4):246–357LinkGoogle Scholar
  • Powell W. B., Sheffi Y. The load-planning problem of motor carriers: Problem description and a proposed solution approach. Transportation Res. A: Policy Practice (1983) 17(6):471–480Google Scholar
  • Powell W. B., Sheffi Y. Interactive optimization for motor carrier load planning. J. Bus. Logist. (1986) 7(2):64–90Google Scholar
  • Powell W. B., Sheffi Y. Design and implementation of an interactive optimization system for the network design in the motor carrier industry. Oper. Res. (1989) 37(1):12–29LinkGoogle Scholar
  • Roy J., Delorme L. Netplan: A network optimization model for tactical planning in the less-than-truckload motor-carrier industry. INFOR (1989) 27(1):22–35Google Scholar
  • Ruszczyński A., Shapiro A. Stochastic programming models. Handbooks in Operations Research and Management Science, Stochastic Models (2003) 10(Elsevier Science B.V., Amsterdam) CrossrefGoogle Scholar
  • Smilowitz K. R., Atamtürk A., Daganzo C. F. Deferred item and vehicle routing within integrated networks. Transportation Res. Part E: Logist. Transportation (2003) 39:305–323CrossrefGoogle Scholar
  • Van Hentenryck P., Bent R.Online Stochastic Combinatorial Optimization (2006) (MIT Press, Cambridge, MA) Google Scholar
  • Wallace S. W. Decision making under uncertainty: Is sensitivity analysis of any use? Oper. Res. (2000) 48(1):20–25LinkGoogle Scholar
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