Multi-objective allocation of multi-function workers with lower bounded capacity

This paper deals with the assignment of a type of task to each member of a multi-function staff (each worker is able to perform a given subset of types of tasks, possibly with a priority index associated to each element of the subset). The resulting number of workers for each type of task must be not less than a given lower bound and as close as possible to another given value. The objectives are to minimise a function of the slacks and the surpluses of capacity, to distribute the slacks and the surpluses homogeneously among the types of task and to maximise the sum of priority indexes of the assignments. The problem is modelled as a nonlinear mixed integer program and is transformed and solved as a minimum cost flow problem.Peer Reviewe

A two-stage stochastic program for scheduling and allocating cross-trained workers

A two-stage stochastic program is developed for scheduling and allocating cross-trained workers in a multi-department service environment with random demands. The first stage corresponds to scheduling days-off over a time horizon such as a week or month. The second stage is the recourse action that deals with allocating available workers at the beginning of a day to accommodate realized demands. After the general two-stage model is formulated, a special case is introduced for computational testing. The testing helps quantify the value of cross-training as a function of problem characteristics. Results show that cross-training can be more valuable than perfect information, especially when demand uncertainty is high

The Influence Of Knowledge Management On Market-Related Performance Through Business Process Effectiveness: An Empirical Investigation Of Hospitals And Financial Firms

Knowledge-based resources are critical in service sectors for facing the challenges of dynamic markets and helping organizations manage changes in consumer preference. Knowledge application is needed to improve the business process in order to attain superior market-related performance because there is the unperfected imitation coming from causal ambiguity. However, there is a lack of empirical study in examining the effect of KM and the effect of the business process within the scope of service sectors. This study examines how KM infrastructure supports and KM capabilities influence market-related performance through business processes effectiveness. Data collections of two studies are from 166 hospitals and 106 financial firms. The findings indicate a positive relationship between KM infrastructure and KM capability, and that they have a positive influence on market-related performance through business process effectiveness. For improving this process, the effect of KM infrastructure is greater than the effect of KM capabilities in hospitals. But the effect of KM capabilities is greater than the effect of KM infrastructure in financial firms. The implications of these findings for research and practices in hospitals and financial firms are also discussed

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

Capacity management of nursing staff as a vehicle for organizational improvement

<p>Abstract</p> <p>Background</p> <p>Capacity management systems create insight into required resources like staff and equipment. For inpatient hospital care, capacity management requires information on beds and nursing staff capacity, on a daily as well as annual basis. This paper presents a comprehensive capacity model that gives insight into required nursing staff capacity and opportunities to improve capacity utilization on a ward level.</p> <p>Methods</p> <p>A capacity model was developed to calculate required nursing staff capacity. The model used historical bed utilization, nurse-patient ratios, and parameters concerning contract hours to calculate beds and nursing staff needed per shift and the number of nurses needed on an annual basis in a ward. The model was applied to three different capacity management problems on three separate groups of hospital wards. The problems entailed operational, tactical, and strategic management issues: optimizing working processes on pediatric wards, predicting the consequences of reducing length of stay on nursing staff required on a cardiology ward, and calculating the nursing staff consequences of merging two internal medicine wards.</p> <p>Results</p> <p>It was possible to build a model based on easily available data that calculate the nursing staff capacity needed daily and annually and that accommodate organizational improvements. Organizational improvement processes were initiated in three different groups of wards. For two pediatric wards, the most important improvements were found to be improving working processes so that the agreed nurse-patient ratios could be attained. In the second case, for a cardiology ward, what-if analyses with the model showed that workload could be substantially lowered by reducing length of stay. The third case demonstrated the possible savings in capacity that could be achieved by merging two small internal medicine wards.</p> <p>Conclusion</p> <p>A comprehensive capacity model was developed and successfully applied to support capacity decisions on operational, tactical, and strategic levels. It appeared to be a useful tool for supporting discussions between wards and hospital management by giving objective and quantitative insight into staff and bed requirements. Moreover, the model was applied to initiate organizational improvements, which resulted in more efficient capacity utilization.</p

Heuristic scheduling for clinical physicians.

Personnel scheduling is a problem faced by many organizations in the healthcare industry, particularly in rapidly developing outpatient centers. The task of creating a schedule that adequately covers patient demand while satisfying the preferences of employees, observing work regulations, and ensuring a fair distribution of work is highly complex. Even though this highly complex task directly affects measures such as patient waiting time and employee satisfaction, many organizations still resort to the traditional and cumbersome manual solution methods. A large segment of prior research on personnel scheduling in healthcare focuses on nurse rostering and the development of automated tools to aid in scheduling. The drawbacks to these methods include the lack of generality and the need for specialized software packages and training. The aim of this study is the development of an effective, low cost, and uncomplicated heuristic tool to aid schedulers in outpatient centers. Solution methodologies used by previous researchers in problems such as nurse rostering and aircrew rostering are adapted to the particular problem of physician scheduling in mixed specialty outpatient clinics. The developed heuristic tool obtains an initial feasible solution using a greedy algorithm and then uses the simulated annealing metaheuristic to improve the solution, which is a measure of physician satisfaction. The heuristic tool developed in this study was tested using eight randomly generated data sets to model 45 unique cases. The heuristic found the optimal solution in 19 of the 45 tested cases. The average difference from the optimal physician satisfaction rating in the other 26 cases was 0.35%

A multi-method scheduling framework for medical staff

Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin. The optimized base staffing patterns and shift schedules actively contributed to solving ED overcrowding problem and reduced the average waiting time for patients by 43% compared to the current waiting time of discharged patients. The reduction was achieved by optimizing the staffing level and then determining the shift schedule that minimized the understaffing and overstaffing of the personnel need to meet patient demand

A Model for Efficiency-Based Resource Integration in Services

Service processes, such as consulting, require coordinated efforts from the service recipient (client) and the service provider in order to deliver the desired output – a process known as resource integration. Client involvement directly affects the efficiency of service processes, thereby affecting capacity decisions. We present a mathematical model of the resource-integration decision for a service process through which the client and the service provider co-produce resource outputs. This workforce planning model is unique because we include the extent of client involvement as a policy variable and introduce to the resource-planning model efficiency and quality performance measures, which are functions of client involvement. The optimization of resource planning for services produces interesting policy prescriptions due to the presence of a client-modulated efficiency function in the capacity constraint and subjective client value placed on participation in the service process. The primary results of this research are optimal decision rules that provide insights into the optimal levels of client involvement and provider commitment in resource integration