Purpose Smart technologies and connected objects are rapidly changing the organizational frontline. Yet, our understanding of how these technologies infuse service encounters remains limited. Therefore, the purpose of this paper is to update existing classifications of Frontline Service Technology (FST) infusion. Moreover, the authors discuss three promising smart and connected technologies – conversational agents, extended reality (XR) and blockchain technology – and their respective implications for customers, frontline employees and service organizations. Design/methodology/approach This paper uses a conceptual approach integrating existing work on FST infusion with artificial intelligence, robotics, XR and blockchain literature, while also building on insights gathered through expert interviews and focus group conversations with members of two service research centers. Findings The authors define FST and propose a set of FST infusion archetypes at the organizational frontline. Additionally, the authors develop future research directions focused on understanding how conversational agents, XR and blockchain technology will impact service. Originality/value This paper updates and extends existing classifications of FST, while paving the road for further work on FST infusion.

This paper introduces a fast solution procedure to solve 100-node instances of the time-dependent orienteering problem (TD-OP) within a few seconds of computation time. Orienteering problems occur in logistic situations were an optimal combination of locations needs to be selected and the routing between the selected locations needs to be optimized. In the time-dependent variant, the travel time between two locations depends on the departure time at the first location. Next to a mathematical formulation of the TD-OP, the main contribution of this paper is the design of a fast and effective algorithm to tackle this problem. This algorithm combines the principles of an ant colony system (ACS) with a time-dependent local search procedure equipped with a local evaluation metric. Additionally, realistic benchmark instances with varying size and properties are constructed. The average score gap with the known optimal solution on these test instances is only 1.4% with an average computation time of 0.5 seconds. An extensive sensitivity analysis shows that the performance of the algorithm is insensitive to small changes in its parameter settings.

This paper proposes a fast ant colony system based solution method to solve realistic instances of the time-dependent orienteering problem with time windows within a few seconds of computation time. Orienteering problems occur in logistic situations where an optimal combination of locations needs to be selected and the routing between these selected locations needs to be optimized. For the time-dependent problem, the travel time between two locations depends on the departure time at the first location. The main contribution of this paper is the design of a fast and effective algorithm for this problem. Numerous experiments on realistic benchmark instances with varying size confirm the state-of-the-art performance and practical relevance of the algorithm.

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An extension of the arc orienteering problem and its application to cycle trip planning

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