An improved MIP-based approach for a multi-skill workforce scheduling problem

Authors:
Murat Fırat;C. A. Hurkens
Affiliations:
Department of Mathematics and Computer Science, TU Eindhoven, Eindhoven, The Netherlands 5600 MB;Department of Mathematics and Computer Science, TU Eindhoven, Eindhoven, The Netherlands 5600 MB
Venue:
Journal of Scheduling
Year:
2012

Citing 6
Cited 0

Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Scheduling projects with multi-skilled personnel by a hybrid MILP/CP benders decomposition algorithm

Journal of Scheduling
High-Performance Local Search for Task Scheduling with Human Resource Allocation

SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Scheduling technicians and tasks in a telecommunications company

Journal of Scheduling
Lower bounds for the multi-skill project scheduling problem with hierarchical levels of skills

PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
A decision aid for the selection and scheduling of software maintenance projects

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with scheduling complex tasks with an inhomogeneous set of resources. The problem is to assign technicians to tasks with multi-level skill requirements. Here, the requirements are merely the presence of a set of technicians that possess the necessary capabilities. An additional complication is that a set of combined technicians stays together for the duration of a work day. This typically applies to scheduling of maintenance and installation operations. We build schedules by repeated application of a flexible matching model that selects tasks to be processed and forms groups of technicians assigned to combinations of tasks. The underlying mixed integer programming (MIP) model is capable of revising technician-task allocations and performs very well, especially in the case of rare skills.