Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/15822
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: The falling tide algorithm: A new multi-objective approach for complex workforce scheduling
Authors: Li, Jingpeng
Burke, Edmund
Curtois, Tim
Petrovic, Sanja
Qu, Rong
Contact Email: e.k.burke@stir.ac.uk
Keywords: Scheduling
Goal programming
Heuristics
Multi-criteria
Issue Date: Jun-2012
Publisher: Elsevier
Citation: Li J, Burke E, Curtois T, Petrovic S & Qu R (2012) The falling tide algorithm: A new multi-objective approach for complex workforce scheduling, Omega, 40 (3), pp. 283-293.
Abstract: We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making.
Type: Journal Article
URI: http://hdl.handle.net/1893/15822
DOI Link: http://dx.doi.org/10.1016/j.omega.2011.05.004
Rights: This article is open-access. Open access publishing allows free access to and distribution of published articles where the author retains copyright of their work by employing a Creative Commons attribution licence. Proper attribution of authorship and correct citation details should be given. Publisher’s policy available from http://www.elsevier.com/journals/omega/0305-0483/guide-for-authors#10100
Affiliation: Computing Science and Mathematics
Deputy Principal's Office
University of Nottingham
University of Nottingham
University of Nottingham

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