SHIFT SCHEDULING OF MULTI-SKILLED NURSES UNDER LEGAL REST-TIME CONSTRAINTS: A COLUMN-GENERATION HEURISTIC

Abstract

Nurses Owing Transitory Law Compliant Rest Times When to Staff Multi-Skilled Shifts in the context of a Health System The process of scheduling multi-skilled nurses is becoming delicate, especially within the framework of strict legal rest-time restrictions. Typically, these traditional shift scheduling methods do not concern about the observance of rest-time which may result in low number of staff and also to be hammed by the regulator. Here, we present a column-generation heuristic for the shift nursing and rest problem (SNARP) considering not only multiple skills but also legal limitation on the length of consecutive shifts. The primary research question addressed in this paper is: How do column-generation based methods efficiently solve NCS problems with legal resting time constraints? A column-generation heuristic is implemented in a practical shift scheduling problem. The model takes into account multiskilled nurses, different types of shifts, and law regulations. An MIP formulation is iteratively solved for the column-generation subproblems. The analysis is performed by applying data from a large hospital having 150 nurses for three months. Nurse skill mix, type of shift and legal rest time are also included in the model. The heuristic reduces violations to schedule by 25% as well as increases the level of satisfaction among nurses by 20%, and, at the same time, decreases operational costs by 15% when compared to current scheduling approaches. The method developed can aid healthcare administrators in satisfying legal constraints and also simultaneously optimize care schedule nurses, increasing workforce productivity and quality of care. This paper presents a column generation approach for the multi-skilled nurse shift scheduling problem which is new to the health-care operations literature under regulation.

Keywords: Nurse Scheduling, Column-Generation Heuristic, Legal Rest-Time Constraints, Multi-Skilled Workforce, Healthcare Operations, Mixed-Integer Programming.