Title: Human resource allocation or recommendation based on multi-factor criteria in on-demand and batch scenarios

Authors: Michael Arias; Jorge Munoz-Gama; Marcos Sepúlveda; Juan Carlos Miranda

Addresses: Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, 7820436 Macul, Chile ' Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, 7820436 Macul, Chile ' Computer Science Department, School of Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, 7820436 Macul, Chile ' Software and Consulting Group, 36th Street, San José, Costa Rica

Abstract: Dynamic resource allocation is considered a major challenge in the context of business process management. At the operational level, flexible methods that support resource allocation and which consider different criteria at run-time are required. It is also important that these methods are able to support multiple allocations in a simultaneous manner. In this paper, we present a framework based on multi-factor criteria that proposes a recommender system which is capable of recommending the most suitable resources for executing a range of different activities, while also considering individual requests or requests made in blocks. To evaluate the proposed framework, a number of experiments were conducted using different test scenarios. These scenarios provide evidence that our approach based on multi-factor criteria successfully allocates the most suitable resources for executing a process in real and flexible environments. In order to demonstrate this assertion, we use a help-desk process as a real case study. [Received: 19 May 2017; Revised: 23 October 2017; Accepted: 31 January 2018]

Keywords: human resource allocation; human resource recommendation; multi-factor criteria; on-demand; batch; dynamic resource allocation; recommender system; business process management; BPM; process mining.

DOI: 10.1504/EJIE.2018.092009

European Journal of Industrial Engineering, 2018 Vol.12 No.3, pp.364 - 404

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 22 May 2018 *

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