staff scheduling
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Author(s):  
Kambombo Mtonga ◽  
Antoine Gatera ◽  
Kayalvizhi Jayavel ◽  
Mwawi Nyirenda ◽  
Santhi Kumaran

Accurate staff scheduling is crucial in overcoming the problem of mismatch between staffing ratios and demand for health services which can impede smooth patient flow. Patient flow is an important process towards provision of improved quality of service and also improved utilization of hospital resources. However, extensive waiting times remains a key source of dissatisfaction with the quality of health care service among patients. With rarely scheduled hospital visits, the in-balance between hospital staffing and health service demand remains a constant challenge in Sub-Saharan Africa. Accurate workload predictions help anticipate financial needs and also aids in strategic planning for the health facility. Using a local health facility for a case study, we investigate problems faced by hospital management in staff scheduling. We apply queuing theory techniques to assess and evaluate the relationship between staffing ratios and waiting times at the facility. Specifically, using patient flow data for a rural clinic in Malawi, we model queue parameters and also approximate recommended staffing ratios to achieve steady state leading to reduced waiting times and consequently, improved service delivery at the clinic.


Author(s):  
Guoyou Yue ◽  
Yuexia Huang ◽  
Linluan Huang

With the development of e-commerce, China is actively promoting the construction of logistics informatization and intelligence, and simultaneously promoting the standardized and efficient development of warehousing industry, one of the important links of logistics. It is clearly pointed out in the "Medium and Long Term Plan for the Development of the Logistics Industry" issued by the State Council that "The warehousing industry is not only the main body of the traditional logistics industry, but also an important part of the modern logistics industry. In the process of material flow, the level of storage cost has a greater impact on the development of the logistics industry." This paper chooses Sinotrans Logistics Company as the research object. Sinotrans Logistics Company is the largest integrated logistics provider in China and a national 5A comprehensive logistics enterprise. The main problems in Sinotrans warehousing management include: increased operation difficulty of cross-warehouse inventory, large order volume and delayed processing, unreasonable scheduling of warehouse staff, more idle time for warehouse staff and large difference in human efficiency per hour, etc. The key elements of these problems are the work efficiency of employees. Therefore, warehouse management needs to carry out fine scheduling of warehouse staff to improve the completion rate of orders and reduce the waste of labor costs. Keywords: Warehouse staff scheduling problem, integer programming, linear regression model, people-oriented


2021 ◽  
pp. 1-22
Author(s):  
Ping-Shun Chen ◽  
Chia-Che Tsai ◽  
Jr-Fong Dang ◽  
Wen-Tso Huang

BACKGROUND: This research studies a medical staff scheduling problem, which includes government regulations and hospital regulations (hard constraints) and the medical staff’s preferences (soft constraints). OBJECTIVE: The objective function is to minimize the violations (or dissatisfaction) of medical staff’s preferences. METHODS: This study develops three variants of the three-phase modified bat algorithms (BAs), named BA1, BA2, and BA3, in order to satisfy the hard constraints, minimize the dissatisfaction of the medical staff and balance the workload of the medical staff. To ensure workload balance, this study balances the workload among medical staff without increasing the objective function values. RESULTS: Based on the numerical results, the BA3 outperforms the BA1, BA2, and particle swarm optimization (PSO). The robustness of the BA1, BA2, and BA3 is verified. Finally, conclusions are drawn, and directions for future research are highlighted. CONCLUSIONS: The framework of this research can be used as a reference for other hospitals seeking to determine their future medical staff schedule.


Author(s):  
Francesca Guerriero ◽  
Rosita Guido

AbstractIn this paper, we propose optimization models to address flexible staff scheduling problems and some main issues arising from efficient workforce management during the Covid-19 pandemic. The adoption of precautionary measures to prevent the pandemic from spreading has raised the need to rethink quickly and effectively the way in which the workforce is scheduled, to ensure that all the activities are conducted in a safe and responsible manner. The emphasis is on novel optimization models that take into account demand requirements, employees’ personal and family responsibilities, and anti-Covid-19 measures at the same time. It is precisely considering the anti-Covid-19 measures that the models allow to define the working mode to be assigned to the employees: working remotely or on-site. The last optimization model, which can be viewed as the most general and the most flexible formulation, has been developed to capture the specificity of a real case study of an Italian University. In order to improve employees’ satisfaction and ensure the best work/life balance possible, an alternative partition of a workday into shifts to the usual two shifts, morning and afternoon, is proposed. The model has been tested on real data provided by the Department of Mechanical, Energy and Management Engineering, University of Calabria, Italy. The computational experiments show good performance and underline the potentiality of the model to handle worker safety requirements and practicalities and to ensure work activities continuity. In addition, the non-cyclic workforce policy, based on the proposed workday organization, is preferred by employees, since it allows them to better meet their needs.


2021 ◽  
Author(s):  
Maryam Khashayardoust

Staff scheduling has received increasing attention over the past few years because of its widespread use, economic significance and difficulty of solution. For most organizations, the ability to have the right staff on duty at the right time is a critically important factor when attempting to satisfy their customers' requirements. The purpose of this study is to develop a genetic algorithm (GA) for the retail staff scheduling problem, and investigate its effectiveness. The proposed GA is compared with the conventional, linear integer programming approach. The GA is tested on a set of six real-world problems. Three are tested using a range of population size and mutation rate parameters. Then all six are solved with the best of those parameters. The results are compared to those obtained with the branch-and-bound algorithm. It is shown that GA can produce near-optimal solutions for all of the problems, and for half of them, it is more successful than the branch-and-bound method.


2021 ◽  
Author(s):  
Maryam Khashayardoust

Staff scheduling has received increasing attention over the past few years because of its widespread use, economic significance and difficulty of solution. For most organizations, the ability to have the right staff on duty at the right time is a critically important factor when attempting to satisfy their customers' requirements. The purpose of this study is to develop a genetic algorithm (GA) for the retail staff scheduling problem, and investigate its effectiveness. The proposed GA is compared with the conventional, linear integer programming approach. The GA is tested on a set of six real-world problems. Three are tested using a range of population size and mutation rate parameters. Then all six are solved with the best of those parameters. The results are compared to those obtained with the branch-and-bound algorithm. It is shown that GA can produce near-optimal solutions for all of the problems, and for half of them, it is more successful than the branch-and-bound method.


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