Fairness aspects in personnel scheduling

In industries like health care, public transport or call centers a shift-based system ensures permanent availability of employees for covering needed services. The resource allocation problem – assigning employees to shifts – is known as personnel scheduling in literature and often aims at minimizing staffing costs. Working in shifts, though, impacts employees’ private lives which adds to the problem of increasing staff shortage in recent years. Therefore, more and more effort is spent on incorporating fairness into scheduling approaches in order to increase employees’ satisfaction. This paper presents a literature review of approaches for personnel scheduling considering fairness aspects. Since fairness is not a quantitative objective, but can be evaluated from different point of views, a large number of fairness measurements exists in the literature. Furthermore, perspective (group vs individual fairness) or time horizon (short-term vs long-term fairness) are often considered very differently. To conclude, we show that a uniform definition and approach for considering fairness in personnel scheduling is challenging and point out gaps for future research

A decision support system for surgery sequencing at UZ Leuven's day-care department.

In this paper, we test the applicability of a decision support system (DSS) that is developed to optimize the sequence of surgeries in the day-care center of the UZ Leuven Campus Gasthuisberg (Belgium). We introduce a multi-objective function in which children and prioritized patients are scheduled as early as possible on the day of surgery, recovery overtime is minimized and recovery workload is leveled throughout the day. This combinatorial optimization problem is solved by applying a pre-processed mixed integer linear programming model. We report on a 10-day case study to illustrate the performance of the DSS. In particular, we compare the schedules provided by the hospital with those that are suggested by the DSS. The results indicate that the DSS leads to both an increased probability of obtaining feasible schedules and an improved quality of the schedules in terms of the objective function value. We further highlight some of the major advantages of the application, such as its visualization and algorithmic performance, but also report on the difficulties that were encountered during the study and the shortcomings that currently delay its implementation in practice, as this information may contribute to the success rate of future software applications in hospitals.Decision support system; Optimization; Visualization; Health care application;

Multi-Agent Based Information Systems For Patient Coordination in Hospitals

The health sector is a central domain in every economy. It is challenged by progressing costs and funding issues. Hospitals play a major role for the examination and treatment of patients. The sequence how patients are assigned to hospital units determines the quality of treatment, the resource utilization, as well as the patients’ overall treatment time. Thus, efficient scheduling of patients in hospitals is crucial. Current approaches disregard the decentral organization in hospitals and neglect the varying pathway of patients since they often focus on one single unit solely. We propose an agent-based coordination mechanism that overcomes these limitations. Patients and hospital resources are modeled as autonomous software agents which follow their own objectives. This reflects the decentralized structure in hospitals. Agents are coordinated by a distributed mechanism where software agents improve their situation through negotiations which moves towards an overall pareto-optimum. We show promising evaluations based on experiments

An Optimization Model for Integrated Capacity Management and Bed Allocation Planning of Hospitals

Hospitals are facing with increasing demands of unlimited needs of people health. On the other hand, due to the rising cost of healthcare services, hospitals need to put more effort in order to overcome these two problems. This paper deals with proposing an integrated strategy for solving these problems. We address an integer optimization model which integrate capacity staff management problem and bed allocation planning problem. We solve the model using a direct approach, based on the notion of superbasic variables