Heuristics and simulated annealing algorithm for the surgical scheduling problem

Gulsah Hancerliogullari*, Emrah Koksalmis, Kadir Oymen Hancerliogullari

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)


Planning and scheduling play a very important role in health care. Effective scheduling optimizes the utilization of scarce resources such as operating rooms (ORs), devices in hospitals, and surgeons. Therefore, operations research/operations management techniques have been frequently used in health care systems management. In this chapter, we examine the surgical scheduling problem over multiple operating rooms. In order to find an optimal solution to surgical scheduling problem, mixed-integer programming (MIP) formulation of the surgical scheduling problem is presented. The model includes constraints for several operational rules and requirements found in most hospitals, and specifically minimizes the total weighted start time as a performance measure (or objective function). Since the problem is known to be an NP-hard in most of its forms, heuristic algorithms (i.e., greedy heuristics and a metaheuristic) are also introduced to find near-optimal solutions efficiently.

Original languageEnglish
Title of host publicationInternational Series in Operations Research and Management Science
PublisherSpringer New York LLC
Number of pages17
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameInternational Series in Operations Research and Management Science
ISSN (Print)0884-8289

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.


  • Greedy heuristic
  • Health care services
  • Mathematical programming
  • Metaheuristic
  • Simulated annealing
  • Surgical scheduling


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