Logistical support scheduling under stochastic travel times given an emergency repair work schedule

Shangyao Yan, Chih Kang Lin, Sheng Yu Chen

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


Stochastic factors during the operational stage could have a significant influence on the planning results of logistical support scheduling for emergency roadway repair work. An optimal plan might therefore lose its optimality when applied in real world operations where stochastic disturbances occur. In this study we employ network flow techniques to construct a logistical support scheduling model under stochastic travel times. The concept of time inconsistency is also proposed for precisely estimating the impact of stochastic disturbances arising from variations in vehicle trip travel times during the planning stage. The objective of the model is to minimize the total operating cost with an unanticipated penalty cost for logistical support under stochastic traveling times in short term operations, based on an emergency repair work schedule, subject to related operating constraints. This model is formulated as a mixed-integer multiple-commodity network flow problem and is characterized as NP-hard. To solve the problem efficiently, a heuristic algorithm, based on problem decomposition and variable fixing techniques, is proposed. A simulation-based evaluation method is also presented to evaluate the schedules obtained using the manual method, the deterministic model and the stochastic model in the operation stage. Computational tests are performed using data from Taiwan's 1999 Chi-Chi earthquake. The preliminary test results demonstrate the potential usefulness of the proposed stochastic model and solution algorithm in actual practice.

Original languageEnglish
Pages (from-to)20-35
Number of pages16
JournalComputers and Industrial Engineering
Issue number1
StatePublished - 2014


  • Emergency repair work schedule
  • Heuristic
  • Logistical support scheduling
  • Stochastic travel time
  • Time-space network


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