There has been an unexpected increase in the amount of healthcare waste during the COVID-19 pandemic. Managing healthcare waste is vital, as improper practices in the waste system can lead to the further spread of the virus. To develop effective and sustainable waste management systems, decisions in all processes from the source of the waste to its disposal should be evaluated together. Strategic decisions involve locating waste processing centers, while operational decisions deal with waste collection. Although the periodic collection of waste is used in practice, it has not been studied in the relevant literature. This paper integrates the periodic inventory routing problem with location decisions for designing healthcare waste management systems and presents a bi-objective mixed-integer nonlinear programming model that minimizes operating costs and risk simultaneously. Due to the complexity of the problem, a two-step approach is proposed. The first stage provides a mixed-integer linear model that generates visiting schedules to source nodes. The second stage offers a Bi-Objective Adaptive Large Neighborhood Search Algorithm (BOALNS) that processes the remaining decisions considered in the problem. The performance of the algorithm is tested on several hypothetical problem instances. Computational analyses are conducted by comparing BOALNS with its other two versions, Adaptive Large Neighborhood Search Algorithm and Bi-Objective Large Neighborhood Search Algorithm (BOLNS). The computational experiments demonstrate that our proposed algorithm is superior to these algorithms in several performance evaluation metrics. Also, it is observed that the adaptive search engine increases the capability of BOALNS to achieve high-quality Pareto-optimal solutions.

中文翻译：

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医疗废物周期性位置库存路由问题的双目标自适应大邻域搜索算法。

在 COVID-19 大流行期间，医疗废物的数量意外增加。管理医疗废物至关重要，因为废物系统中的不当做法可能导致病毒进一步传播。为了开发有效和可持续的废物管理系统，从废物来源到处置的所有过程中的决策都应该一起评估。战略决策涉及定位废物处理中心，而运营决策则涉及废物收集。尽管在实践中使用了定期收集废物，但在相关文献中尚未对此进行研究。本文将周期性库存路由问题与设计医疗废物管理系统的位置决策相结合，并提出了一个双目标混合整数非线性规划模型，该模型可同时最小化运营成本和风险。由于问题的复杂性，提出了一种两步法。第一阶段提供了一个混合整数线性模型，该模型生成对源节点的访问时间表。第二阶段提供了一个双目标自适应大邻域搜索算法 (BOALNS)，用于处理问题中考虑的剩余决策。该算法的性能在几个假设的问题实例上进行了测试。通过将 BOALNS 与其他两个版本进行比较来进行计算分析，自适应大邻域搜索算法和双目标大邻域搜索算法 (BOLNS)。计算实验表明，我们提出的算法在几个性能评估指标上都优于这些算法。此外，可以观察到自适应搜索引擎提高了 BOALNS 实现高质量帕累托最优解的能力。