Algorithms for large scale Shift Minimisation Personnel Task Scheduling Problems

Krishnamoorthy, Mohan; Ernst, Andreas T.; Baatar, Davaatseren

doi:10.1016/j.ejor.2011.11.034

Cited by 67 publications

(88 citation statements)

References 16 publications

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“…A study comparing the performance against recently published solution techniques from the literature, and against a commercial solver (Gurobi 5.1.0) demonstrates the efficiency and effectiveness of the new hybrid heuristic. Furthermore, it is shown that the algorithm was able to find, for the first time, optimal solutions for all instances from the dataset of Krishnamoorthy et al [77]. The second contribution is an investigation of SMPTSP properties that affect algorithm performance.…”

Section: Contributionsmentioning

confidence: 99%

“…Krishnamoorthy et al [77] state that when the qualification constraints of the SMPTSP are relaxed, i.e. when all employees are qualified to perform all tasks, the resulting problem can be solved in polynomial time with a forward pass maximal clique algorithm on an interval graph [57].…”

Section: First Fit and Best Fit Heuristicsmentioning

confidence: 99%

“…This problem was introduced by Krishnamoorthy and Ernst [76] as the shift minimisation personnel task scheduling problem. Krishnamoorthy et al [77] propose a Lagrangian relaxation based approach that combines two problem specific heuristics. In doing so, they were able to find 135 feasible solutions and 67 optimal solutions out of 137 benchmark instances.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nurse rostering: models and algorithms for theory, practice and integration with other problems

Smet

2016

4OR-Q J Oper Res

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Section: Contributionsmentioning

confidence: 99%

Section: First Fit and Best Fit Heuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nurse rostering: models and algorithms for theory, practice and integration with other problems

Smet

2016

4OR-Q J Oper Res

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“…As they do not consider time preferences for individual tasks, the objective is to minimize the makespan. Krishnamoorthy et al (2012) present a task scheduling problem with hierarchical skill levels. They assume fixed start times for all tasks as a hard constraint.…”

Section: Task Scheduling With Different Qualificationsmentioning

confidence: 99%

Task scheduling in long-term care facilities: A client-centered approach

Lieder

Moeke

Koole

et al. 2015

Operations Research for Health Care

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In this study, we analyze a task scheduling problem with small time windows and care workers with different levels of qualification in a nursing home. A set of care tasks has to be assigned to a given number of care workers, so that the total earliness and tardiness from the nursing home residents' preferred time is minimized.To optimally solve this scheduling problem, we formulate a mixed integer program (MIP) and develop a dynamic programming (DP) approach. The numerical analysis shows the reliability of this optimization approach as well as of a heuristic DP approach. A sensitivity analysis with real-world demand data shows the impact of hierarchical qualification levels and large-scale scheduling.

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“…Gomez-Gasquet et al developed a modified GA for collaborative scheduling of products-packages service [13]. Krishnamoorthy et al proposed a Lagrangean approach for solving the Personnel Task Scheduling Problem (PTSP) [14]. Besides, competent genetic algorithms [15], ant colony algorithms [16], particle swarm optimization (PSO) [17], simulation [18], simulated annealing (SA) [19] and many other algorithms and methods [20][21] were used in PCD scheduling.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

Yu¹,

Yang²,

Zhao³

et al. 2014

Int. j. simul. model.

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Disturbance is inevitable in product collaborative design (PCDSP), which has always posed a great challenge for enterprises making quick response. Thus, in the paper, a multi-objective rescheduling model and its solution algorithm are presented. Disturbance cases are analysed and dynamic scheduling procedure based on event-driven and lifecycle-driven is developed firstly. Then a multiobjective rescheduling model aiming to minimum the makespan and tardiness penalty is developed. As a solution, multi-objective dynamic adaptive scheduling algorithm (MODASA), based on bi-layer coding strategy, self-adaptive double point crossover and self-adaptive mutation, is proposed following closely. Finally, analytic results from a case of a wind turbine are used to illustrate the model and method proposed in this paper. Simulation results shows that the model and algorithm have full advantages in computing speed and precision. With the analysis, it can provide insight into ways of improving the strategic and operational decision making for enterprises.

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Algorithms for large scale Shift Minimisation Personnel Task Scheduling Problems

Cited by 67 publications

References 16 publications

Nurse rostering: models and algorithms for theory, practice and integration with other problems

Nurse rostering: models and algorithms for theory, practice and integration with other problems

Task scheduling in long-term care facilities: A client-centered approach

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

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