Optimal staffing under an annualized hours regime using Cross-Entropy optimization

This paper discusses staffing under annualized hours. Staffing is the selection of the most cost-efficient workforce to cover workforce demand. Annualized hours measure working time per year instead of per week, relaxing the restriction for employees to work the same number of hours every week. To solve the underlying combinatorial optimization problem this paper develops a Cross-Entropy optimization implementation that includes a penalty function and a repair function to guarantee feasible solutions. Our experimental results show Cross-Entropy optimization is efficient across a broad range of instances, where real-life sized instances are solved in seconds, which significantly outperforms an MILP formulation solved with CPLEX. In addition, the solution quality of Cross-Entropy closely approaches the optimal solutions obtained by CPLEX. Our Cross-Entropy implementation offers an outstanding method for real-time decision making, for example in response to unexpected staff illnesses, and scenario analysis

Arbeitsbericht Nr. 2020-02, März 2020

Determining size and structure of a company’s workforce is one of the most challenging tasks in human resource planning, especially when considering a long-term planning horizon with varying demand. In this paper an approach for integrated staffing and scheduling in a strategic long-term context is presented by applying evolutionary bilevel optimization. For demonstration, the example of determining the number of employees in different categories over the period of one year in a midsized call center of a utility is used. In doing so, two contrary objectives were optimized simultaneously: reduce the overall workforce costs and retain a high scheduling quality. The results show that the proposed approach could be used to support corporate decision making related to strategic workforce planning, not only for call centers but for any other kind of workforce planning involving personnel scheduling

An Analysis of Robust Workforce Scheduling Models for a Nurse Rostering Problem

Disruptions impacting workforce schedules can be costly. A 1999 study of the United Kingdom\u27s National Health Service estimated that as much as 4% of the total resources spent on staffing were lost to schedule disruptions like absenteeism. Although disruptions can not be eliminated, workforce schedules can be improved to be more responsive to disruptions. One key area of study that has expanded over the past few years is the application of traditional scheduling techniques to re-rostering problems. These efforts have provided methods for responding to schedule disruptions, but typically require deviations to the disrupted schedule. This thesis examines five workforce scheduling models designed for a nurse rostering problem. Each model is designed to produce a robust workforce schedule that remains valid in the midst of disruptions and requires no schedule deviations. Each model is evaluated based on the number of disruptions it can receive before becoming invalid. Nonparametric statistical analysis is used to analyze the disruption data for each model and determine which workforce scheduling model produces the most robust schedule. The results of this research indicate that additional manpower must be applied to the correct skill sets in order to produce robust workforce schedules. Furthermore, workforce managers can consider leaving a portion of the workforce unscheduled (or in reserve) to accommodate schedule disruptions

Innovation, Artificial Intelligence in Contingent Work-Force Management

In recent years, the global use of contingent workers is rapidly increasing despite the increasing quantity of artificial intelligence applications in business. The question is "how these companies leverage the use of artificial intelligence to enhance contingent workforce's management?". The ideal goal of this paper is to develop a purely conceptual application of innovation, artificial intelligence (AI) adjacent to contingent workforce management(CWM). The researcher used qualitative information gathered from various authors and observations to reinforce the usage of AI. One of the critical tools to integrate with contingent workforce management for reduction of time spent on human resource administrative tasks is AI. There must be a transformation of thinking, accepting positive organizational change, utilization of technology and openness to new technology to foster  AI. Along with that, integrating contingent workforce management with AI reduces risks and costs, increases efficiency and quality of work. Innovation and Artificial intelligence have been used in five pillars performance of contingent workforce management to mitigate the challenges associated with it.In recent years, the global use of contingent workers is rapidly increasing despite the increasing quantity of artificial intelligence applications in business. The question is "how these companies leverage the use of artificial intelligence to enhance contingent workforce's management?". The ideal goal of this paper is to develop a purely conceptual application of innovation, artificial intelligence (AI) adjacent to contingent workforce management(CWM). The researcher used qualitative information gathered from various authors and observations to reinforce the usage of AI. One of the critical tools to integrate with contingent workforce management for reduction of time spent on human resource administrative tasks is AI. There must be a transformation of thinking, accepting positive organizational change, utilization of technology and openness to new technology to foster  AI. Along with that, integrating contingent workforce management with AI reduces risks and costs, increases efficiency and quality of work. Innovation and Artificial intelligence have been used in five pillars performance of contingent workforce management to mitigate the challenges associated with it

"You have to get wet to learn how to swim" applied to bridging the gap between research into personnel scheduling and its implementation in practice

Personnel scheduling problems have attracted research interests for several decades. They have been considerably changed over time, accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems including optimisation, meta-heuristics, artificial intelligence, decision-support, and also hybrids of these approaches. However, this still does not imply that this research has a large impact on practice and that state-of-the art models and algorithms are widely in use in organisations. One can find a reasonably large number of software packages that aim to assist in personnel scheduling. A classification of this software based on its purpose will be proposed, accompanied with a discussion about the level of support that this software offers to schedulers. A general conclusion is that the available software, with some exceptions, does not benefit from the wealth of developed models and methods. The remaining of the paper will provide insights into some characteristics of real-world scheduling problems that, in the author’s opinion, have not been given a due attention in the personnel scheduling research community yet and which could contribute to the enhancement of the implementation of research results in practice. Concluding remarks are that in order to bridge the gap that still exists between research into personnel scheduling and practice, we need to engage more with schedulers in practice and also with software developers; one may say we need to get wet if we want to learn how to swim

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.Siirretty Doriast