Algorithms for handling soft constraints and their applications to staff scheduling problems

Author(s):  
Qi Zhao ◽  
Randy Goebel
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.


1989 ◽  
Vol 01 (02) ◽  
pp. 167-176 ◽  
Author(s):  
Lars Gislén ◽  
Carsten Peterson ◽  
Bo Söderberg

A convenient mapping and an efficient algorithm for solving scheduling problems within the neural network paradigm is presented. It is based on a reduced encoding scheme and a mean field annealing prescription which was recently successfully applied to TSP. Most scheduling problems are characterized by a set of hard and soft constraints. The prime target of this work is the hard constraints. In this domain the algorithm persistently finds legal solutions for quite difficult problems. We also make some exploratory investigations by adding soft constraints with very encouraging results. Our numerical studies cover problem sizes up to O(105) degrees of freedom with no parameter tuning. We stress the importance of adding self-coupling terms to the energy functions which are redundant from the encoding point of view but beneficial when it comes to ignoring local minima and to stabilizing the good solutions in the annealing process.


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.


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