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A chance-constrained stochastic approach to intermodal container routing problems.

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This study optimizes container routing in sea-rail transport by minimizing expected costs using a chance-constrained programming model. It addresses uncertain travel and transfer times to ensure reliable delivery and ship fulfillment.

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Area of Science:

  • Operations Research
  • Logistics Management
  • Transportation Science

Background:

  • Intermodal transportation systems face challenges with uncertain travel and transfer times.
  • Optimizing container routing is crucial for efficiency and cost reduction in sea-rail logistics.

Purpose of the Study:

  • To develop a model for the container routing problem with stochastic time variables in sea-rail intermodal transport.
  • To minimize the expected total cost while ensuring ship fulfillment and on-time cargo delivery probabilities.

Main Methods:

  • Formulation of a binary integer chance-constrained programming model.
  • Inclusion of stochastic travel times and stochastic transfer time.
  • Development of two chance constraints for service reliability.
  • Application of a hybrid heuristic algorithm for solving the model.

Main Results:

  • Demonstration of the model's feasibility through two case studies.
  • Analysis of the impact of stochastic variables on optimal solutions.
  • Evaluation of the influence of chance constraints on total costs.

Conclusions:

  • The proposed chance-constrained programming model effectively addresses stochasticity in sea-rail container routing.
  • The hybrid heuristic algorithm provides a viable solution for complex optimization problems in logistics.
  • Stochastic variables and chance constraints significantly impact routing decisions and overall system cost.