Hazardous material transportation is an integral part of industries that pose significant risks. Hazardous material transportation risk of is proportional to the volume of materials transferred, the length of the link, and the population density, which varies over time. By modeling the effects of time on population density, this paper presents a multi-objective time-dependent hazardous materials routing problem for efficiently managing hazardous material transportation. The proposed model's objectives include the reduction of transportation risks and travel costs. Given the uncertainty associated with hazardous materials routing, an interval type-2 fuzzy logic controller is used to estimate risk; its inputs include population density, vehicle load, link length, and time of day. A type-2 fuzzy multi-objective evolutionary algorithm based on decomposition is used to optimize the proposed model and an adaptive large neighborhood search to enhance the local neighbor search mechanism. Additionally, by utilizing the nadir reference point, the distribution of Pareto front approximation is improved. Furthermore, a new metric is introduced to evaluate the dispersion of Pareto front approximation. The proposed method is compared to three other state-of-the-art evolutionary algorithms using eleven different performance metrics for validation. Then, using the multiple-criteria decision-making approach, the meta-heuristics algorithms are evaluated. The obtained results demonstrate the proposed solution method algorithm's competitiveness and superiority over the other three evolutionary algorithms.

危险品运输是构成重大风险的行业的一个组成部分。危险品运输风险与运输的物资量、链路长度和人口密度成正比，人口密度随时间变化。通过对时间对人口密度的影响进行建模，本文提出了一个多目标时间相关的危险品路线问题，用于有效地管理危险品运输。拟议模型的目标包括降低运输风险和旅行成本。考虑到与危险材料路线相关的不确定性，使用区间类型 2 模糊逻辑控制器来估计风险；它的输入包括人口密度、车辆负载、链路长度和一天中的时间。基于分解的2型模糊多目标进化算法用于优化所提出的模型，并使用自适应大邻域搜索来增强局部邻域搜索机制。此外，通过利用最低点参考点，改进了 Pareto 前沿近似的分布。此外，引入了一种新的度量来评估帕累托前沿近似的离散度。将所提出的方法与其他三种最先进的进化算法进行比较，使用 11 种不同的性能指标进行验证。然后，使用多标准决策方法，对元启发式算法进行评估。所得结果证明了所提出的求解方法算法相对于其他三种进化算法的竞争力和优越性。