Conventional wisdom holds that adding constraints to optimization problems, such as Vehicle Routing Problems (VRPs), increases computational complexity and reduces efficiency. This study challenges this assumption. By introducing time window constraints (VRPTW) into VRP models and evaluating them across datasets of varying sizes, results reveal an unexpected outcome: the additional constraints lead to considerable cost reductions—up to 13.3% on average, over 30 independent runs. These results defy traditional expectations, demonstrating that well-crafted constraints can streamline optimization processes and enhance results. This insight into complex logistical systems opens new avenues for leveraging constraints as strategic tools to improve performance in complex logistical systems, redefining their role in optimization theory and practice.

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