Improving fairness in staff assignment: An approach for lexicographic goal programming

Rihm, Tom; Baumann, Paul

doi:10.1109/ieem.2015.7385847

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Furthermore, according to the software provider, most of their clients consider a fair distribution of refusals among employees to be a desirable objective. In Rihm and Baumann (2015a), we present model extensions that allow to improve an existing schedule in terms of fairness without deteriorating its quality with regard to refused requests. Balancing both fairness and number of refusals is still to be addressed.…”

Section: Discussionmentioning

confidence: 99%

“…A distinctive feature of our approach is that all deviational variables are binary, which provides great flexibility for extensions; e.g., a balanced distribution of refused subrequests among employees could be easily incorporated. In Rihm and Baumann (2015a), we introduced an approach for improving an existing schedule in terms of fairness that takes advantage of this feature.…”

Section: Phase 2: Lexicographic Goal Programmentioning

confidence: 99%

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Staff assignment with lexicographically ordered acceptance levels

Rihm

Baumann

2017

J Sched

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Staff assignment is a compelling exercise that affects most companies and organizations in the service industries. Here, we introduce a new real-world staff assignment problem that was reported to us by a Swiss provider of commercial employee scheduling software. The problem consists of assigning employees to work shifts subject to a large variety of critical and noncritical requests, including employees' personal preferences. Each request has a target value, and deviations from the target value are associated with integer acceptance levels. These acceptance levels reflect the relative severity of possible deviations, e.g., for the request of an employee to have at least two weekends off, having one weekend off is preferable to having no weekend off and thus receives a higher acceptance level. The objective is to minimize the total number of deviations in lexicographical order of the acceptance levels. Staff assignment approaches from the literature are not applicable to this problem. We provide a binary linear programming formulation and propose a matheuristic for large-scale instances. The matheuristic employs effective strategies to determine the subproblems and focuses on finding good feasible solutions to the subproblems rather than proving their optimality. Our computational analysis based on real-world data shows that the matheuristic scales well and outperforms commercial employee scheduling software.

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Section: Discussionmentioning

confidence: 99%

Section: Phase 2: Lexicographic Goal Programmentioning

confidence: 99%

Staff assignment with lexicographically ordered acceptance levels

Rihm

Baumann

2017

J Sched

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“…The authors of [7] take a different approach to the issue of assignment of staff in that in their paper, they tackle the issue of resource allocation in staff to work shifts. They make use of lexicographic goal programming (LGP) taking into account the approach of fairness.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of an optimal algorithm to assign applicants to suitable teaching positions

Chishimba

Kunda

2018

zictjournal

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Resource allocation has always been an area of interest in the area of computing. Areas such as machine learning provide many solutions to the problem of resource allocation. The issue addressed in this study is the issue of optimal allocation of applicants (teachers) to positions in schools where their area of specialization will be better applied. We develop an algorithm that is able to allocate applicants to schools based on the applicant qualifications and the school's needs. We use the principles of resource allocation and machine learning in order to create an application to allocate applicants to schools where their qualifications are most suited. Methods used include classification techniques in machine learning, regression and similarity comparison. For the identification of subjects an applicant in proficient in, various machine learning algorithms are tested to determine which machine learning algorithm was best. The actual process of identifying which applicant qualifies for a school position is also tested against sequential assignment if applicants to schools. The results of this was the algorithm based assignment of applicants to schools which produced more accurate assignment of applicants to schools than the sequential assignment of applicants. The aim of this algorithm is to provide a solution to that automatically identifies the needs (subjects) of a school, determine which needs have a higher priority, identify the qualifications of the applicants and assign the applicants to the school according to the school's needs and the applicant's qualifications.

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Improving fairness in staff assignment: An approach for lexicographic goal programming

Cited by 2 publications

References 7 publications

Staff assignment with lexicographically ordered acceptance levels

Staff assignment with lexicographically ordered acceptance levels

Design and Development of an optimal algorithm to assign applicants to suitable teaching positions

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