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An adaptive compromise programming method for multi-objective path optimization

Author

Listed:
  • Rongrong Li
  • Yee Leung
  • Hui Lin
  • Bo Huang

Abstract

Network routing problems generally involve multiple objectives which may conflict one another. An effective way to solve such problems is to generate a set of Pareto-optimal solutions that is small enough to be handled by a decision maker and large enough to give an overview of all possible trade-offs among the conflicting objectives. To accomplish this, the present paper proposes an adaptive method based on compromise programming to assist decision makers in identifying Pareto-optimal paths, particularly for non-convex problems. This method can provide an unbiased approximation of the Pareto-optimal alternatives by adaptively changing the origin and direction of search in the objective space via the dynamic updating of the largest unexplored region till an appropriately structured Pareto front is captured. To demonstrate the efficacy of the proposed methodology, a case study is carried out for the transportation of dangerous goods in the road network of Hong Kong with the support of geographic information system. The experimental results confirm the effectiveness of the approach. Copyright Springer-Verlag 2013

Suggested Citation

  • Rongrong Li & Yee Leung & Hui Lin & Bo Huang, 2013. "An adaptive compromise programming method for multi-objective path optimization," Journal of Geographical Systems, Springer, vol. 15(2), pages 211-228, April.
    • Handle: RePEc:kap:jgeosy:v:15:y:2013:i:2:p:211-228
    DOI: 10.1007/s10109-012-0172-1
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    References listed on IDEAS

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    1. Davies, Cedric & Lingras, Pawan, 2003. "Genetic algorithms for rerouting shortest paths in dynamic and stochastic networks," European Journal of Operational Research, Elsevier, vol. 144(1), pages 27-38, January.
    2. Akgun, Vedat & Erkut, Erhan & Batta, Rajan, 2000. "On finding dissimilar paths," European Journal of Operational Research, Elsevier, vol. 121(2), pages 232-246, March.
    3. Rongrong Li & Yee Leung, 2011. "Multi-objective route planning for dangerous goods using compromise programming," Journal of Geographical Systems, Springer, vol. 13(3), pages 249-271, September.
    4. Ningchuan Xiao & David A Bennett & Marc P Armstrong, 2002. "Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems," Environment and Planning A, , vol. 34(4), pages 639-656, April.
    5. Alan Murray, 2010. "Advances in location modeling: GIS linkages and contributions," Journal of Geographical Systems, Springer, vol. 12(3), pages 335-354, September.
    6. Stanley Zionts & Jyrki Wallenius, 1976. "An Interactive Programming Method for Solving the Multiple Criteria Problem," Management Science, INFORMS, vol. 22(6), pages 652-663, February.
    7. Granat, Janusz & Guerriero, Francesca, 2003. "The interactive analysis of the multicriteria shortest path problem by the reference point method," European Journal of Operational Research, Elsevier, vol. 151(1), pages 103-118, November.
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    Cited by:

    1. Sazvar, Z. & Mirzapour Al-e-hashem, S.M.J. & Baboli, A. & Akbari Jokar, M.R., 2014. "A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products," International Journal of Production Economics, Elsevier, vol. 150(C), pages 140-154.

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    More about this item

    Keywords

    Multi-objective optimization; Compromise programming; Multi-objective shortest path; Pareto-optimality; C44; C61; L91;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General

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