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.

中文翻译：

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使用多目标算法解决带有时间窗和人力计划的多程接送问题

具有时间窗和人力计划（MTPDPTW-MP）的多程接送问题决定了一组救护车路线并找到了医院的人员分配。它涉及具有不同兴趣和目标的不同利益相关者。本研究首先介绍了具有三个目标的多目标MTPDPTW-MP（MO-MTPDPTWMP），以更好地描述现实情况。为了解决该问题，提出了一种具有自适应邻域选择的多目标迭代局部搜索算法（MOILS-ANS）。MOILS-ANS可以为决策者提供多种多样的替代解决方案，以满足他们的需求。为了更好地探索搜索空间，在MOILS-ANS中精心设计了针对特定问题的邻域结构和自适应邻域选择策略。实验结果表明，提出的MOILS-ANS明显优于其他两种多目标算法。此外，分析了目标函数的性质和问题的性质。最后，将提出的MOILS-ANS与先前的单目标算法进行比较，并讨论了多目标优化的好处。