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A Review of Sensitivity Results for Linear Networks and a New Approximation to Reduce the Effects of Degeneracy

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  • Warren B. Powell

    (Princeton University, Princeton, New Jersey 08544)

Abstract

Estimating the reduced cost of an upper bound in a classical linear transshipment network is traditionally accomplished using the shadow price for this constraint, given by the standard calculation c̄ ıj = c ij + (pi) j - (pi) ı . This reduced cost is only a subgradient due to network degeneracy and often exhibits errors of 50% or more compared to the actual change in the objective function if the upper bound were raised by one unit and the network reoptimized. A new approximation is developed, using a simple modification of the original reduced cost calculation, which is shown to be significantly more accurate. This paper summarizes the basic theory behind network sensitivity, much of which is known as folklore in the networks community, to establish the theoretical properties of the new approximation. The essential idea is to use least-cost flow augmenting paths in the basis to estimate certain directional derivatives which are used in the development of the approximation. The technique is motivated with an application to pricing in truckload trucking.

Suggested Citation

  • Warren B. Powell, 1989. "A Review of Sensitivity Results for Linear Networks and a New Approximation to Reduce the Effects of Degeneracy," Transportation Science, INFORMS, vol. 23(4), pages 231-243, November.
  • Handle: RePEc:inm:ortrsc:v:23:y:1989:i:4:p:231-243
    DOI: 10.1287/trsc.23.4.231
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    Citations

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    Cited by:

    1. Raymond K. Cheung & Chuen-Yih Chen, 1998. "A Two-Stage Stochastic Network Model and Solution Methods for the Dynamic Empty Container Allocation Problem," Transportation Science, INFORMS, vol. 32(2), pages 142-162, May.
    2. Meissner, Joern & Senicheva, Olga V., 2018. "Approximate dynamic programming for lateral transshipment problems in multi-location inventory systems," European Journal of Operational Research, Elsevier, vol. 265(1), pages 49-64.
    3. Warren B. Powell & Tassio A. Carvalho, 1998. "Dynamic Control of Logistics Queueing Networks for Large-Scale Fleet Management," Transportation Science, INFORMS, vol. 32(2), pages 90-109, May.
    4. Huseyin Topaloglu & Warren Powell, 2007. "Incorporating Pricing Decisions into the Stochastic Dynamic Fleet Management Problem," Transportation Science, INFORMS, vol. 41(3), pages 281-301, August.
    5. Gregory A. Godfrey & Warren B. Powell, 2002. "An Adaptive Dynamic Programming Algorithm for Dynamic Fleet Management, II: Multiperiod Travel Times," Transportation Science, INFORMS, vol. 36(1), pages 40-54, February.
    6. Huseyin Topaloglu & Warren B. Powell, 2007. "Sensitivity Analysis of a Dynamic Fleet Management Model Using Approximate Dynamic Programming," Operations Research, INFORMS, vol. 55(2), pages 319-331, April.
    7. G J King & H Topaloglu, 2007. "Incorporating the pricing decisions into the dynamic fleet management problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(8), pages 1065-1074, August.
    8. Kraft, Edwin R., 2002. "Scheduling railway freight delivery appointments using a bid price approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(2), pages 145-165, February.
    9. Michael Z. Spivey & Warren B. Powell, 2004. "The Dynamic Assignment Problem," Transportation Science, INFORMS, vol. 38(4), pages 399-419, November.
    10. Lin, Chi-Jen & Wen, Ue-Pyng, 2003. "Sensitivity analysis of the optimal assignment," European Journal of Operational Research, Elsevier, vol. 149(1), pages 35-46, August.
    11. Gregory A. Godfrey & Warren B. Powell, 2002. "An Adaptive Dynamic Programming Algorithm for Dynamic Fleet Management, I: Single Period Travel Times," Transportation Science, INFORMS, vol. 36(1), pages 21-39, February.
    12. Yan, Shangyao & Yang, Dah-Hwei, 1996. "A decision support framework for handling schedule perturbation," Transportation Research Part B: Methodological, Elsevier, vol. 30(6), pages 405-419, December.
    13. Balaji Gopalakrishnan & Seunghyun Kong & Earl Barnes & Ellis Johnson & Joel Sokol, 2011. "A least-squares minimum-cost network flow algorithm," Annals of Operations Research, Springer, vol. 186(1), pages 119-140, June.
    14. Yan, Shangyao & Tu, Yu-ping, 1997. "Multifleet routing and multistop flight scheduling for schedule perturbation," European Journal of Operational Research, Elsevier, vol. 103(1), pages 155-169, November.
    15. Yan, Shangyao & Young, Hwei-Fwa, 1996. "A decision support framework for multi-fleet routing and multi-stop flight scheduling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(5), pages 379-398, September.
    16. Huseyin Topaloglu & Warren B. Powell, 2006. "Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 31-42, February.
    17. Kuyzu, Gültekin & Akyol, Çağla Gül & Ergun, Özlem & Savelsbergh, Martin, 2015. "Bid price optimization for truckload carriers in simultaneous transportation procurement auctions," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 34-58.

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