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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Jiahai Wang, Yuyan Sun, Zizhen Zhang and Shangce Gao, "Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1134-1153, July 2020. doi: 10.1109/JAS.2020.1003204
Citation: Jiahai Wang, Yuyan Sun, Zizhen Zhang and Shangce Gao, "Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1134-1153, July 2020. doi: 10.1109/JAS.2020.1003204

Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

doi: 10.1109/JAS.2020.1003204
Funds:  This work was supported by the National Key R&D Program of China (2018AAA0101203), the National Natural Science Foundation of China (61673403, 71601191), and the JSPS KAKENHI (JP17K12751)
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  • The multitrip pickup and delivery problem with time windows and manpower planning (MTPDPTW-MP) determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP (MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection (MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

     

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    Highlights

    • A multiobjective pickup and delivery problem with time windows and manpower planning is introduced.
    • A multiobjective iterated local search algorithm with adaptive neighborhood is proposed.
    • The nature of objective functions and the properties of the problem are analyzed.
    • The benefits of multiobjective optimization are discussed.

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