Towards Distributed Real-Time Coordination of Shoppers’ Routes in Smart Hypermarkets

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.