A two-phase Pareto front method for solving the bi-objective personnel task rescheduling problem

Borgonjon, Tessa; Maenhout, Broos

doi:10.1016/j.cor.2021.105624

Cited by 5 publications

(1 citation statement)

References 59 publications

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“…Conversely, other works decompose their heuristics based on different objectives (Santiago-Mozos et al, 2005;Al-Yakoob and Sherali, 2015). Lastly, while most works regard multiple objectives, none propose approaches that allow the search of the solution space for several solutions, commonly employed in other scheduling problems (Liu et al, 2014;Ahmadi et al, 2016;Lei et al, 2016;Wang et al, 2017;Borgonjon and Maenhout, 2022). Contrarily, we adapt the augmented −constraint method, introduced in Mavrotas ( 2009) and Mavrotas and Florios (2013).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Model for Thesis Defence Scheduling

Almeida¹,

Santos²,

Figueira³

2022

Preprint

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In this paper, we address the thesis defence scheduling problem, a critical academic scheduling management process, which has been overshadowed in the literature by its counterparts, course timetabling and exam scheduling. Specifically, we address the single defence assignment type of thesis defence scheduling problems, where each committee is assigned to a single defence, scheduled for a specific day, hour and room. We formulate a multi-objective mixed-integer linear programming model, which aims to be a general representation of the problem mentioned above, and that can, therefore, be applied to a broader set of cases than other models present in the literature, which have a focus on the characteristics of their universities. For such a purpose, we introduce a different decision variable, propose constraint formulations that are not regulation and policy specific, and cover and offer new takes on the more common objectives seen in the literature. We also include new objective functions based on our experience with the problem at our university and by applying knowledge from other academic scheduling problems. We also propose a two-stage solution approach. The first stage is employed to find the number of schedulable defences, enabling the optimisation of instances with unschedulable defences. The second stage is an implementation of the augmented −constraint method, which allows for the search of a set of different and non-dominated solutions while skipping redundant iterations. A novel instance generator for thesis scheduling problems is presented. Its main benefit is the generation of the availability of committee members and rooms in availability and unavailability blocks, resembling their real-world counterparts. A set of 96 randomly generated instances of varying sizes is solved and analysed regarding their relative computational performance, the number of schedulable defences and the distribution of the considered types of iterations. The proposed method can find the optimal number of schedulable defences and present non-dominated solutions within the set time limits for every tested instance.

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Section: Introductionmentioning

confidence: 99%

A Multi-Objective Model for Thesis Defence Scheduling

Almeida¹,

Santos²,

Figueira³

2022

Preprint

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Time autonomy in personnel planning: Requirements and solution approaches in the context of intelligent scheduling from a holistic organizational perspective

Latos,

Buckhorst,

Kalantar

et al. 2024

Z. Arb. Wiss.

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Traditional work models often need more flexibility and time autonomy for employees, especially in manufacturing. Quantitative approaches and Artificial Intelligence (AI) applications offer the potential to improve work design. However, current research does not entirely focus on human-centric criteria that enable time autonomy. This paper addresses this gap by developing a set of criteria to evaluate intelligent personnel planning approaches based on their ability to enhance time autonomy for employees. Existing quantitative approaches are not sufficient to fully integrate the developed criteria.Consequently, a novel model approach is proposed in an attempt to bridge the gap between current practices and the newly developed criteria. This two-stage planning approach fosters democratization of time autonomy on the shopfloor, moving beyond traditional top-down scheduling. The paper concludes by outlining the implementation process and discusses future developments with respect to AI for this model approach.Practical Relevance: In order to make working conditions on the shopfloor in high-wage countries more attractive, an alternative organization of shift work is needed. Intelligent planning approaches that combine traditional operations research methods with artificial intelligence approaches can democratize shift organization regarding time autonomy. Planning that takes both employee and employer preferences into account in a balanced way will strengthen the long-term competitiveness of manufacturing companies in high-wage countries and counteract the shortage of skilled labor.

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