Multi-Objective Travelling Salesman Problem (MOTSP) is one of the most crucial problems in realistic scenarios, and it is difficult to solve by classical methods. However, it can be solved by evolutionary methods. This paper investigates the Constrained Multi-Objective Travelling Salesman Problem (CMOTSP) and the Constrained Multi-Objective Solid Travelling Salesman Problem (CMOSTSP) under an uncertain environment with zigzag uncertain variables. To solve CMOTSP and CMOSTSP models under uncertain environment, the expected value and optimistic value models are developed using two different ranking criteria of uncertainty theory. The models are transformed to their deterministic forms using the fundamentals of uncertainty. The Models are solved using two solution methodologies Aspiration level-based Multi-Objective Quasi Oppositional Jaya Algorithm (AL-based MOQO Jaya) and Fuzzy Programming Technique (FPT) with linear membership function. Further, the numerical illustration is solved using both methodologies to demonstrate its application. The sensitivity of the OVM model’s objective functions regarding confidence levels is also investigated to look at the variation in the objective function. The paper concludes that the developed approach has solved CMOTSP and CMOSTSP efficiently with an effective output and provides alternative solutions for decision-making to DM.

多目标旅行商问题（MOTSP）是现实场景中最关键的问题之一，很难用经典方法解决。然而，它可以通过进化方法来解决。本文研究了锯齿形不确定变量的不确定环境下的约束多目标旅行商问题（CMOTSP）和约束多目标实体旅行商问题（CMOSTSP）。为了解决不确定环境下的CMOTSP和CMOSTSP模型，使用不确定性理论的两种不同排序标准开发了期望值和乐观值模型。使用不确定性的基本原理将模型转换为其确定性形式。使用两种解决方法来求解模型：基于愿望水平的多目标准对立 Jaya 算法（基于 AL 的 MOQO Jaya）和具有线性隶属函数的模糊编程技术（FPT）。此外，使用两种方法求解数值说明以证明其应用。还研究了 OVM 模型的目标函数对于置信水平的敏感性，以了解目标函数的变化。本文的结论是，所开发的方法有效地解决了 CMOTSP 和 CMOSTSP 问题，并提供了有效的输出，并为 DM 决策提供了替代解决方案。