A multistage approach for an employee timetabling problem with a high diversity of shifts as a solution for a strongly varying workforce demand

Bäumelt, Zdeněk; Šůcha, Přemysl; Hanzálek, Zdeněk

doi:10.1016/j.cor.2014.03.019

Cited by 3 publications

(5 citation statements)

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“…In this section, we present the results of experiments obtained using standard (Curtois, 2013) and real-world benchmark instances (Bäumelt et al, 2014). First, we describe the experimental setup.…”

Section: Methodsmentioning

confidence: 99%

“…Our TSA was implemented in C#. The experiments were executed using the standard benchmark instances: "Valouxis," "Millar," "Millar-s," "Ortec," and "GPost" (Curtois, 2013), and the real-world benchmark instances: "bp01," "bp02," "bp03," "bp04," and "bp05" (Bäumelt et al, 2014). These specific standard instances were chosen to cover a wide spectrum of instance types.…”

Section: Methodsmentioning

confidence: 99%

“…• a nurse should have at least 10 days off. block-based (Bäumelt et al, 2014), i.e., a block is a sequence of consecutive shifts in a schedule that meets some requirements:…”

Section: • Soft Constraintsmentioning

confidence: 99%

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Roster evaluation based on classifiers for the nurse rostering problem

2016

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The personnel scheduling problem is a well-known NP-hard combinatorial problem. Due to the complexity of this problem and the size of the realworld instances, it is not possible to use exact methods, and thus heuristics, meta-heuristics, or hyper-heuristics must be employed. The majority of heuristic approaches are based on iterative search, where the quality of intermediate solutions must be calculated. Unfortunately, this is computationally highly expensive because these problems have many constraints and some are very complex. In this study, we propose a machine learning technique as a tool to accelerate the evaluation phase in heuristic approaches. The solution is based on a simple classifier, which is able to determine whether the changed solution (more precisely, the changed part of the solution) is better than the original or not. This decision is made much faster than a standard cost-oriented evaluation process. However, the classification process cannot guarantee 100% correctness. Therefore, our approach, which is illustrated using a tabu search algorithm in this study, includes a filtering mechanism, where the classifier rejects the majority of the potentially bad solutions and the remaining solutions are then evaluated in a standard manner. We also show how the boosting algorithms can improve the quality of the final solution compared with a simple classifier. We verified our proposed approach and premises, based on standard and real-world benchmark instances, to demonstrate the significant speedup obtained with comparable solution quality.Keywords neural network · nurse rostering problem · adaptive boosting · pattern learning.R. Václavík ( ) · P.Šůcha · Z. Hanzálek

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Roster evaluation based on classifiers for the nurse rostering problem

2016

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“…Different user interactions with the client application generate different computational intensive tasks (e.g. the automatic roster design) and these tasks are solved by the anytime heuristic algorithms [4] on the server side.…”

Section: Case Studymentioning

confidence: 99%

“…Function BisectionControl(t, S, sch, maxIters, φ) 2 averageN ormalizedLateness ← ScheduleRecomputation(t, sch, 1);3 if averageN ormalizedLateness ≤ 0 then return;4 averageN ormalizedLateness ← ScheduleRecomputation(t, sch, φ);5 if averageN ormalizedLateness ≥ 0 then return;6 iter ← 0; lb ← φ; ub ← 1; φ ← 1; ← ScheduleRecomputation(t, sch, φ);Algorithm Bisection control algorithm experiments, for the majority of the cases, the sufficient number of iterations is 30.The complexity of Algorithm 2 is O maxIters · m + n ( * ) , where m + n ( * ) is due to schedule recomputation algorithm.…”

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Adaptive online scheduling of tasks with anytime property on heterogeneous resources

Módos

Šůcha

Václavík

et al. 2016

Computers & Operations Research

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An acceptable response time of a server is an important aspect in many client-server applications; this is evident in situations in which the server is overloaded by many computationally intensive requests. In this work, we consider that the requests, or in this case tasks, generated by the clients are instances of optimization problems solved by anytime algorithms, i.e. the quality of the solution increases with the processing time of a task. These tasks are submitted to the server which schedules them to the available computational resources where the tasks are processed. To tackle the overload problem, we propose a scheduling algorithm which combines traditional scheduling approaches with a quality control heuristic which adjusts the requested quality of the solutions and thus changes the processing time of the tasks. Two efficient quality control heuristics are introduced: the first heuristic sets a global quality for all tasks, whereas the second heuristic sets the quality for each task independently. Moreover, in practice, the relationship between the processing time and the quality is not known a priori. Because it is crucial for scheduling algorithms to know at least the estimation of these relationships, we propose a general procedure for estimating these relationships using information obtained from the already executed tasks. Finally, the performance of the proposed scheduling algorithm is demonstrated on a real-world problem from the domain of personnel rostering with very good results.

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Intuitionistic fuzzy two-sided matching model and its application to personnel-position matching problems

Zhang

2019

Journal of the Operational Research Society

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A multistage approach for an employee timetabling problem with a high diversity of shifts as a solution for a strongly varying workforce demand

Cited by 3 publications

References 21 publications

Roster evaluation based on classifiers for the nurse rostering problem

Roster evaluation based on classifiers for the nurse rostering problem

Adaptive online scheduling of tasks with anytime property on heterogeneous resources

Intuitionistic fuzzy two-sided matching model and its application to personnel-position matching problems

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