Abstract
In this paper, we consider the problem of route guidance for shoppers in crowded hypermarkets equipped with smart space technologies. This is an actual and a highly computationally complex problem in peak hours due to dynamically changing congestion conditions, the size and complexity of hypermarkets, and the presence of a multitude of shoppers with different shopping constraints and preferences. High computational complexity of this problem requires a computationally efficient solution approach. We propose a shopper route guidance architecture in which a hypermarket is modelled as a network of communicating smart building agents, each one monitoring its exclusive physical area. Moreover, each shopper is represented by an agent installed on a shopper’s app that, by interacting with other shoppers and smart building agents, dynamically updates its shopping route. Each shopper agent resolves the pick sequencing problem with congestion, i.e., given a shopper’s list, the shopper’s items’ locations are sequenced in the route proposed to a shopper so that the overall traveling time is minimized considering congestion in real-time. We propose a (low computational complexity) greedy tour algorithm and a distributed TSP mathematical model solved in Cplex for this problem and compare their performance. The results show that the proposed architecture and methods scale well and provide efficient shoppers’ routes.
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Notes
- 1.
Simulation experiments were performed on HP ProBook with Intel Core i5-6200U CPU at 2.30 Ghz with 16 Gb RAM memory.
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Lujak, M., Doniec, A. (2019). Towards Distributed Real-Time Coordination of Shoppers’ Routes in Smart Hypermarkets. In: Lujak, M. (eds) Agreement Technologies. AT 2018. Lecture Notes in Computer Science(), vol 11327. Springer, Cham. https://doi.org/10.1007/978-3-030-17294-7_17
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