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Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction

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  • Jingling Zhang
  • Wanliang Wang
  • Yanwei Zhao
  • Carlo Cattani

Abstract

The multiobjective vehicle routing problem considering customer satisfaction (MVRPCS) involves the distribution of orders from several depots to a set of customers over a time window. This paper presents a self-adaptive grid multi-objective quantum evolutionary algorithm (MOQEA) for the MVRPCS, which takes into account customer satisfaction as well as travel costs. The degree of customer satisfaction is represented by proposing an improved fuzzy due-time window, and the optimization problem is modeled as a mixed integer linear program. In the MOQEA, nondominated solution set is constructed by the Challenge Cup rules. Moreover, an adaptive grid is designed to achieve the diversity of solution sets; that is, the number of grids in each generation is not fixed but is automatically adjusted based on the distribution of the current generation of nondominated solution set. In the study, the MOQEA is evaluated by applying it to classical benchmark problems. Results of numerical simulation and comparison show that the established model is valid and the MOQEA is effective for MVRPCS.

Suggested Citation

  • Jingling Zhang & Wanliang Wang & Yanwei Zhao & Carlo Cattani, 2012. "Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-19, December.
    • Handle: RePEc:hin:jnlmpe:879614
    DOI: 10.1155/2012/879614
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    Cited by:

    1. Gaoyuan Qin & Fengming Tao & Lixia Li, 2019. "A Vehicle Routing Optimization Problem for Cold Chain Logistics Considering Customer Satisfaction and Carbon Emissions," IJERPH, MDPI, vol. 16(4), pages 1-17, February.
    2. Benyamin Moghaddasi & Amir Salar Ghafari Majid & Zahra Mohammadnazari & Amir Aghsami & Masoud Rabbani, 2023. "A green routing-location problem in a cold chain logistics network design within the Balanced Score Card pillars in fuzzy environment," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-33, July.
    3. Gitae Kim, 2023. "Dynamic Vehicle Routing Problem with Fuzzy Customer Response," Sustainability, MDPI, vol. 15(5), pages 1-13, March.
    4. Wang, Minxi & Wang, Yajie & Liu, Wei & Ma, Yu & Xiang, Longtao & Yang, Yunqi & Li, Xin, 2021. "How to achieve a win–win scenario between cost and customer satisfaction for cold chain logistics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).

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