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A Goal-Robust-Optimization Approach for Solving Open Vehicle Routing Problems with Demand Uncertainty

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Abstract

We investigate a goal-robust-optimization approach for solving open vehicle routing problem with demand uncertainty. The approach obtains an optimal solution that minimizes the weighted sum of undesirable deviations from a predetermined time window; for any realizations of the demand-uncertainty set, the solution enables the cumulative travel time for each route to finish within a predetermined time window as closely as possible. To improve the probability of finding exact solution for the robust-optimization model using a heuristic algorithm, we also propose a particle swarm optimization based on genetic algorithms (HPSO-GA) within the framework of hyper-heuristic to solve the goal-robust-optimization model. The computational results demonstrate that the optimal solution obtained by our goal-robust-optimization approach substantially reduced the penalty cost incurred by deviations from a predetermined time window.

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Authors and Affiliations

  1. School of Mechanical and Automation, Shanghai University, Shanghai, China

    Bing Wang

  2. School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China

    Liang Sun

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  1. Liang Sun
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  2. Bing Wang
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Correspondence to Bing Wang.

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Sun, L., Wang, B. A Goal-Robust-Optimization Approach for Solving Open Vehicle Routing Problems with Demand Uncertainty. Wireless Pers Commun 103, 1059–1075 (2018). https://doi.org/10.1007/s11277-018-5496-9

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  • DOI: https://doi.org/10.1007/s11277-018-5496-9

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