Optimizing railway crew schedules with fairness preferences

Jütte, Silke; Müller, Daniel; Thonemann, Ulrich W.

doi:10.1007/s10951-016-0499-4

Cited by 27 publications

(12 citation statements)

References 30 publications

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“…A CSP integration with the vehicle scheduling problem was developed in [69]. In [70], an application of the CSP with fairness preferences was explored. The crew pairing and fleet assignment problems were studied in [71].…”

Section: Real-world Applicationmentioning

confidence: 99%

A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems

García

Moraga

Valenzuela

et al. 2019

Computational Intelligence and Neuroscience

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The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times.

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Section: Real-world Applicationmentioning

confidence: 99%

A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems

García

Moraga

Valenzuela

et al. 2019

Computational Intelligence and Neuroscience

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“…As an example, we found CrSP applied to railway. In [19], CrSP with attendance rates was solved; a version of CrSP with fairness preferences was solved in [20]. Crew scheduling problem applications were also found for airlines and bus transportation.…”

Section: Crew Scheduling Problemsmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

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“…With scheduling adherence in place, it becomes likely that some workers are stuck with shifts that are undesired, hence a need for fairness considerations in workforce scheduling [2,12]. Especially in developing countries, transportation structure, work culture, and diverse technical skills make fair schedules a must, for employee engagement.…”

mentioning

confidence: 99%

“…That is, they define both an upper bound for the number of undesired shifts assigned to each worker and an objective term that minimizes the undesirability of assigned shifts. Jütte et al [12] aim to balance distribution of unpopular work for railway crews. Unfair schedules reduce employee engagement, low engagement causes employee turnover, and turnover brings extra operational cost on companies [29].…”

mentioning

confidence: 99%

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Cost of fairness in agent scheduling for contact centers

Şimşek¹,

Kundakcioglu²

2022

JIMO

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We study a workforce scheduling problem faced in contact centers with considerations on a fair distribution of shifts in compliance with agent preferences. We develop a mathematical model that aims to minimize operating costs associated with labor, transportation of agents, and lost customers. Aside from typical work hour-related constraints, we also try to conform with agents' preferences for shifts, as a measure of fairness. We plot the trade-off between agent satisfaction and total operating costs for Vestel, one of Turkey's largest consumer electronics companies. We present insights on the increased cost to have content and a fair environment on several agent availability scenarios.

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Optimizing railway crew schedules with fairness preferences

Cited by 27 publications

References 30 publications

A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems

A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

Cost of fairness in agent scheduling for contact centers

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