Parameter Adjustment Approach Based on Distribution of Schedules in the Past for Staff Scheduling Problems

Author(s):  
Makoto Ohara ◽  
Hisashi Tamaki
2014 ◽  
Vol 998-999 ◽  
pp. 1532-1535
Author(s):  
Yu Ming Zhao

The optimal scheduling of crude-oil operation in refineries has been studied by various groups during the past decade leading to different mixed integer linear programming or mixed nonlinear programming formulations. This paper presents a new formulation with oil residency time constraint based on single-operation sequencing (SOS). It is different from previous formulations as it considers oil residency time constraint and pipeline transfer and does not require to postulate the number of priority-slots in which operations take place. This model is also based on the representation of a crude-oil scheduling by a single sequence of transfer operations. A simple MILP procedure has been used to solve this model leading to an satisfactory optimal result.


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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