### Problem Statement  
Supermarket navigation is a long-standing issue that is only expected to become significantly worse due to the projected increase in global retail sales from USD 23.74 trillion sales in 2020 to USD 31.70 trillion sales in 2025 [8]. There have been many attempts to optimize this issue, primarily through tangible forms such as enhancing the effectiveness of signage using psychological principles [9], but also technological solutions like interactive information kiosks which provide users with a top-down two-dimensional vision of the store layout and their current location, this allows them to search for products and the system provides directions towards the items. Other hardware-based solutions such as augmented reality (AR) applications [10], shopping cart trackers [11], and grocery store mapping software [12] attempt to control the shopping experience with less consumer intervention; however, these systems are expensive to maintain, are not cost-effective, and are mostly abandoned projects; thus, we must turn to hardware independence, mainly to lessen financial costs.  
In addition, software-based solutions such us two-dimensional-map pathfinding algorithms, augmented with machine learning (ML) approaches, are already widely available as they are closely tied to supermarket navigation since the problem is essentially a shortest-path problem that can be likened to the traveling salesperson problem [13]. However, these are 'best-case' shortest-path algorithms and do not consider other external factors such as grocery shopper behavior, constantly changing shelf arrangement, and product availability. Furthermore, traditional ML approaches under unsupervised variety like [PERSON]'s and A* path-search algorithms alone are typically static and fail to adapt to the dynamic nature of supermarket environments, leading to suboptimal routing decisions that may prolong the dwell time of grocery shopping tasks. Motivated by this, bringing in a dynamic sorting algorithm augments pathfinding algorithms for supermarket navigation. An ML-assisted sorting algorithm that leverages real-time data and proximity-driven clustering to dynamically adjust the sorting of shopping lists can effectively reduce dwell time, especially for epistemic shoppers. Its adaptive characteristic makes a supermarket navigation system more efficient and practical in real-world shopping scenarios.  
No optimally fit algorithm can solve the supermarket navigation problem in a realistic setting, with realistic conditions, and with no requirement for tracking hardware; having stated that, this research seeks to answer these questions:  
* What parameters of grocery shopping behavior can be leveraged to formulate an algorithm that does not require the location of the shopper to work?
* What navigation approach can be developed to assist shoppers in finding products efficiently, minimizing their travel distance and search time?
* What are the potential limitations or challenges in the implementation of the machine learning-assisted proximity-based dynamic sorting algorithm in a simulated supermarket setting?
* What are the considerations for implementing it in supermarkets that have different sizes and layouts?